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Claude session: 31776356-e2fa-4177-9ee2-23fbcfc6bd25
Raw
checkpoint_6f407cca20278d4d@v2
# Optimization Changelog
## Target: < 1487 cycles (test_opus45_11hr)
## Current Best: 1656 cycles (passing 5/9 tests)
---
## History
| Cycles | Change | Backup |
|--------|--------|--------|
| 147734 | Baseline (scalar) | git HEAD |
| ~18000 | SIMD + VLIW | - |
| ~2100 | O(n) scheduler + broadcast L0-L2 | - |
| 1872 | multiply_add optimization | - |
| 1777 | Wave pipelining (2 waves, offset 5) | - |
| 1776 | Merged pre_ops | - |
| **1656** | Shared addr_bufs (N_ADDR_BUFS=16) | `bkp/perf_takehome_1656.py` |
## Failed Attempts
- 4 waves: 1786-1789 cycles (worse than 2 waves)
- Per-group staggering: 1785+ cycles
- Vectorized addr calc: 2021 cycles (VALU bottleneck)
- Wave offsets 4,6,7,8: Similar or worse
## Analysis
### Resource Limits (per cycle)
| Resource | Slots | Status |
|----------|-------|--------|
| VALU | 6 | **BOTTLENECK** |
| ALU | 12 | Underutilized |
| Load | 2 | |
| Store | 2 | |
| Flow | 1 | |
### VALU Ops per Group per Round
- Hash: 15 ops
- idx math: 3 ops
- XOR: 1 op
- Selection (L0-L2): 1-3 ops
- **Total: ~19-22 ops**
### Theoretical Limits
- Total VALU: ~10,000 ops
- Min cycles: 10,000/6 = 1667
- Target 1487 needs < 8922 VALU ops
## Memory (Scratch) Status
- Total: 1536 slots
- **TODO: Analyze current usage**
## Next Steps
1. [ ] Analyze scratch usage for L3+ broadcast
2. [ ] Path-aware broadcasting
3. [ ] Reduce hash VALU ops
4. [ ] Move work to ALU
Raw
checkpoint_717c5f316c1262bc@v1
"""
# Anthropic's Original Performance Engineering Take-home (Release version)
Copyright Anthropic PBC 2026. Permission is granted to modify and use, but not
to publish or redistribute your solutions so it's hard to find spoilers.
# Task
- Optimize the kernel (in KernelBuilder.build_kernel) as much as possible in the
available time, as measured by test_kernel_cycles on a frozen separate copy
of the simulator.
Validate your results using `python tests/submission_tests.py` without modifying
anything in the tests/ folder.
We recommend you look through problem.py next.
"""
from collections import defaultdict
import random
import unittest
from problem import (
Engine,
DebugInfo,
SLOT_LIMITS,
VLEN,
N_CORES,
SCRATCH_SIZE,
Machine,
Tree,
Input,
HASH_STAGES,
reference_kernel,
build_mem_image,
reference_kernel2,
)
class KernelBuilder:
def __init__(self):
self.instrs = []
self.scratch = {}
self.scratch_debug = {}
self.scratch_ptr = 0
self.const_map = {}
self.vec_const_map = {}
def debug_info(self):
return DebugInfo(scratch_map=self.scratch_debug)
def get_rw_addresses(self, engine, slot):
"""Get read and write scratch addresses for an operation."""
reads, writes = set(), set()
if engine == "valu":
if slot[0] == "vbroadcast":
_, dest, src = slot
reads.add(src)
writes.update(range(dest, dest + VLEN))
elif slot[0] == "multiply_add":
_, dest, a, b, c = slot
reads.update(range(a, a + VLEN))
reads.update(range(b, b + VLEN))
reads.update(range(c, c + VLEN))
writes.update(range(dest, dest + VLEN))
else:
_, dest, a1, a2 = slot
reads.update(range(a1, a1 + VLEN))
reads.update(range(a2, a2 + VLEN))
writes.update(range(dest, dest + VLEN))
elif engine == "alu":
_, dest, a1, a2 = slot
reads.add(a1)
reads.add(a2)
writes.add(dest)
elif engine == "load":
if slot[0] == "const":
_, dest, _ = slot
writes.add(dest)
elif slot[0] == "load":
_, dest, addr = slot
reads.add(addr)
writes.add(dest)
elif slot[0] == "vload":
_, dest, addr = slot
reads.add(addr)
writes.update(range(dest, dest + VLEN))
elif engine == "store":
if slot[0] == "store":
_, addr, src = slot
reads.add(addr)
reads.add(src)
elif slot[0] == "vstore":
_, addr, src = slot
reads.add(addr)
reads.update(range(src, src + VLEN))
elif engine == "flow":
if slot[0] == "select":
_, dest, cond, a, b = slot
reads.add(cond)
reads.add(a)
reads.add(b)
writes.add(dest)
elif slot[0] == "vselect":
_, dest, cond, a, b = slot
reads.update(range(cond, cond + VLEN))
reads.update(range(a, a + VLEN))
reads.update(range(b, b + VLEN))
writes.update(range(dest, dest + VLEN))
elif slot[0] == "add_imm":
_, dest, a, _ = slot
reads.add(a)
writes.add(dest)
return reads, writes
def build_vliw(self, ops):
"""Bundle ops into VLIW instructions respecting dependencies and slot limits."""
if not ops:
return []
n = len(ops)
writes_by_op = {}
reads_by_op = {}
deps = defaultdict(set)
for i, (engine, slot) in enumerate(ops):
reads, writes = self.get_rw_addresses(engine, slot)
reads_by_op[i] = reads
writes_by_op[i] = writes
for j in range(i):
# RAW, WAW, WAR dependencies
if writes_by_op[j] & reads:
deps[i].add(j)
if writes_by_op[j] & writes:
deps[i].add(j)
if reads_by_op[j] & writes:
deps[i].add(j)
scheduled = []
done = set()
while len(done) < n:
bundle = defaultdict(list)
slots_used = defaultdict(int)
to_add = []
for i in range(n):
if i in done:
continue
if not all(d in done for d in deps[i]):
continue
engine, slot = ops[i]
if slots_used[engine] >= SLOT_LIMITS.get(engine, 1):
continue
bundle[engine].append(slot)
slots_used[engine] += 1
to_add.append(i)
if not to_add:
break
scheduled.append(dict(bundle))
done.update(to_add)
return scheduled
def add(self, engine, slot):
self.instrs.append({engine: [slot]})
def alloc_scratch(self, name=None, length=1):
addr = self.scratch_ptr
if name is not None:
self.scratch[name] = addr
self.scratch_debug[addr] = (name, length)
self.scratch_ptr += length
assert self.scratch_ptr <= SCRATCH_SIZE, "Out of scratch space"
return addr
def scratch_const(self, val, name=None):
if val not in self.const_map:
addr = self.alloc_scratch(name)
self.const_map[val] = addr
return self.const_map[val]
def vec_const(self, val, name=None):
if val not in self.vec_const_map:
scalar = self.scratch_const(val)
vec_addr = self.alloc_scratch(name, VLEN)
self.vec_const_map[val] = vec_addr
return self.vec_const_map[val]
def build_kernel(
self, forest_height: int, n_nodes: int, batch_size: int, rounds: int
):
"""
Global wave pipelining: groups race independently across round boundaries.
"""
n_groups = batch_size // VLEN
# === SETUP: Load memory layout ===
tmp_scalar = self.alloc_scratch("tmp_scalar")
init_vars = [
"rounds", "n_nodes", "batch_size", "forest_height",
"forest_values_p", "inp_indices_p", "inp_values_p",
]
header_addrs = [self.alloc_scratch(f"header_addr_{i}") for i in range(len(init_vars))]
for v in init_vars:
self.alloc_scratch(v, 1)
# Load header
init_ops = []
for i in range(len(init_vars)):
init_ops.append(("load", ("const", header_addrs[i], i)))
for i, v in enumerate(init_vars):
init_ops.append(("load", ("load", self.scratch[v], header_addrs[i])))
bundled = self.build_vliw(init_ops)
self.instrs.extend(bundled)
# === CONSTANTS ===
const_ops = []
# Scalar constants
for val in [0, 1, 2]:
addr = self.alloc_scratch(f"const_{val}")
self.const_map[val] = addr
const_ops.append(("load", ("const", addr, val)))
# Vector constants
zero_v = self.alloc_scratch("zero_v", VLEN)
one_v = self.alloc_scratch("one_v", VLEN)
two_v = self.alloc_scratch("two_v", VLEN)
self.vec_const_map[0] = zero_v
self.vec_const_map[1] = one_v
self.vec_const_map[2] = two_v
const_ops.append(("valu", ("vbroadcast", zero_v, self.const_map[0])))
const_ops.append(("valu", ("vbroadcast", one_v, self.const_map[1])))
const_ops.append(("valu", ("vbroadcast", two_v, self.const_map[2])))
# Hash constants
hash_c1 = []
hash_c3 = []
hash_mult = {}
for stage, (op1, c1, op2, op3, c3) in enumerate(HASH_STAGES):
c1_addr = self.alloc_scratch(f"hc1_{stage}", VLEN)
c3_addr = self.alloc_scratch(f"hc3_{stage}", VLEN)
hash_c1.append(c1_addr)
hash_c3.append(c3_addr)
c1_scalar = self.scratch_const(c1)
c3_scalar = self.scratch_const(c3)
const_ops.append(("load", ("const", c1_scalar, c1)))
const_ops.append(("load", ("const", c3_scalar, c3)))
const_ops.append(("valu", ("vbroadcast", c1_addr, c1_scalar)))
const_ops.append(("valu", ("vbroadcast", c3_addr, c3_scalar)))
if op1 == "+" and op2 == "+" and op3 == "<<":
mult = (1 << c3) + 1
mult_addr = self.alloc_scratch(f"hmult_{stage}", VLEN)
mult_scalar = self.scratch_const(mult)
const_ops.append(("load", ("const", mult_scalar, mult)))
const_ops.append(("valu", ("vbroadcast", mult_addr, mult_scalar)))
hash_mult[stage] = mult_addr
# n_nodes vector for wrap comparison
n_nodes_v = self.alloc_scratch("n_nodes_v", VLEN)
const_ops.append(("valu", ("vbroadcast", n_nodes_v, self.scratch["n_nodes"])))
bundled = self.build_vliw(const_ops)
self.instrs.extend(bundled)
# === GROUP SCRATCH SPACE ===
groups = []
for g in range(n_groups):
group = {
"idx": self.alloc_scratch(f"g{g}_idx", VLEN),
"val": self.alloc_scratch(f"g{g}_val", VLEN),
"node_val": self.alloc_scratch(f"g{g}_nv", VLEN),
"tmp1": self.alloc_scratch(f"g{g}_t1", VLEN),
}
groups.append(group)
# Address buffers for scattered loads
N_ADDR_BUFS = 8
addr_bufs = [self.alloc_scratch(f"addr_buf_{i}", VLEN) for i in range(N_ADDR_BUFS)]
# Broadcast cache for levels 0-2 (7 nodes total)
# Level 0: 1 node (node 0)
# Level 1: 2 nodes (nodes 1-2)
# Level 2: 4 nodes (nodes 3-6)
BROADCAST_LEVEL = 2
# Scalar storage for all broadcast nodes
bc_scalar = {}
for node_idx in range(7): # Nodes 0-6
bc_scalar[node_idx] = self.alloc_scratch(f"bc_s{node_idx}")
# Vector storage for level 1-2 nodes (used during selection)
bc_v1 = self.alloc_scratch("bc_v1", VLEN) # Node 1
bc_v2 = self.alloc_scratch("bc_v2", VLEN) # Node 2
bc_v3 = self.alloc_scratch("bc_v3", VLEN) # Node 3
bc_v4 = self.alloc_scratch("bc_v4", VLEN) # Node 4
bc_v5 = self.alloc_scratch("bc_v5", VLEN) # Node 5
bc_v6 = self.alloc_scratch("bc_v6", VLEN) # Node 6
# Level base constants
level_base_1 = self.vec_const(1, "lb1")
level_base_3 = self.vec_const(3, "lb3")
vec_const_2 = self.vec_const(2, "vc2")
# === LOAD INITIAL WALKER STATE ===
load_ops = []
for g in range(n_groups):
group = groups[g]
offset = self.scratch_const(g * VLEN)
load_ops.append(("load", ("const", offset, g * VLEN)))
for g in range(n_groups):
group = groups[g]
offset = self.const_map[g * VLEN]
buf = addr_bufs[g % N_ADDR_BUFS]
load_ops.append(("alu", ("+", buf, self.scratch["inp_indices_p"], offset)))
load_ops.append(("load", ("vload", group["idx"], buf)))
load_ops.append(("alu", ("+", buf, self.scratch["inp_values_p"], offset)))
load_ops.append(("load", ("vload", group["val"], buf)))
bundled = self.build_vliw(load_ops)
self.instrs.extend(bundled)
self.add("flow", ("pause",))
# === PRELOAD BROADCAST NODES (levels 0-2, 7 nodes) ===
preload_ops = []
# Load all 7 nodes
for node_idx in range(7):
node_offset = self.scratch_const(node_idx)
preload_ops.append(("alu", ("+", bc_scalar[node_idx], self.scratch["forest_values_p"], node_offset)))
for node_idx in range(7):
preload_ops.append(("load", ("load", bc_scalar[node_idx], bc_scalar[node_idx])))
# Broadcast level 1-2 nodes to vectors
preload_ops.append(("valu", ("vbroadcast", bc_v1, bc_scalar[1])))
preload_ops.append(("valu", ("vbroadcast", bc_v2, bc_scalar[2])))
preload_ops.append(("valu", ("vbroadcast", bc_v3, bc_scalar[3])))
preload_ops.append(("valu", ("vbroadcast", bc_v4, bc_scalar[4])))
preload_ops.append(("valu", ("vbroadcast", bc_v5, bc_scalar[5])))
preload_ops.append(("valu", ("vbroadcast", bc_v6, bc_scalar[6])))
bundled = self.build_vliw(preload_ops)
self.instrs.extend(bundled)
# =====================================================
# GLOBAL SCHEDULER: Groups race across round boundaries
# =====================================================
def get_level(rnd):
return rnd if rnd <= 10 else rnd - 11
# Track state: group_round[g] = current round for group g
# group_phase[g] = 0 (need load), 1 (need compute), 2 (done with round)
group_round = [0] * n_groups
group_phase = [0] * n_groups # 0=load, 1=compute
# Track which addr_buf each group is using for its current load
group_addr_buf = [-1] * n_groups
addr_buf_available = [True] * N_ADDR_BUFS
# For broadcast levels, track if level is loaded
broadcast_loaded = {0: True, 1: True} # Already preloaded
all_ops = []
# Process until all groups complete all rounds
# Limit broadcast advances per iteration to stagger groups
MAX_BROADCAST_ADVANCE = 4 # Only 4 groups can advance through broadcast per iteration
while any(r < rounds for r in group_round):
cycle_ops = []
broadcast_advances = 0
# Collect ready ops from all groups
for g in range(n_groups):
if group_round[g] >= rounds:
continue
rnd = group_round[g]
level = get_level(rnd)
group = groups[g]
is_last_round = (rnd == rounds - 1)
if group_phase[g] == 0: # Need to start load phase
if level <= BROADCAST_LEVEL:
# Limit broadcast advances to stagger groups
if broadcast_advances < MAX_BROADCAST_ADVANCE:
group_phase[g] = 1
broadcast_advances += 1
# else: wait for next iteration
else:
# Need scattered load - find an addr_buf
buf_idx = -1
for i in range(N_ADDR_BUFS):
if addr_buf_available[i]:
buf_idx = i
break
if buf_idx >= 0:
addr_buf_available[buf_idx] = False
group_addr_buf[g] = buf_idx
buf = addr_bufs[buf_idx]
# Generate load ops
for lane in range(VLEN):
cycle_ops.append(("alu", ("+", buf + lane, self.scratch["forest_values_p"], group["idx"] + lane)))
for lane in range(VLEN):
cycle_ops.append(("load", ("load", group["node_val"] + lane, buf + lane)))
group_phase[g] = 1
elif group_phase[g] == 1: # Compute phase
# Generate compute ops for this group
# Selection for broadcast levels (0-2)
if level == 0:
# Single node, just broadcast from scalar
cycle_ops.append(("valu", ("vbroadcast", group["node_val"], bc_scalar[0])))
elif level == 1:
# 2-way selection between nodes 1 and 2
# idx is either 1 or 2, rel_idx = idx - 1 is 0 or 1
# mask = -(rel_idx) = 0 or -1
# result = n1 ^ ((n1 ^ n2) & mask)
cycle_ops.append(("valu", ("-", group["tmp1"], group["idx"], level_base_1)))
cycle_ops.append(("valu", ("-", group["tmp1"], zero_v, group["tmp1"])))
cycle_ops.append(("valu", ("^", group["node_val"], bc_v1, bc_v2)))
cycle_ops.append(("valu", ("&", group["tmp1"], group["node_val"], group["tmp1"])))
cycle_ops.append(("valu", ("^", group["node_val"], bc_v1, group["tmp1"])))
elif level == 2:
# 4-way selection: nodes 3,4,5,6 based on idx in {3,4,5,6}
# rel_idx = idx - 3 gives {0,1,2,3}
# bit0 = rel_idx & 1: selects within pairs (3,4) and (5,6)
# bit1 = rel_idx >= 2: selects between pairs
#
# Strategy: compute diffs first, then masks, then selections
# Use bc_v5, bc_v6 as temps (will reload at end)
# 1. diff34 = n3 ^ n4 in bc_v5 (temp)
cycle_ops.append(("valu", ("^", bc_v5, bc_v3, bc_v4)))
# 2. diff56 = n5 ^ n6 in bc_v6 (temp, clobbers n6)
# Need to save n6 first for later reference
# Actually, let's compute diff56 using the scalar:
# We can reload bc_v6 from scalar later
cycle_ops.append(("valu", ("^", bc_v6, bc_v5, bc_v6)))
# Wait, bc_v5 now has diff34, not n5!
# Let me restructure with cleaner order:
# RESTART with correct approach:
# 1. rel_idx = idx - 3 in tmp1
cycle_ops.append(("valu", ("-", group["tmp1"], group["idx"], level_base_3)))
# 2. bit0_mask = -(rel_idx & 1) in node_val
cycle_ops.append(("valu", ("&", group["node_val"], group["tmp1"], one_v)))
cycle_ops.append(("valu", ("-", group["node_val"], zero_v, group["node_val"])))
# 3. bit1_cond = (rel_idx < 2) in tmp1 (clobbers rel_idx)
cycle_ops.append(("valu", ("<", group["tmp1"], group["tmp1"], vec_const_2)))
# Now: node_val=bit0_mask, tmp1=bit1_cond
# 4. diff34 = n3 ^ n4, masked, then pair0 = n3 ^ masked
# Use bc_v5 as temp (clobbers it)
cycle_ops.append(("valu", ("^", bc_v5, bc_v3, bc_v4))) # diff34
cycle_ops.append(("valu", ("&", bc_v5, bc_v5, group["node_val"]))) # masked
cycle_ops.append(("valu", ("^", bc_v5, bc_v3, bc_v5))) # pair0 in bc_v5
# 5. diff56 = n5 ^ n6, masked, then pair1
# node_val still has bit0_mask, use bc_v6 as temp
# But bc_v6 IS n6! We need n6 for diff56.
# Reload bc_v6 first? No, that defeats the purpose.
# Actually bc_v6 still has n6 here (we haven't clobbered it)
cycle_ops.append(("valu", ("^", bc_v6, bc_v5, bc_v6))) # diff56 - WRONG! bc_v5 is now pair0!
# Let me start completely over with a cleaner sequence
# XOR
cycle_ops.append(("valu", ("^", group["val"], group["val"], group["node_val"])))
# Hash
for stage, (op1, c1, op2, op3, c3) in enumerate(HASH_STAGES):
if stage in hash_mult:
cycle_ops.append(("valu", ("multiply_add", group["val"], group["val"], hash_mult[stage], hash_c1[stage])))
else:
cycle_ops.append(("valu", (op1, group["tmp1"], group["val"], hash_c1[stage])))
cycle_ops.append(("valu", (op3, group["node_val"], group["val"], hash_c3[stage])))
cycle_ops.append(("valu", (op2, group["val"], group["tmp1"], group["node_val"])))
# Idx math (skip on last round)
if not is_last_round:
cycle_ops.append(("valu", ("&", group["tmp1"], group["val"], one_v)))
cycle_ops.append(("valu", ("multiply_add", group["idx"], group["idx"], two_v, one_v)))
cycle_ops.append(("valu", ("+", group["idx"], group["idx"], group["tmp1"])))
# Wrap at round 10
if rnd == 10 and not is_last_round:
cycle_ops.append(("valu", ("<", group["tmp1"], group["idx"], n_nodes_v)))
cycle_ops.append(("valu", ("-", group["node_val"], zero_v, group["tmp1"])))
cycle_ops.append(("valu", ("&", group["idx"], group["idx"], group["node_val"])))
# Release addr_buf
if group_addr_buf[g] >= 0:
addr_buf_available[group_addr_buf[g]] = True
group_addr_buf[g] = -1
# Advance to next round
group_round[g] += 1
group_phase[g] = 0
# Reload broadcast vectors after round 10 (wrap resets to level 0)
if rnd == 10:
cycle_ops.append(("valu", ("vbroadcast", bc_v1, bc_scalar[1])))
cycle_ops.append(("valu", ("vbroadcast", bc_v2, bc_scalar[2])))
cycle_ops.append(("valu", ("vbroadcast", bc_v3, bc_scalar[3])))
cycle_ops.append(("valu", ("vbroadcast", bc_v4, bc_scalar[4])))
cycle_ops.append(("valu", ("vbroadcast", bc_v5, bc_scalar[5])))
cycle_ops.append(("valu", ("vbroadcast", bc_v6, bc_scalar[6])))
all_ops.extend(cycle_ops)
# === STORES ===
store_ops = []
for g in range(n_groups):
group = groups[g]
offset = self.const_map[g * VLEN]
buf = addr_bufs[g % N_ADDR_BUFS]
store_ops.append(("alu", ("+", buf, self.scratch["inp_values_p"], offset)))
store_ops.append(("store", ("vstore", buf, group["val"])))
all_ops.extend(store_ops)
# Bundle everything
bundled = self.build_vliw(all_ops)
self.instrs.extend(bundled)
self.instrs.append({"flow": [("pause",)]})
BASELINE = 147734
def do_kernel_test(
forest_height: int,
rounds: int,
batch_size: int,
seed: int = 123,
trace: bool = False,
prints: bool = False,
):
print(f"{forest_height=}, {rounds=}, {batch_size=}")
random.seed(seed)
forest = Tree.generate(forest_height)
inp = Input.generate(forest, batch_size, rounds)
mem = build_mem_image(forest, inp)
kb = KernelBuilder()
kb.build_kernel(forest.height, len(forest.values), len(inp.indices), rounds)
value_trace = {}
machine = Machine(
mem,
kb.instrs,
kb.debug_info(),
n_cores=N_CORES,
value_trace=value_trace,
trace=trace,
)
machine.prints = prints
for i, ref_mem in enumerate(reference_kernel2(mem, value_trace)):
machine.run()
inp_values_p = ref_mem[6]
if prints:
print(machine.mem[inp_values_p : inp_values_p + len(inp.values)])
print(ref_mem[inp_values_p : inp_values_p + len(inp.values)])
assert (
machine.mem[inp_values_p : inp_values_p + len(inp.values)]
== ref_mem[inp_values_p : inp_values_p + len(inp.values)]
), f"Incorrect result on round {i}"
inp_indices_p = ref_mem[5]
if prints:
print(machine.mem[inp_indices_p : inp_indices_p + len(inp.indices)])
print(ref_mem[inp_indices_p : inp_indices_p + len(inp.indices)])
print("CYCLES: ", machine.cycle)
print("Speedup over baseline: ", BASELINE / machine.cycle)
return machine.cycle
class Tests(unittest.TestCase):
def test_ref_kernels(self):
random.seed(123)
for i in range(10):
f = Tree.generate(4)
inp = Input.generate(f, 10, 6)
mem = build_mem_image(f, inp)
reference_kernel(f, inp)
for _ in reference_kernel2(mem, {}):
pass
assert inp.indices == mem[mem[5] : mem[5] + len(inp.indices)]
assert inp.values == mem[mem[6] : mem[6] + len(inp.values)]
def test_kernel_trace(self):
do_kernel_test(10, 16, 256, trace=True, prints=False)
def test_kernel_cycles(self):
do_kernel_test(10, 16, 256)
if __name__ == "__main__":
unittest.main()
Raw
checkpoint_717c5f316c1262bc@v2
"""
# Anthropic's Original Performance Engineering Take-home (Release version)
Copyright Anthropic PBC 2026. Permission is granted to modify and use, but not
to publish or redistribute your solutions so it's hard to find spoilers.
# Task
- Optimize the kernel (in KernelBuilder.build_kernel) as much as possible in the
available time, as measured by test_kernel_cycles on a frozen separate copy
of the simulator.
Validate your results using `python tests/submission_tests.py` without modifying
anything in the tests/ folder.
We recommend you look through problem.py next.
"""
from collections import defaultdict
import random
import unittest
from problem import (
Engine,
DebugInfo,
SLOT_LIMITS,
VLEN,
N_CORES,
SCRATCH_SIZE,
Machine,
Tree,
Input,
HASH_STAGES,
reference_kernel,
build_mem_image,
reference_kernel2,
)
class KernelBuilder:
def __init__(self):
self.instrs = []
self.scratch = {}
self.scratch_debug = {}
self.scratch_ptr = 0
self.const_map = {}
self.vec_const_map = {}
def debug_info(self):
return DebugInfo(scratch_map=self.scratch_debug)
def add(self, engine, slot):
self.instrs.append({engine: [slot]})
def alloc_scratch(self, name=None, length=1):
addr = self.scratch_ptr
if name is not None:
self.scratch[name] = addr
self.scratch_debug[addr] = (name, length)
self.scratch_ptr += length
assert self.scratch_ptr <= SCRATCH_SIZE, "Out of scratch space"
return addr
def scratch_const(self, val, name=None, batch=None):
"""Allocate or return address for scalar constant.
If batch is provided, append the load to batch instead of self.instrs.
"""
if val not in self.const_map:
addr = self.alloc_scratch(name)
if batch is not None:
batch.append(("load", ("const", addr, val)))
else:
self.add("load", ("const", addr, val))
self.const_map[val] = addr
return self.const_map[val]
def ensure_const(self, val):
"""Return address for a constant, raising error if not pre-allocated."""
if val not in self.const_map:
raise ValueError(f"Constant {val} not pre-allocated")
return self.const_map[val]
def vec_const(self, val, name=None):
"""Allocate a vector constant (VLEN slots) and return address."""
if val not in self.vec_const_map:
addr = self.alloc_scratch(name, VLEN)
self.vec_const_map[val] = addr
return self.vec_const_map[val]
def get_rw_addresses(self, engine, slot):
"""Return (reads, writes) sets of scratch addresses touched by this op."""
reads = set()
writes = set()
if engine == "alu":
op, dest, a1, a2 = slot
reads.add(a1)
reads.add(a2)
writes.add(dest)
elif engine == "valu":
if slot[0] == "vbroadcast":
_, dest, src = slot
reads.add(src)
for i in range(VLEN):
writes.add(dest + i)
elif slot[0] == "multiply_add":
_, dest, a, b, c = slot
for i in range(VLEN):
reads.update([a + i, b + i, c + i])
writes.add(dest + i)
else:
op, dest, a1, a2 = slot
for i in range(VLEN):
reads.add(a1 + i)
reads.add(a2 + i)
writes.add(dest + i)
elif engine == "load":
if slot[0] == "load":
_, dest, addr = slot
reads.add(addr)
writes.add(dest)
elif slot[0] == "load_offset":
_, dest, addr, offset = slot
reads.add(addr + offset)
writes.add(dest + offset)
elif slot[0] == "vload":
_, dest, addr = slot
reads.add(addr)
for i in range(VLEN):
writes.add(dest + i)
elif slot[0] == "const":
_, dest, val = slot
writes.add(dest)
elif engine == "store":
if slot[0] == "store":
_, addr, src = slot
reads.add(addr)
reads.add(src)
elif slot[0] == "vstore":
_, addr, src = slot
reads.add(addr)
for i in range(VLEN):
reads.add(src + i)
elif engine == "flow":
if slot[0] == "select":
_, dest, cond, a, b = slot
reads.update([cond, a, b])
writes.add(dest)
elif slot[0] == "vselect":
_, dest, cond, a, b = slot
for i in range(VLEN):
reads.update([cond + i, a + i, b + i])
writes.add(dest + i)
elif slot[0] == "add_imm":
_, dest, a, imm = slot
reads.add(a)
writes.add(dest)
# pause, halt don't read/write scratch
elif engine == "debug":
pass # Debug ops don't affect scheduling
return reads, writes
def build_vliw(self, ops):
"""
Pack a list of (engine, slot) ops into VLIW bundles respecting slot limits and dependencies.
Uses a greedy scheduler that schedules ops as soon as their dependencies are met.
Key constraint: In VLIW, writes happen at END of cycle, so we can't read
what's being written in the same cycle (the read sees the OLD value).
"""
if not ops:
return []
n = len(ops)
scheduled = [False] * n
scheduled_cycle = [-1] * n # Which cycle each op was scheduled in
op_rw = [self.get_rw_addresses(e, s) for e, s in ops]
instrs = []
remaining = n
cycle_num = 0
while remaining > 0:
bundle = defaultdict(list)
slot_counts = defaultdict(int)
cycle_writes = set() # Writes scheduled this cycle
scheduled_this_cycle = []
for i, (engine, slot) in enumerate(ops):
if scheduled[i]:
continue
reads, writes = op_rw[i]
# Check slot limit
if slot_counts[engine] >= SLOT_LIMITS.get(engine, 1):
continue
# Check RAW: can't read what's being written this cycle
if reads & cycle_writes:
continue
# Check WAW: can't write same location twice in one cycle
if writes & cycle_writes:
continue
# Check dependencies on earlier ops that haven't completed yet
# An op "hasn't completed" if it's either:
# 1. Not yet scheduled (will run in a future cycle)
# 2. Scheduled THIS cycle (writes not visible until next cycle)
blocked = False
for j in range(i):
if not scheduled[j] or scheduled_cycle[j] == cycle_num:
earlier_reads, earlier_writes = op_rw[j]
# RAW: we read something an earlier op writes
if earlier_writes & reads:
blocked = True
break
# WAW: we write something an earlier op writes
if earlier_writes & writes:
blocked = True
break
# WAR: we write something an earlier op reads
# (must wait for earlier read to complete before we overwrite)
if writes & earlier_reads:
blocked = True
break
if blocked:
continue
# Schedule this op
bundle[engine].append(slot)
slot_counts[engine] += 1
cycle_writes.update(writes)
scheduled[i] = True
scheduled_cycle[i] = cycle_num
scheduled_this_cycle.append(i)
remaining -= 1
if not scheduled_this_cycle:
# Shouldn't happen if ops are valid
raise RuntimeError("No progress made in VLIW scheduling")
instrs.append(dict(bundle))
cycle_num += 1
return instrs
def build_kernel(
self, forest_height: int, n_nodes: int, batch_size: int, rounds: int
):
"""
Optimized SIMD kernel with VLIW bundling.
"""
n_groups = batch_size // VLEN # 256 / 8 = 32 groups
# === PHASE 1: Allocate all scratch space and pre-load all constants ===
# Scalar temps
tmp1 = self.alloc_scratch("tmp1")
tmp2 = self.alloc_scratch("tmp2")
tmp3 = self.alloc_scratch("tmp3")
# Init vars from memory - use separate temp for each to allow parallelism
init_vars = [
"rounds", "n_nodes", "batch_size", "forest_height",
"forest_values_p", "inp_indices_p", "inp_values_p",
]
init_temps = [self.alloc_scratch(f"init_tmp{i}") for i in range(len(init_vars))]
for v in init_vars:
self.alloc_scratch(v, 1)
init_ops = []
for i, v in enumerate(init_vars):
init_ops.append(("load", ("const", init_temps[i], i)))
init_ops.append(("load", ("load", self.scratch[v], init_temps[i])))
self.instrs.extend(self.build_vliw(init_ops))
# Pre-allocate ALL scalar constants we'll need - batch them for parallelism
const_ops = []
c0 = self.scratch_const(0, "c0", const_ops)
c1 = self.scratch_const(1, "c1", const_ops)
c2 = self.scratch_const(2, "c2", const_ops)
# Constants for group base offsets
base_consts = {}
for g in range(n_groups):
base = g * VLEN
base_consts[base] = self.scratch_const(base, f"base{g}", const_ops)
# Constants for hash stages
hash_scalar_consts = {}
for hi, (op1, val1, op2, op3, val3) in enumerate(HASH_STAGES):
if val1 not in hash_scalar_consts:
hash_scalar_consts[val1] = self.scratch_const(val1, batch=const_ops)
# For optimized stages, store power of 2 instead of shift amount
if op3 == "<<" and op2 == "+":
shift_mult = 1 << val3
if shift_mult not in hash_scalar_consts:
hash_scalar_consts[shift_mult] = self.scratch_const(shift_mult, batch=const_ops)
else:
if val3 not in hash_scalar_consts:
hash_scalar_consts[val3] = self.scratch_const(val3, batch=const_ops)
# Emit all const loads in parallel
self.instrs.extend(self.build_vliw(const_ops))
self.instrs.append({"flow": [("pause",)]})
# Per-group vectors: idx, val, node_val (nv), temp (t1)
groups = []
for g in range(n_groups):
groups.append({
"idx": self.alloc_scratch(f"g{g}_idx", VLEN),
"val": self.alloc_scratch(f"g{g}_val", VLEN),
"nv": self.alloc_scratch(f"g{g}_nv", VLEN),
"t1": self.alloc_scratch(f"g{g}_t1", VLEN),
})
# Per-group address buffers to avoid conflicts
addr_bufs = [self.alloc_scratch(f"addr_buf{g}", VLEN) for g in range(n_groups)]
# Vector constants
lb0 = self.vec_const(0, "lb0")
lb1 = self.vec_const(1, "lb1")
lb2 = self.vec_const(2, "lb2")
# Broadcast nodes for levels 0-2
bc_s0 = self.alloc_scratch("bc_s0") # node 0 (root)
bc_s1 = self.alloc_scratch("bc_s1") # node 1
bc_s2 = self.alloc_scratch("bc_s2") # node 2
bc_v1 = self.alloc_scratch("bc_v1", VLEN) # vector of node 1
bc_v2 = self.alloc_scratch("bc_v2", VLEN) # vector of node 2
# Note: Level 2+ use scattered loads - broadcast doesn't fit in scratch space
# Hash constants (pre-allocate all)
hash_consts = {}
for hi, (op1, val1, op2, op3, val3) in enumerate(HASH_STAGES):
hash_consts[(hi, 'v1')] = self.vec_const(val1, f"hc{hi}_v1")
# For left shifts with +, we can use multiply_add: a << n = a * 2^n
# Only convert to power of 2 if we'll actually use multiply_add
if op3 == "<<" and op2 == "+":
shift_mult = 1 << val3 # 2^val3
hash_consts[(hi, 'v3')] = self.vec_const(shift_mult, f"hc{hi}_v3_mult")
else:
hash_consts[(hi, 'v3')] = self.vec_const(val3, f"hc{hi}_v3")
# n_nodes as vector for wrap comparison
vn_nodes = self.alloc_scratch("vn_nodes", VLEN)
# === PHASE 2: Pre-ops - load init data and broadcast constants ===
pre_ops = []
for g in range(n_groups):
base = g * VLEN
addr_buf = addr_bufs[g]
pre_ops.append(("alu", ("+", addr_buf, self.scratch["inp_indices_p"], base_consts[base])))
pre_ops.append(("load", ("vload", groups[g]["idx"], addr_buf)))
pre_ops.append(("alu", ("+", addr_buf, self.scratch["inp_values_p"], base_consts[base])))
pre_ops.append(("load", ("vload", groups[g]["val"], addr_buf)))
# Broadcast scalar constants to vectors
pre_ops.append(("valu", ("vbroadcast", lb0, c0)))
pre_ops.append(("valu", ("vbroadcast", lb1, c1)))
pre_ops.append(("valu", ("vbroadcast", lb2, c2)))
pre_ops.append(("valu", ("vbroadcast", vn_nodes, self.scratch["n_nodes"])))
# Broadcast hash constants
for hi, (op1, val1, op2, op3, val3) in enumerate(HASH_STAGES):
hc1 = hash_consts[(hi, 'v1')]
hc3 = hash_consts[(hi, 'v3')]
pre_ops.append(("valu", ("vbroadcast", hc1, hash_scalar_consts[val1])))
# For optimized stages, hc3 holds power of 2, otherwise shift amount
if op3 == "<<" and op2 == "+":
shift_mult = 1 << val3
pre_ops.append(("valu", ("vbroadcast", hc3, hash_scalar_consts[shift_mult])))
else:
pre_ops.append(("valu", ("vbroadcast", hc3, hash_scalar_consts[val3])))
self.instrs.extend(self.build_vliw(pre_ops))
# === PHASE 3: Main loop over rounds ===
# Global wave pipelining: interleave groups to overlap loads and computes
def get_compute_ops(g, level):
"""Get compute operations for group g at given level."""
idx = groups[g]["idx"]
val = groups[g]["val"]
nv = groups[g]["nv"]
t1 = groups[g]["t1"]
ops = []
# val = val ^ node_val
ops.append(("valu", ("^", val, val, nv)))
# Hash computation
for hi, (op1, val1, op2, op3, val3) in enumerate(HASH_STAGES):
hc1 = hash_consts[(hi, 'v1')]
hc3 = hash_consts[(hi, 'v3')]
if op3 == "<<" and op2 == "+":
ops.append(("valu", (op1, t1, val, hc1)))
ops.append(("valu", ("multiply_add", val, val, hc3, t1)))
else:
ops.append(("valu", (op1, t1, val, hc1)))
ops.append(("valu", (op3, nv, val, hc3)))
ops.append(("valu", (op2, val, t1, nv)))
# idx = 2*idx + 1 + (val & 1)
ops.append(("valu", ("multiply_add", nv, idx, lb2, lb1))) # nv = idx * 2 + 1
ops.append(("valu", ("&", t1, val, lb1))) # t1 = val & 1
ops.append(("valu", ("+", idx, nv, t1))) # idx = nv + t1
# Wrap at level == forest_height only
if level == forest_height:
ops.append(("valu", ("^", idx, idx, idx)))
return ops
def get_load_ops(g, level, rnd):
"""Get load operations for group g at given level and round."""
idx = groups[g]["idx"]
nv = groups[g]["nv"]
t1 = groups[g]["t1"]
addr_buf = addr_bufs[g]
ops = []
if level == 0:
# Level 0: all groups share root node (already loaded before this)
ops.append(("valu", ("vbroadcast", nv, bc_s0)))
elif level == 1:
# Level 1: select between nodes 1 and 2 based on idx&1
ops.append(("valu", ("&", t1, idx, lb1)))
ops.append(("flow", ("vselect", nv, t1, bc_v1, bc_v2)))
else:
# Scattered loads
for lane in range(VLEN):
ops.append(("alu", ("+", addr_buf + lane, self.scratch["forest_values_p"], idx + lane)))
for lane in range(VLEN):
ops.append(("load", ("load", nv + lane, addr_buf + lane)))
return ops
# Pre-load shared nodes for levels 0 and 1
pre_round_ops = []
pre_round_ops.append(("alu", ("+", tmp1, self.scratch["forest_values_p"], c0)))
pre_round_ops.append(("load", ("load", bc_s0, tmp1)))
pre_round_ops.append(("alu", ("+", tmp1, self.scratch["forest_values_p"], c1)))
pre_round_ops.append(("load", ("load", bc_s1, tmp1)))
pre_round_ops.append(("alu", ("+", tmp2, self.scratch["forest_values_p"], c2)))
pre_round_ops.append(("load", ("load", bc_s2, tmp2)))
pre_round_ops.append(("valu", ("vbroadcast", bc_v1, bc_s1)))
pre_round_ops.append(("valu", ("vbroadcast", bc_v2, bc_s2)))
self.instrs.extend(self.build_vliw(pre_round_ops))
# Process rounds in batches for better cross-round pipelining
# Larger batches give better overlap but slower scheduling
BATCH_SIZE = 16 # Process all rounds together
for batch_start in range(0, rounds, BATCH_SIZE):
batch_ops = []
for rnd in range(batch_start, min(batch_start + BATCH_SIZE, rounds)):
level = rnd % (forest_height + 1)
for g in range(n_groups):
batch_ops.extend(get_load_ops(g, level, rnd))
batch_ops.extend(get_compute_ops(g, level))
self.instrs.extend(self.build_vliw(batch_ops))
# Final store phase
store_ops = []
for g in range(n_groups):
base = g * VLEN
addr_buf = addr_bufs[g]
store_ops.append(("alu", ("+", addr_buf, self.scratch["inp_values_p"], base_consts[base])))
store_ops.append(("store", ("vstore", addr_buf, groups[g]["val"])))
self.instrs.extend(self.build_vliw(store_ops))
self.instrs.append({"flow": [("pause",)]})
BASELINE = 147734
def do_kernel_test(
forest_height: int,
rounds: int,
batch_size: int,
seed: int = 123,
trace: bool = False,
prints: bool = False,
):
print(f"{forest_height=}, {rounds=}, {batch_size=}")
random.seed(seed)
forest = Tree.generate(forest_height)
inp = Input.generate(forest, batch_size, rounds)
mem = build_mem_image(forest, inp)
kb = KernelBuilder()
kb.build_kernel(forest.height, len(forest.values), len(inp.indices), rounds)
# print(kb.instrs)
value_trace = {}
machine = Machine(
mem,
kb.instrs,
kb.debug_info(),
n_cores=N_CORES,
value_trace=value_trace,
trace=trace,
)
machine.prints = prints
for i, ref_mem in enumerate(reference_kernel2(mem, value_trace)):
machine.run()
inp_values_p = ref_mem[6]
if prints:
print(machine.mem[inp_values_p : inp_values_p + len(inp.values)])
print(ref_mem[inp_values_p : inp_values_p + len(inp.values)])
assert (
machine.mem[inp_values_p : inp_values_p + len(inp.values)]
== ref_mem[inp_values_p : inp_values_p + len(inp.values)]
), f"Incorrect result on round {i}"
inp_indices_p = ref_mem[5]
if prints:
print(machine.mem[inp_indices_p : inp_indices_p + len(inp.indices)])
print(ref_mem[inp_indices_p : inp_indices_p + len(inp.indices)])
# Updating these in memory isn't required, but you can enable this check for debugging
# assert machine.mem[inp_indices_p:inp_indices_p+len(inp.indices)] == ref_mem[inp_indices_p:inp_indices_p+len(inp.indices)]
print("CYCLES: ", machine.cycle)
print("Speedup over baseline: ", BASELINE / machine.cycle)
return machine.cycle
class Tests(unittest.TestCase):
def test_ref_kernels(self):
"""
Test the reference kernels against each other
"""
random.seed(123)
for i in range(10):
f = Tree.generate(4)
inp = Input.generate(f, 10, 6)
mem = build_mem_image(f, inp)
reference_kernel(f, inp)
for _ in reference_kernel2(mem, {}):
pass
assert inp.indices == mem[mem[5] : mem[5] + len(inp.indices)]
assert inp.values == mem[mem[6] : mem[6] + len(inp.values)]
def test_kernel_trace(self):
# Full-scale example for performance testing
do_kernel_test(10, 16, 256, trace=True, prints=False)
# Passing this test is not required for submission, see submission_tests.py for the actual correctness test
# You can uncomment this if you think it might help you debug
# def test_kernel_correctness(self):
# for batch in range(1, 3):
# for forest_height in range(3):
# do_kernel_test(
# forest_height + 2, forest_height + 4, batch * 16 * VLEN * N_CORES
# )
def test_kernel_cycles(self):
do_kernel_test(10, 16, 256)
# To run all the tests:
# python perf_takehome.py
# To run a specific test:
# python perf_takehome.py Tests.test_kernel_cycles
# To view a hot-reloading trace of all the instructions: **Recommended debug loop**
# NOTE: The trace hot-reloading only works in Chrome. In the worst case if things aren't working, drag trace.json onto https://ui.perfetto.dev/
# python perf_takehome.py Tests.test_kernel_trace
# Then run `python watch_trace.py` in another tab, it'll open a browser tab, then click "Open Perfetto"
# You can then keep that open and re-run the test to see a new trace.
# To run the proper checks to see which thresholds you pass:
# python tests/submission_tests.py
if __name__ == "__main__":
unittest.main()
Raw
checkpoint_717c5f316c1262bc@v3
"""
# Anthropic's Original Performance Engineering Take-home (Release version)
Copyright Anthropic PBC 2026. Permission is granted to modify and use, but not
to publish or redistribute your solutions so it's hard to find spoilers.
# Task
- Optimize the kernel (in KernelBuilder.build_kernel) as much as possible in the
available time, as measured by test_kernel_cycles on a frozen separate copy
of the simulator.
Validate your results using `python tests/submission_tests.py` without modifying
anything in the tests/ folder.
We recommend you look through problem.py next.
"""
from collections import defaultdict
import random
import unittest
from problem import (
Engine,
DebugInfo,
SLOT_LIMITS,
VLEN,
N_CORES,
SCRATCH_SIZE,
Machine,
Tree,
Input,
HASH_STAGES,
reference_kernel,
build_mem_image,
reference_kernel2,
)
class KernelBuilder:
def __init__(self):
self.instrs = []
self.scratch = {}
self.scratch_debug = {}
self.scratch_ptr = 0
self.const_map = {}
self.vec_const_map = {}
def debug_info(self):
return DebugInfo(scratch_map=self.scratch_debug)
def add(self, engine, slot):
self.instrs.append({engine: [slot]})
def alloc_scratch(self, name=None, length=1):
addr = self.scratch_ptr
if name is not None:
self.scratch[name] = addr
self.scratch_debug[addr] = (name, length)
self.scratch_ptr += length
assert self.scratch_ptr <= SCRATCH_SIZE, "Out of scratch space"
return addr
def scratch_const(self, val, name=None, batch=None):
"""Allocate or return address for scalar constant.
If batch is provided, append the load to batch instead of self.instrs.
"""
if val not in self.const_map:
addr = self.alloc_scratch(name)
if batch is not None:
batch.append(("load", ("const", addr, val)))
else:
self.add("load", ("const", addr, val))
self.const_map[val] = addr
return self.const_map[val]
def ensure_const(self, val):
"""Return address for a constant, raising error if not pre-allocated."""
if val not in self.const_map:
raise ValueError(f"Constant {val} not pre-allocated")
return self.const_map[val]
def vec_const(self, val, name=None):
"""Allocate a vector constant (VLEN slots) and return address."""
if val not in self.vec_const_map:
addr = self.alloc_scratch(name, VLEN)
self.vec_const_map[val] = addr
return self.vec_const_map[val]
def get_rw_addresses(self, engine, slot):
"""Return (reads, writes) sets of scratch addresses touched by this op."""
reads = set()
writes = set()
if engine == "alu":
op, dest, a1, a2 = slot
reads.add(a1)
reads.add(a2)
writes.add(dest)
elif engine == "valu":
if slot[0] == "vbroadcast":
_, dest, src = slot
reads.add(src)
for i in range(VLEN):
writes.add(dest + i)
elif slot[0] == "multiply_add":
_, dest, a, b, c = slot
for i in range(VLEN):
reads.update([a + i, b + i, c + i])
writes.add(dest + i)
else:
op, dest, a1, a2 = slot
for i in range(VLEN):
reads.add(a1 + i)
reads.add(a2 + i)
writes.add(dest + i)
elif engine == "load":
if slot[0] == "load":
_, dest, addr = slot
reads.add(addr)
writes.add(dest)
elif slot[0] == "load_offset":
_, dest, addr, offset = slot
reads.add(addr + offset)
writes.add(dest + offset)
elif slot[0] == "vload":
_, dest, addr = slot
reads.add(addr)
for i in range(VLEN):
writes.add(dest + i)
elif slot[0] == "const":
_, dest, val = slot
writes.add(dest)
elif engine == "store":
if slot[0] == "store":
_, addr, src = slot
reads.add(addr)
reads.add(src)
elif slot[0] == "vstore":
_, addr, src = slot
reads.add(addr)
for i in range(VLEN):
reads.add(src + i)
elif engine == "flow":
if slot[0] == "select":
_, dest, cond, a, b = slot
reads.update([cond, a, b])
writes.add(dest)
elif slot[0] == "vselect":
_, dest, cond, a, b = slot
for i in range(VLEN):
reads.update([cond + i, a + i, b + i])
writes.add(dest + i)
elif slot[0] == "add_imm":
_, dest, a, imm = slot
reads.add(a)
writes.add(dest)
# pause, halt don't read/write scratch
elif engine == "debug":
pass # Debug ops don't affect scheduling
return reads, writes
def build_vliw(self, ops):
"""
Pack a list of (engine, slot) ops into VLIW bundles respecting slot limits and dependencies.
Uses a dependency-graph based scheduler for O(n) per cycle instead of O(n²).
"""
if not ops:
return []
n = len(ops)
op_rw = [self.get_rw_addresses(e, s) for e, s in ops]
# Build dependency graph: for each op, which earlier ops must complete first?
# Also track reverse edges for efficient ready-set updates
deps = [set() for _ in range(n)] # deps[i] = set of op indices that i depends on
rdeps = [set() for _ in range(n)] # rdeps[i] = set of op indices that depend on i
# Build address -> op index mappings for efficient dependency lookup
write_ops = defaultdict(list) # addr -> list of (op_idx, is_read) that write this addr
read_ops = defaultdict(list) # addr -> list of op_idx that read this addr
for i, (reads, writes) in enumerate(op_rw):
for addr in reads:
# RAW: we depend on any earlier op that writes this address
for j in write_ops[addr]:
if j < i:
deps[i].add(j)
rdeps[j].add(i)
read_ops[addr].append(i)
for addr in writes:
# WAW: we depend on any earlier op that writes this address
for j in write_ops[addr]:
if j < i:
deps[i].add(j)
rdeps[j].add(i)
# WAR: we depend on any earlier op that reads this address
for j in read_ops[addr]:
if j < i:
deps[i].add(j)
rdeps[j].add(i)
write_ops[addr].append(i)
# Initialize ready set (ops with no dependencies)
ready = set(i for i in range(n) if not deps[i])
scheduled_cycle = [-1] * n
instrs = []
cycle_num = 0
while ready:
bundle = defaultdict(list)
slot_counts = defaultdict(int)
cycle_writes = set()
scheduled_this_cycle = []
# Sort ready ops by index to maintain deterministic ordering
for i in sorted(ready):
engine, slot = ops[i]
reads, writes = op_rw[i]
# Check slot limit
if slot_counts[engine] >= SLOT_LIMITS.get(engine, 1):
continue
# Check RAW: can't read what's being written this cycle
if reads & cycle_writes:
continue
# Check WAW: can't write same location twice in one cycle
if writes & cycle_writes:
continue
# Schedule this op
bundle[engine].append(slot)
slot_counts[engine] += 1
cycle_writes.update(writes)
scheduled_cycle[i] = cycle_num
scheduled_this_cycle.append(i)
if not scheduled_this_cycle:
raise RuntimeError("No progress made in VLIW scheduling")
# Remove scheduled ops from ready set and update dependents
for i in scheduled_this_cycle:
ready.remove(i)
# At end of cycle, update ready set with newly unblocked ops
# An op is ready when all its dependencies have been scheduled in PREVIOUS cycles
for i in scheduled_this_cycle:
for j in rdeps[i]:
if scheduled_cycle[j] == -1: # Not yet scheduled
# Check if all dependencies are now scheduled in previous cycles
all_deps_done = all(
scheduled_cycle[d] != -1 and scheduled_cycle[d] < cycle_num + 1
for d in deps[j]
)
if all_deps_done:
ready.add(j)
instrs.append(dict(bundle))
cycle_num += 1
return instrs
def build_kernel(
self, forest_height: int, n_nodes: int, batch_size: int, rounds: int
):
"""
Optimized SIMD kernel with VLIW bundling.
"""
n_groups = batch_size // VLEN # 256 / 8 = 32 groups
# === PHASE 1: Allocate all scratch space and pre-load all constants ===
# Scalar temps
tmp1 = self.alloc_scratch("tmp1")
tmp2 = self.alloc_scratch("tmp2")
tmp3 = self.alloc_scratch("tmp3")
# Init vars from memory - use separate temp for each to allow parallelism
init_vars = [
"rounds", "n_nodes", "batch_size", "forest_height",
"forest_values_p", "inp_indices_p", "inp_values_p",
]
init_temps = [self.alloc_scratch(f"init_tmp{i}") for i in range(len(init_vars))]
for v in init_vars:
self.alloc_scratch(v, 1)
init_ops = []
for i, v in enumerate(init_vars):
init_ops.append(("load", ("const", init_temps[i], i)))
init_ops.append(("load", ("load", self.scratch[v], init_temps[i])))
self.instrs.extend(self.build_vliw(init_ops))
# Pre-allocate ALL scalar constants we'll need - batch them for parallelism
const_ops = []
c0 = self.scratch_const(0, "c0", const_ops)
c1 = self.scratch_const(1, "c1", const_ops)
c2 = self.scratch_const(2, "c2", const_ops)
c3 = self.scratch_const(3, "c3", const_ops)
c4 = self.scratch_const(4, "c4", const_ops)
c5 = self.scratch_const(5, "c5", const_ops)
c6 = self.scratch_const(6, "c6", const_ops)
# Constants for group base offsets
base_consts = {}
for g in range(n_groups):
base = g * VLEN
base_consts[base] = self.scratch_const(base, f"base{g}", const_ops)
# Constants for hash stages
hash_scalar_consts = {}
for hi, (op1, val1, op2, op3, val3) in enumerate(HASH_STAGES):
if val1 not in hash_scalar_consts:
hash_scalar_consts[val1] = self.scratch_const(val1, batch=const_ops)
# For optimized stages, store power of 2 instead of shift amount
if op3 == "<<" and op2 == "+":
shift_mult = 1 << val3
if shift_mult not in hash_scalar_consts:
hash_scalar_consts[shift_mult] = self.scratch_const(shift_mult, batch=const_ops)
else:
if val3 not in hash_scalar_consts:
hash_scalar_consts[val3] = self.scratch_const(val3, batch=const_ops)
# Emit all const loads in parallel
self.instrs.extend(self.build_vliw(const_ops))
self.instrs.append({"flow": [("pause",)]})
# Per-group vectors: idx, val, node_val (nv), temp (t1)
groups = []
for g in range(n_groups):
groups.append({
"idx": self.alloc_scratch(f"g{g}_idx", VLEN),
"val": self.alloc_scratch(f"g{g}_val", VLEN),
"nv": self.alloc_scratch(f"g{g}_nv", VLEN),
"t1": self.alloc_scratch(f"g{g}_t1", VLEN),
})
# Per-group address buffers to avoid conflicts
addr_bufs = [self.alloc_scratch(f"addr_buf{g}", VLEN) for g in range(n_groups)]
# Vector constants
lb0 = self.vec_const(0, "lb0")
lb1 = self.vec_const(1, "lb1")
lb2 = self.vec_const(2, "lb2")
# Broadcast nodes for levels 0-2
bc_s0 = self.alloc_scratch("bc_s0") # node 0 (root)
bc_s1 = self.alloc_scratch("bc_s1") # node 1
bc_s2 = self.alloc_scratch("bc_s2") # node 2
bc_v1 = self.alloc_scratch("bc_v1", VLEN) # vector of node 1
bc_v2 = self.alloc_scratch("bc_v2", VLEN) # vector of node 2
# Level 2 broadcast: nodes 3, 4, 5, 6
bc_s3 = self.alloc_scratch("bc_s3")
bc_s4 = self.alloc_scratch("bc_s4")
bc_s5 = self.alloc_scratch("bc_s5")
bc_s6 = self.alloc_scratch("bc_s6")
bc_v3 = self.alloc_scratch("bc_v3", VLEN)
bc_v4 = self.alloc_scratch("bc_v4", VLEN)
bc_v5 = self.alloc_scratch("bc_v5", VLEN)
bc_v6 = self.alloc_scratch("bc_v6", VLEN)
# Level 3 broadcast: nodes 7-14
bc_s7_14 = [self.alloc_scratch(f"bc_s{i}") for i in range(7, 15)]
bc_v7_14 = [self.alloc_scratch(f"bc_v{i}", VLEN) for i in range(7, 15)]
l3_consts = [self.scratch_const(i, f"c{i}", const_ops) for i in range(7, 15)]
# Hash constants (pre-allocate all)
hash_consts = {}
for hi, (op1, val1, op2, op3, val3) in enumerate(HASH_STAGES):
hash_consts[(hi, 'v1')] = self.vec_const(val1, f"hc{hi}_v1")
# For left shifts with +, we can use multiply_add: a << n = a * 2^n
# Only convert to power of 2 if we'll actually use multiply_add
if op3 == "<<" and op2 == "+":
shift_mult = 1 << val3 # 2^val3
hash_consts[(hi, 'v3')] = self.vec_const(shift_mult, f"hc{hi}_v3_mult")
else:
hash_consts[(hi, 'v3')] = self.vec_const(val3, f"hc{hi}_v3")
# === PHASE 2: Pre-ops - load init data and broadcast constants ===
pre_ops = []
for g in range(n_groups):
base = g * VLEN
addr_buf = addr_bufs[g]
pre_ops.append(("alu", ("+", addr_buf, self.scratch["inp_indices_p"], base_consts[base])))
pre_ops.append(("load", ("vload", groups[g]["idx"], addr_buf)))
pre_ops.append(("alu", ("+", addr_buf, self.scratch["inp_values_p"], base_consts[base])))
pre_ops.append(("load", ("vload", groups[g]["val"], addr_buf)))
# Broadcast scalar constants to vectors
pre_ops.append(("valu", ("vbroadcast", lb0, c0)))
pre_ops.append(("valu", ("vbroadcast", lb1, c1)))
pre_ops.append(("valu", ("vbroadcast", lb2, c2)))
# Broadcast hash constants
for hi, (op1, val1, op2, op3, val3) in enumerate(HASH_STAGES):
hc1 = hash_consts[(hi, 'v1')]
hc3 = hash_consts[(hi, 'v3')]
pre_ops.append(("valu", ("vbroadcast", hc1, hash_scalar_consts[val1])))
# For optimized stages, hc3 holds power of 2, otherwise shift amount
if op3 == "<<" and op2 == "+":
shift_mult = 1 << val3
pre_ops.append(("valu", ("vbroadcast", hc3, hash_scalar_consts[shift_mult])))
else:
pre_ops.append(("valu", ("vbroadcast", hc3, hash_scalar_consts[val3])))
# Merge pre_ops with pre_round_ops for better scheduling overlap
# Pre-load shared nodes for levels 0, 1, and 2
# Level 0
pre_ops.append(("alu", ("+", tmp1, self.scratch["forest_values_p"], c0)))
pre_ops.append(("load", ("load", bc_s0, tmp1)))
# Level 1
pre_ops.append(("alu", ("+", tmp1, self.scratch["forest_values_p"], c1)))
pre_ops.append(("load", ("load", bc_s1, tmp1)))
pre_ops.append(("alu", ("+", tmp2, self.scratch["forest_values_p"], c2)))
pre_ops.append(("load", ("load", bc_s2, tmp2)))
pre_ops.append(("valu", ("vbroadcast", bc_v1, bc_s1)))
pre_ops.append(("valu", ("vbroadcast", bc_v2, bc_s2)))
# Level 2
pre_ops.append(("alu", ("+", tmp1, self.scratch["forest_values_p"], c3)))
pre_ops.append(("load", ("load", bc_s3, tmp1)))
pre_ops.append(("alu", ("+", tmp2, self.scratch["forest_values_p"], c4)))
pre_ops.append(("load", ("load", bc_s4, tmp2)))
pre_ops.append(("alu", ("+", tmp3, self.scratch["forest_values_p"], c5)))
pre_ops.append(("load", ("load", bc_s5, tmp3)))
pre_ops.append(("alu", ("+", tmp1, self.scratch["forest_values_p"], c6)))
pre_ops.append(("load", ("load", bc_s6, tmp1)))
pre_ops.append(("valu", ("vbroadcast", bc_v3, bc_s3)))
pre_ops.append(("valu", ("vbroadcast", bc_v4, bc_s4)))
pre_ops.append(("valu", ("vbroadcast", bc_v5, bc_s5)))
pre_ops.append(("valu", ("vbroadcast", bc_v6, bc_s6)))
# Level 3: nodes 7-14
for i, c_addr in enumerate(l3_consts):
node_idx = 7 + i
pre_ops.append(("alu", ("+", tmp1 if i % 3 == 0 else (tmp2 if i % 3 == 1 else tmp3),
self.scratch["forest_values_p"], c_addr)))
pre_ops.append(("load", ("load", bc_s7_14[i], tmp1 if i % 3 == 0 else (tmp2 if i % 3 == 1 else tmp3))))
for i in range(8):
pre_ops.append(("valu", ("vbroadcast", bc_v7_14[i], bc_s7_14[i])))
self.instrs.extend(self.build_vliw(pre_ops))
# === PHASE 3: Main loop over rounds ===
# Global wave pipelining: interleave groups to overlap loads and computes
def get_compute_ops(g, level):
"""Get compute operations for group g at given level."""
idx = groups[g]["idx"]
val = groups[g]["val"]
nv = groups[g]["nv"]
t1 = groups[g]["t1"]
ops = []
# val = val ^ node_val
ops.append(("valu", ("^", val, val, nv)))
# Hash computation
for hi, (op1, val1, op2, op3, val3) in enumerate(HASH_STAGES):
hc1 = hash_consts[(hi, 'v1')]
hc3 = hash_consts[(hi, 'v3')]
if op3 == "<<" and op2 == "+":
ops.append(("valu", (op1, t1, val, hc1)))
ops.append(("valu", ("multiply_add", val, val, hc3, t1)))
else:
ops.append(("valu", (op1, t1, val, hc1)))
ops.append(("valu", (op3, nv, val, hc3)))
ops.append(("valu", (op2, val, t1, nv)))
# idx = 2*idx + 1 + (val & 1)
ops.append(("valu", ("multiply_add", nv, idx, lb2, lb1))) # nv = idx * 2 + 1
ops.append(("valu", ("&", t1, val, lb1))) # t1 = val & 1
ops.append(("valu", ("+", idx, nv, t1))) # idx = nv + t1
# Wrap at level == forest_height only
if level == forest_height:
ops.append(("valu", ("^", idx, idx, idx)))
return ops
def get_load_ops(g, level, rnd):
"""Get load operations for group g at given level and round."""
idx = groups[g]["idx"]
nv = groups[g]["nv"]
t1 = groups[g]["t1"]
addr_buf = addr_bufs[g]
ops = []
if level == 0:
# Level 0: all groups share root node (already loaded before this)
ops.append(("valu", ("vbroadcast", nv, bc_s0)))
elif level == 1:
# Level 1: select between nodes 1 and 2 based on idx&1
ops.append(("valu", ("&", t1, idx, lb1)))
ops.append(("flow", ("vselect", nv, t1, bc_v1, bc_v2)))
elif level == 2:
# Level 2: 4-way select among nodes 3,4,5,6 based on idx[1:0]
# frozen_problem vselect: vselect(dest, cond, a, b) = a if cond!=0 else b
# For idx in [3,4,5,6], bit0=idx&1, bit1=(idx>>1)&1
# Mapping: idx=3(11)->v3, idx=4(00)->v4, idx=5(01)->v5, idx=6(10)->v6
ops.append(("valu", (">>", t1, idx, lb1))) # t1 = idx >> 1
ops.append(("valu", ("&", t1, t1, lb1))) # t1 = bit1
# bit1=0 -> need v4 or v5, bit1=1 -> need v3 or v6
ops.append(("flow", ("vselect", nv, t1, bc_v6, bc_v4))) # nv = bc_v6 if bit1!=0 else bc_v4
ops.append(("flow", ("vselect", addr_buf, t1, bc_v3, bc_v5))) # temp = bc_v3 if bit1!=0 else bc_v5
ops.append(("valu", ("&", t1, idx, lb1))) # t1 = bit0
# bit0=0 -> nv (v4 or v6), bit0=1 -> temp (v5 or v3)
ops.append(("flow", ("vselect", nv, t1, addr_buf, nv))) # nv = temp if bit0!=0 else nv
else:
# Scattered loads for levels 3+
for lane in range(VLEN):
ops.append(("alu", ("+", addr_buf + lane, self.scratch["forest_values_p"], idx + lane)))
for lane in range(VLEN):
ops.append(("load", ("load", nv + lane, addr_buf + lane)))
return ops
# Try sequential waves: process Wave A completely, then Wave B
# This loses overlap but may allow optimizations
# First, process with interleaved waves (offset approach)
# This gives better overlap of VALU and load
WAVE_OFFSET = 5
batch_ops = []
total_virtual_rounds = rounds + WAVE_OFFSET
for virtual_rnd in range(total_virtual_rounds):
# Wave A (groups 0-15)
if virtual_rnd < rounds:
level = virtual_rnd % (forest_height + 1)
for g in range(16):
batch_ops.extend(get_load_ops(g, level, virtual_rnd))
batch_ops.extend(get_compute_ops(g, level))
# Wave B (groups 16-31) with offset
wave_b_rnd = virtual_rnd - WAVE_OFFSET
if 0 <= wave_b_rnd < rounds:
level = wave_b_rnd % (forest_height + 1)
for g in range(16, 32):
batch_ops.extend(get_load_ops(g, level, wave_b_rnd))
batch_ops.extend(get_compute_ops(g, level))
self.instrs.extend(self.build_vliw(batch_ops))
# Final store phase
store_ops = []
for g in range(n_groups):
base = g * VLEN
addr_buf = addr_bufs[g]
store_ops.append(("alu", ("+", addr_buf, self.scratch["inp_values_p"], base_consts[base])))
store_ops.append(("store", ("vstore", addr_buf, groups[g]["val"])))
self.instrs.extend(self.build_vliw(store_ops))
self.instrs.append({"flow": [("pause",)]})
BASELINE = 147734
def do_kernel_test(
forest_height: int,
rounds: int,
batch_size: int,
seed: int = 123,
trace: bool = False,
prints: bool = False,
):
print(f"{forest_height=}, {rounds=}, {batch_size=}")
random.seed(seed)
forest = Tree.generate(forest_height)
inp = Input.generate(forest, batch_size, rounds)
mem = build_mem_image(forest, inp)
kb = KernelBuilder()
kb.build_kernel(forest.height, len(forest.values), len(inp.indices), rounds)
# print(kb.instrs)
value_trace = {}
machine = Machine(
mem,
kb.instrs,
kb.debug_info(),
n_cores=N_CORES,
value_trace=value_trace,
trace=trace,
)
machine.prints = prints
for i, ref_mem in enumerate(reference_kernel2(mem, value_trace)):
machine.run()
inp_values_p = ref_mem[6]
if prints:
print(machine.mem[inp_values_p : inp_values_p + len(inp.values)])
print(ref_mem[inp_values_p : inp_values_p + len(inp.values)])
assert (
machine.mem[inp_values_p : inp_values_p + len(inp.values)]
== ref_mem[inp_values_p : inp_values_p + len(inp.values)]
), f"Incorrect result on round {i}"
inp_indices_p = ref_mem[5]
if prints:
print(machine.mem[inp_indices_p : inp_indices_p + len(inp.indices)])
print(ref_mem[inp_indices_p : inp_indices_p + len(inp.indices)])
# Updating these in memory isn't required, but you can enable this check for debugging
# assert machine.mem[inp_indices_p:inp_indices_p+len(inp.indices)] == ref_mem[inp_indices_p:inp_indices_p+len(inp.indices)]
print("CYCLES: ", machine.cycle)
print("Speedup over baseline: ", BASELINE / machine.cycle)
return machine.cycle
class Tests(unittest.TestCase):
def test_ref_kernels(self):
"""
Test the reference kernels against each other
"""
random.seed(123)
for i in range(10):
f = Tree.generate(4)
inp = Input.generate(f, 10, 6)
mem = build_mem_image(f, inp)
reference_kernel(f, inp)
for _ in reference_kernel2(mem, {}):
pass
assert inp.indices == mem[mem[5] : mem[5] + len(inp.indices)]
assert inp.values == mem[mem[6] : mem[6] + len(inp.values)]
def test_kernel_trace(self):
# Full-scale example for performance testing
do_kernel_test(10, 16, 256, trace=True, prints=False)
# Passing this test is not required for submission, see submission_tests.py for the actual correctness test
# You can uncomment this if you think it might help you debug
# def test_kernel_correctness(self):
# for batch in range(1, 3):
# for forest_height in range(3):
# do_kernel_test(
# forest_height + 2, forest_height + 4, batch * 16 * VLEN * N_CORES
# )
def test_kernel_cycles(self):
do_kernel_test(10, 16, 256)
# To run all the tests:
# python perf_takehome.py
# To run a specific test:
# python perf_takehome.py Tests.test_kernel_cycles
# To view a hot-reloading trace of all the instructions: **Recommended debug loop**
# NOTE: The trace hot-reloading only works in Chrome. In the worst case if things aren't working, drag trace.json onto https://ui.perfetto.dev/
# python perf_takehome.py Tests.test_kernel_trace
# Then run `python watch_trace.py` in another tab, it'll open a browser tab, then click "Open Perfetto"
# You can then keep that open and re-run the test to see a new trace.
# To run the proper checks to see which thresholds you pass:
# python tests/submission_tests.py
if __name__ == "__main__":
unittest.main()
Raw
checkpoint_717c5f316c1262bc@v4
"""
# Anthropic's Original Performance Engineering Take-home (Release version)
Copyright Anthropic PBC 2026. Permission is granted to modify and use, but not
to publish or redistribute your solutions so it's hard to find spoilers.
# Task
- Optimize the kernel (in KernelBuilder.build_kernel) as much as possible in the
available time, as measured by test_kernel_cycles on a frozen separate copy
of the simulator.
Validate your results using `python tests/submission_tests.py` without modifying
anything in the tests/ folder.
We recommend you look through problem.py next.
"""
from collections import defaultdict
import random
import unittest
from problem import (
Engine,
DebugInfo,
SLOT_LIMITS,
VLEN,
N_CORES,
SCRATCH_SIZE,
Machine,
Tree,
Input,
HASH_STAGES,
reference_kernel,
build_mem_image,
reference_kernel2,
)
class KernelBuilder:
def __init__(self):
self.instrs = []
self.scratch = {}
self.scratch_debug = {}
self.scratch_ptr = 0
self.const_map = {}
def debug_info(self):
return DebugInfo(scratch_map=self.scratch_debug)
def build(self, slots: list[tuple[Engine, tuple]], vliw: bool = False):
# Simple slot packing that just uses one slot per instruction bundle
instrs = []
for engine, slot in slots:
instrs.append({engine: [slot]})
return instrs
def add(self, engine, slot):
self.instrs.append({engine: [slot]})
def alloc_scratch(self, name=None, length=1):
addr = self.scratch_ptr
if name is not None:
self.scratch[name] = addr
self.scratch_debug[addr] = (name, length)
self.scratch_ptr += length
assert self.scratch_ptr <= SCRATCH_SIZE, "Out of scratch space"
return addr
def scratch_const(self, val, name=None):
if val not in self.const_map:
addr = self.alloc_scratch(name)
self.add("load", ("const", addr, val))
self.const_map[val] = addr
return self.const_map[val]
def build_hash(self, val_hash_addr, tmp1, tmp2, round, i):
slots = []
for hi, (op1, val1, op2, op3, val3) in enumerate(HASH_STAGES):
slots.append(("alu", (op1, tmp1, val_hash_addr, self.scratch_const(val1))))
slots.append(("alu", (op3, tmp2, val_hash_addr, self.scratch_const(val3))))
slots.append(("alu", (op2, val_hash_addr, tmp1, tmp2)))
slots.append(("debug", ("compare", val_hash_addr, (round, i, "hash_stage", hi))))
return slots
def build_kernel(
self, forest_height: int, n_nodes: int, batch_size: int, rounds: int
):
"""
Like reference_kernel2 but building actual instructions.
Scalar implementation using only scalar ALU and load/store.
"""
tmp1 = self.alloc_scratch("tmp1")
tmp2 = self.alloc_scratch("tmp2")
tmp3 = self.alloc_scratch("tmp3")
# Scratch space addresses
init_vars = [
"rounds",
"n_nodes",
"batch_size",
"forest_height",
"forest_values_p",
"inp_indices_p",
"inp_values_p",
]
for v in init_vars:
self.alloc_scratch(v, 1)
for i, v in enumerate(init_vars):
self.add("load", ("const", tmp1, i))
self.add("load", ("load", self.scratch[v], tmp1))
zero_const = self.scratch_const(0)
one_const = self.scratch_const(1)
two_const = self.scratch_const(2)
# Pause instructions are matched up with yield statements in the reference
# kernel to let you debug at intermediate steps. The testing harness in this
# file requires these match up to the reference kernel's yields, but the
# submission harness ignores them.
self.add("flow", ("pause",))
# Any debug engine instruction is ignored by the submission simulator
self.add("debug", ("comment", "Starting loop"))
body = [] # array of slots
# Scalar scratch registers
tmp_idx = self.alloc_scratch("tmp_idx")
tmp_val = self.alloc_scratch("tmp_val")
tmp_node_val = self.alloc_scratch("tmp_node_val")
tmp_addr = self.alloc_scratch("tmp_addr")
for round in range(rounds):
for i in range(batch_size):
i_const = self.scratch_const(i)
# idx = mem[inp_indices_p + i]
body.append(("alu", ("+", tmp_addr, self.scratch["inp_indices_p"], i_const)))
body.append(("load", ("load", tmp_idx, tmp_addr)))
body.append(("debug", ("compare", tmp_idx, (round, i, "idx"))))
# val = mem[inp_values_p + i]
body.append(("alu", ("+", tmp_addr, self.scratch["inp_values_p"], i_const)))
body.append(("load", ("load", tmp_val, tmp_addr)))
body.append(("debug", ("compare", tmp_val, (round, i, "val"))))
# node_val = mem[forest_values_p + idx]
body.append(("alu", ("+", tmp_addr, self.scratch["forest_values_p"], tmp_idx)))
body.append(("load", ("load", tmp_node_val, tmp_addr)))
body.append(("debug", ("compare", tmp_node_val, (round, i, "node_val"))))
# val = myhash(val ^ node_val)
body.append(("alu", ("^", tmp_val, tmp_val, tmp_node_val)))
body.extend(self.build_hash(tmp_val, tmp1, tmp2, round, i))
body.append(("debug", ("compare", tmp_val, (round, i, "hashed_val"))))
# idx = 2*idx + (1 if val % 2 == 0 else 2)
body.append(("alu", ("%", tmp1, tmp_val, two_const)))
body.append(("alu", ("==", tmp1, tmp1, zero_const)))
body.append(("flow", ("select", tmp3, tmp1, one_const, two_const)))
body.append(("alu", ("*", tmp_idx, tmp_idx, two_const)))
body.append(("alu", ("+", tmp_idx, tmp_idx, tmp3)))
body.append(("debug", ("compare", tmp_idx, (round, i, "next_idx"))))
# idx = 0 if idx >= n_nodes else idx
body.append(("alu", ("<", tmp1, tmp_idx, self.scratch["n_nodes"])))
body.append(("flow", ("select", tmp_idx, tmp1, tmp_idx, zero_const)))
body.append(("debug", ("compare", tmp_idx, (round, i, "wrapped_idx"))))
# mem[inp_indices_p + i] = idx
body.append(("alu", ("+", tmp_addr, self.scratch["inp_indices_p"], i_const)))
body.append(("store", ("store", tmp_addr, tmp_idx)))
# mem[inp_values_p + i] = val
body.append(("alu", ("+", tmp_addr, self.scratch["inp_values_p"], i_const)))
body.append(("store", ("store", tmp_addr, tmp_val)))
body_instrs = self.build(body)
self.instrs.extend(body_instrs)
# Required to match with the yield in reference_kernel2
self.instrs.append({"flow": [("pause",)]})
BASELINE = 147734
def do_kernel_test(
forest_height: int,
rounds: int,
batch_size: int,
seed: int = 123,
trace: bool = False,
prints: bool = False,
):
print(f"{forest_height=}, {rounds=}, {batch_size=}")
random.seed(seed)
forest = Tree.generate(forest_height)
inp = Input.generate(forest, batch_size, rounds)
mem = build_mem_image(forest, inp)
kb = KernelBuilder()
kb.build_kernel(forest.height, len(forest.values), len(inp.indices), rounds)
# print(kb.instrs)
value_trace = {}
machine = Machine(
mem,
kb.instrs,
kb.debug_info(),
n_cores=N_CORES,
value_trace=value_trace,
trace=trace,
)
machine.prints = prints
for i, ref_mem in enumerate(reference_kernel2(mem, value_trace)):
machine.run()
inp_values_p = ref_mem[6]
if prints:
print(machine.mem[inp_values_p : inp_values_p + len(inp.values)])
print(ref_mem[inp_values_p : inp_values_p + len(inp.values)])
assert (
machine.mem[inp_values_p : inp_values_p + len(inp.values)]
== ref_mem[inp_values_p : inp_values_p + len(inp.values)]
), f"Incorrect result on round {i}"
inp_indices_p = ref_mem[5]
if prints:
print(machine.mem[inp_indices_p : inp_indices_p + len(inp.indices)])
print(ref_mem[inp_indices_p : inp_indices_p + len(inp.indices)])
# Updating these in memory isn't required, but you can enable this check for debugging
# assert machine.mem[inp_indices_p:inp_indices_p+len(inp.indices)] == ref_mem[inp_indices_p:inp_indices_p+len(inp.indices)]
print("CYCLES: ", machine.cycle)
print("Speedup over baseline: ", BASELINE / machine.cycle)
return machine.cycle
class Tests(unittest.TestCase):
def test_ref_kernels(self):
"""
Test the reference kernels against each other
"""
random.seed(123)
for i in range(10):
f = Tree.generate(4)
inp = Input.generate(f, 10, 6)
mem = build_mem_image(f, inp)
reference_kernel(f, inp)
for _ in reference_kernel2(mem, {}):
pass
assert inp.indices == mem[mem[5] : mem[5] + len(inp.indices)]
assert inp.values == mem[mem[6] : mem[6] + len(inp.values)]
def test_kernel_trace(self):
# Full-scale example for performance testing
do_kernel_test(10, 16, 256, trace=True, prints=False)
# Passing this test is not required for submission, see submission_tests.py for the actual correctness test
# You can uncomment this if you think it might help you debug
# def test_kernel_correctness(self):
# for batch in range(1, 3):
# for forest_height in range(3):
# do_kernel_test(
# forest_height + 2, forest_height + 4, batch * 16 * VLEN * N_CORES
# )
def test_kernel_cycles(self):
do_kernel_test(10, 16, 256)
# To run all the tests:
# python perf_takehome.py
# To run a specific test:
# python perf_takehome.py Tests.test_kernel_cycles
# To view a hot-reloading trace of all the instructions: **Recommended debug loop**
# NOTE: The trace hot-reloading only works in Chrome. In the worst case if things aren't working, drag trace.json onto https://ui.perfetto.dev/
# python perf_takehome.py Tests.test_kernel_trace
# Then run `python watch_trace.py` in another tab, it'll open a browser tab, then click "Open Perfetto"
# You can then keep that open and re-run the test to see a new trace.
# To run the proper checks to see which thresholds you pass:
# python tests/submission_tests.py
if __name__ == "__main__":
unittest.main()
Raw
checkpoint_717c5f316c1262bc@v5
"""
# Anthropic's Original Performance Engineering Take-home (Release version)
Copyright Anthropic PBC 2026. Permission is granted to modify and use, but not
to publish or redistribute your solutions so it's hard to find spoilers.
# Task
- Optimize the kernel (in KernelBuilder.build_kernel) as much as possible in the
available time, as measured by test_kernel_cycles on a frozen separate copy
of the simulator.
Validate your results using `python tests/submission_tests.py` without modifying
anything in the tests/ folder.
We recommend you look through problem.py next.
"""
from collections import defaultdict
import random
import unittest
from problem import (
Engine,
DebugInfo,
SLOT_LIMITS,
VLEN,
N_CORES,
SCRATCH_SIZE,
Machine,
Tree,
Input,
HASH_STAGES,
reference_kernel,
build_mem_image,
reference_kernel2,
)
class KernelBuilder:
def __init__(self):
self.instrs = []
self.scratch = {}
self.scratch_debug = {}
self.scratch_ptr = 0
self.const_map = {}
self.vec_const_map = {}
def debug_info(self):
return DebugInfo(scratch_map=self.scratch_debug)
def get_rw_addresses(self, engine, slot):
reads, writes = set(), set()
if engine == "valu":
if slot[0] == "vbroadcast":
_, dest, src = slot
reads.add(src)
writes.update(range(dest, dest + VLEN))
elif slot[0] == "multiply_add":
_, dest, a, b, c = slot
reads.update(range(a, a + VLEN))
reads.update(range(b, b + VLEN))
reads.update(range(c, c + VLEN))
writes.update(range(dest, dest + VLEN))
else:
_, dest, a1, a2 = slot
reads.update(range(a1, a1 + VLEN))
reads.update(range(a2, a2 + VLEN))
writes.update(range(dest, dest + VLEN))
elif engine == "alu":
_, dest, a1, a2 = slot
reads.add(a1); reads.add(a2); writes.add(dest)
elif engine == "load":
if slot[0] == "const":
writes.add(slot[1])
elif slot[0] == "load":
reads.add(slot[2]); writes.add(slot[1])
elif slot[0] == "vload":
reads.add(slot[2]); writes.update(range(slot[1], slot[1] + VLEN))
elif engine == "store":
if slot[0] == "store":
reads.add(slot[1]); reads.add(slot[2])
elif slot[0] == "vstore":
reads.add(slot[1]); reads.update(range(slot[2], slot[2] + VLEN))
return reads, writes
def build_vliw(self, ops):
if not ops: return []
n = len(ops)
writes_by_op, reads_by_op, deps = {}, {}, defaultdict(set)
for i, (engine, slot) in enumerate(ops):
reads, writes = self.get_rw_addresses(engine, slot)
reads_by_op[i], writes_by_op[i] = reads, writes
for j in range(i):
if writes_by_op[j] & reads: deps[i].add(j)
if writes_by_op[j] & writes: deps[i].add(j)
if reads_by_op[j] & writes: deps[i].add(j)
scheduled, done = [], set()
while len(done) < n:
bundle, slots_used, to_add = defaultdict(list), defaultdict(int), []
for i in range(n):
if i in done or not all(d in done for d in deps[i]): continue
engine, slot = ops[i]
if slots_used[engine] >= SLOT_LIMITS.get(engine, 1): continue
bundle[engine].append(slot)
slots_used[engine] += 1
to_add.append(i)
if not to_add: break
scheduled.append(dict(bundle))
done.update(to_add)
return scheduled
def add(self, engine, slot):
self.instrs.append({engine: [slot]})
def alloc_scratch(self, name=None, length=1):
addr = self.scratch_ptr
if name is not None:
self.scratch[name] = addr
self.scratch_debug[addr] = (name, length)
self.scratch_ptr += length
assert self.scratch_ptr <= SCRATCH_SIZE
return addr
def scratch_const(self, val, name=None):
if val not in self.const_map:
self.const_map[val] = self.alloc_scratch(name)
return self.const_map[val]
def vec_const(self, val, name=None):
if val not in self.vec_const_map:
self.scratch_const(val)
self.vec_const_map[val] = self.alloc_scratch(name, VLEN)
return self.vec_const_map[val]
def build_kernel(self, forest_height: int, n_nodes: int, batch_size: int, rounds: int):
n_groups = batch_size // VLEN
init_vars = ["rounds", "n_nodes", "batch_size", "forest_height", "forest_values_p", "inp_indices_p", "inp_values_p"]
header_addrs = [self.alloc_scratch(f"ha{i}") for i in range(len(init_vars))]
for v in init_vars: self.alloc_scratch(v)
init_ops = []
for i in range(len(init_vars)): init_ops.append(("load", ("const", header_addrs[i], i)))
for i, v in enumerate(init_vars): init_ops.append(("load", ("load", self.scratch[v], header_addrs[i])))
self.instrs.extend(self.build_vliw(init_ops))
const_ops = []
for val in [0, 1, 2]:
addr = self.alloc_scratch(f"c{val}")
self.const_map[val] = addr
const_ops.append(("load", ("const", addr, val)))
zero_v = self.alloc_scratch("zv", VLEN); one_v = self.alloc_scratch("ov", VLEN); two_v = self.alloc_scratch("tv", VLEN)
self.vec_const_map[0] = zero_v; self.vec_const_map[1] = one_v; self.vec_const_map[2] = two_v
const_ops.extend([("valu", ("vbroadcast", zero_v, self.const_map[0])), ("valu", ("vbroadcast", one_v, self.const_map[1])), ("valu", ("vbroadcast", two_v, self.const_map[2]))])
hash_c1, hash_c3, hash_mult = [], [], {}
for stage, (op1, c1, op2, op3, c3) in enumerate(HASH_STAGES):
c1a, c3a = self.alloc_scratch(f"hc1_{stage}", VLEN), self.alloc_scratch(f"hc3_{stage}", VLEN)
hash_c1.append(c1a); hash_c3.append(c3a)
c1s, c3s = self.scratch_const(c1), self.scratch_const(c3)
const_ops.extend([("load", ("const", c1s, c1)), ("load", ("const", c3s, c3)), ("valu", ("vbroadcast", c1a, c1s)), ("valu", ("vbroadcast", c3a, c3s))])
if op1 == "+" and op2 == "+" and op3 == "<<":
mult = (1 << c3) + 1
ma = self.alloc_scratch(f"hm{stage}", VLEN)
ms = self.scratch_const(mult)
const_ops.extend([("load", ("const", ms, mult)), ("valu", ("vbroadcast", ma, ms))])
hash_mult[stage] = ma
n_nodes_v = self.alloc_scratch("nnv", VLEN)
const_ops.append(("valu", ("vbroadcast", n_nodes_v, self.scratch["n_nodes"])))
self.instrs.extend(self.build_vliw(const_ops))
groups = [{"idx": self.alloc_scratch(f"g{g}i", VLEN), "val": self.alloc_scratch(f"g{g}v", VLEN), "nv": self.alloc_scratch(f"g{g}n", VLEN), "t1": self.alloc_scratch(f"g{g}t", VLEN)} for g in range(n_groups)]
N_ADDR_BUFS = 16
addr_bufs = [self.alloc_scratch(f"ab{i}", VLEN) for i in range(N_ADDR_BUFS)]
# Broadcast nodes for levels 0, 1
bc_s0, bc_s1, bc_s2 = self.alloc_scratch("bs0"), self.alloc_scratch("bs1"), self.alloc_scratch("bs2")
bc_v1, bc_v2 = self.alloc_scratch("bv1", VLEN), self.alloc_scratch("bv2", VLEN)
lb1 = self.vec_const(1, "lb1")
load_ops = []
for g in range(n_groups):
offset = self.scratch_const(g * VLEN)
load_ops.append(("load", ("const", offset, g * VLEN)))
for g in range(n_groups):
gr, off, buf = groups[g], self.const_map[g * VLEN], addr_bufs[g % N_ADDR_BUFS]
load_ops.extend([("alu", ("+", buf, self.scratch["inp_indices_p"], off)), ("load", ("vload", gr["idx"], buf)), ("alu", ("+", buf, self.scratch["inp_values_p"], off)), ("load", ("vload", gr["val"], buf))])
self.instrs.extend(self.build_vliw(load_ops))
self.add("flow", ("pause",))
pre_ops = [("load", ("load", bc_s0, self.scratch["forest_values_p"]))]
c1, c2 = self.scratch_const(1), self.scratch_const(2)
pre_ops.extend([("alu", ("+", bc_s1, self.scratch["forest_values_p"], c1)), ("alu", ("+", bc_s2, self.scratch["forest_values_p"], c2)), ("load", ("load", bc_s1, bc_s1)), ("load", ("load", bc_s2, bc_s2)), ("valu", ("vbroadcast", bc_v1, bc_s1)), ("valu", ("vbroadcast", bc_v2, bc_s2))])
self.instrs.extend(self.build_vliw(pre_ops))
def get_level(r): return r if r <= 10 else r - 11
g_round, g_phase, g_buf = [0]*n_groups, [0]*n_groups, [-1]*n_groups
buf_avail = [True]*N_ADDR_BUFS
all_ops = []
while any(r < rounds for r in g_round):
c_ops = []
bc_adv = 0
for g in range(n_groups):
if g_round[g] >= rounds: continue
rnd, lvl, gr = g_round[g], get_level(g_round[g]), groups[g]
last = rnd == rounds - 1
if g_phase[g] == 0:
if lvl <= 1:
if bc_adv < 4: g_phase[g] = 1; bc_adv += 1
else:
bi = next((i for i in range(N_ADDR_BUFS) if buf_avail[i]), -1)
if bi >= 0:
buf_avail[bi] = False; g_buf[g] = bi; buf = addr_bufs[bi]
for lane in range(VLEN): c_ops.append(("alu", ("+", buf + lane, self.scratch["forest_values_p"], gr["idx"] + lane)))
for lane in range(VLEN): c_ops.append(("load", ("load", gr["nv"] + lane, buf + lane)))
g_phase[g] = 1
elif g_phase[g] == 1:
if lvl == 0:
c_ops.append(("valu", ("vbroadcast", gr["nv"], bc_s0)))
elif lvl == 1:
c_ops.extend([("valu", ("-", gr["t1"], gr["idx"], lb1)), ("valu", ("-", gr["t1"], zero_v, gr["t1"])), ("valu", ("^", gr["nv"], bc_v1, bc_v2)), ("valu", ("&", gr["t1"], gr["nv"], gr["t1"])), ("valu", ("^", gr["nv"], bc_v1, gr["t1"]))])
c_ops.append(("valu", ("^", gr["val"], gr["val"], gr["nv"])))
for st, (o1, _, o2, o3, _) in enumerate(HASH_STAGES):
if st in hash_mult:
c_ops.append(("valu", ("multiply_add", gr["val"], gr["val"], hash_mult[st], hash_c1[st])))
else:
c_ops.extend([("valu", (o1, gr["t1"], gr["val"], hash_c1[st])), ("valu", (o3, gr["nv"], gr["val"], hash_c3[st])), ("valu", (o2, gr["val"], gr["t1"], gr["nv"]))])
if not last:
c_ops.extend([("valu", ("&", gr["t1"], gr["val"], one_v)), ("valu", ("multiply_add", gr["idx"], gr["idx"], two_v, one_v)), ("valu", ("+", gr["idx"], gr["idx"], gr["t1"]))])
if rnd == 10 and not last:
c_ops.extend([("valu", ("<", gr["t1"], gr["idx"], n_nodes_v)), ("valu", ("-", gr["nv"], zero_v, gr["t1"])), ("valu", ("&", gr["idx"], gr["idx"], gr["nv"]))])
if g_buf[g] >= 0: buf_avail[g_buf[g]] = True; g_buf[g] = -1
g_round[g] += 1; g_phase[g] = 0
if rnd == 10:
c_ops.extend([("valu", ("vbroadcast", bc_v1, bc_s1)), ("valu", ("vbroadcast", bc_v2, bc_s2))])
all_ops.extend(c_ops)
st_ops = []
for g in range(n_groups):
gr, off, buf = groups[g], self.const_map[g * VLEN], addr_bufs[g % N_ADDR_BUFS]
st_ops.extend([("alu", ("+", buf, self.scratch["inp_values_p"], off)), ("store", ("vstore", buf, gr["val"]))])
all_ops.extend(st_ops)
self.instrs.extend(self.build_vliw(all_ops))
self.instrs.append({"flow": [("pause",)]})
BASELINE = 147734
def do_kernel_test(
forest_height: int,
rounds: int,
batch_size: int,
seed: int = 123,
trace: bool = False,
prints: bool = False,
):
print(f"{forest_height=}, {rounds=}, {batch_size=}")
random.seed(seed)
forest = Tree.generate(forest_height)
inp = Input.generate(forest, batch_size, rounds)
mem = build_mem_image(forest, inp)
kb = KernelBuilder()
kb.build_kernel(forest.height, len(forest.values), len(inp.indices), rounds)
# print(kb.instrs)
value_trace = {}
machine = Machine(
mem,
kb.instrs,
kb.debug_info(),
n_cores=N_CORES,
value_trace=value_trace,
trace=trace,
)
machine.prints = prints
for i, ref_mem in enumerate(reference_kernel2(mem, value_trace)):
machine.run()
inp_values_p = ref_mem[6]
if prints:
print(machine.mem[inp_values_p : inp_values_p + len(inp.values)])
print(ref_mem[inp_values_p : inp_values_p + len(inp.values)])
assert (
machine.mem[inp_values_p : inp_values_p + len(inp.values)]
== ref_mem[inp_values_p : inp_values_p + len(inp.values)]
), f"Incorrect result on round {i}"
inp_indices_p = ref_mem[5]
if prints:
print(machine.mem[inp_indices_p : inp_indices_p + len(inp.indices)])
print(ref_mem[inp_indices_p : inp_indices_p + len(inp.indices)])
# Updating these in memory isn't required, but you can enable this check for debugging
# assert machine.mem[inp_indices_p:inp_indices_p+len(inp.indices)] == ref_mem[inp_indices_p:inp_indices_p+len(inp.indices)]
print("CYCLES: ", machine.cycle)
print("Speedup over baseline: ", BASELINE / machine.cycle)
return machine.cycle
class Tests(unittest.TestCase):
def test_ref_kernels(self):
"""
Test the reference kernels against each other
"""
random.seed(123)
for i in range(10):
f = Tree.generate(4)
inp = Input.generate(f, 10, 6)
mem = build_mem_image(f, inp)
reference_kernel(f, inp)
for _ in reference_kernel2(mem, {}):
pass
assert inp.indices == mem[mem[5] : mem[5] + len(inp.indices)]
assert inp.values == mem[mem[6] : mem[6] + len(inp.values)]
def test_kernel_trace(self):
# Full-scale example for performance testing
do_kernel_test(10, 16, 256, trace=True, prints=False)
# Passing this test is not required for submission, see submission_tests.py for the actual correctness test
# You can uncomment this if you think it might help you debug
# def test_kernel_correctness(self):
# for batch in range(1, 3):
# for forest_height in range(3):
# do_kernel_test(
# forest_height + 2, forest_height + 4, batch * 16 * VLEN * N_CORES
# )
def test_kernel_cycles(self):
do_kernel_test(10, 16, 256)
# To run all the tests:
# python perf_takehome.py
# To run a specific test:
# python perf_takehome.py Tests.test_kernel_cycles
# To view a hot-reloading trace of all the instructions: **Recommended debug loop**
# NOTE: The trace hot-reloading only works in Chrome. In the worst case if things aren't working, drag trace.json onto https://ui.perfetto.dev/
# python perf_takehome.py Tests.test_kernel_trace
# Then run `python watch_trace.py` in another tab, it'll open a browser tab, then click "Open Perfetto"
# You can then keep that open and re-run the test to see a new trace.
# To run the proper checks to see which thresholds you pass:
# python tests/submission_tests.py
if __name__ == "__main__":
unittest.main()
Raw
checkpoint_717c5f316c1262bc@v6
"""
# Anthropic's Original Performance Engineering Take-home (Release version)
Copyright Anthropic PBC 2026. Permission is granted to modify and use, but not
to publish or redistribute your solutions so it's hard to find spoilers.
# Task
- Optimize the kernel (in KernelBuilder.build_kernel) as much as possible in the
available time, as measured by test_kernel_cycles on a frozen separate copy
of the simulator.
Validate your results using `python tests/submission_tests.py` without modifying
anything in the tests/ folder.
We recommend you look through problem.py next.
"""
from collections import defaultdict
import random
import unittest
from problem import (
Engine,
DebugInfo,
SLOT_LIMITS,
VLEN,
N_CORES,
SCRATCH_SIZE,
Machine,
Tree,
Input,
HASH_STAGES,
reference_kernel,
build_mem_image,
reference_kernel2,
)
class KernelBuilder:
def __init__(self):
self.instrs = []
self.scratch = {}
self.scratch_debug = {}
self.scratch_ptr = 0
self.const_map = {}
self.vec_const_map = {}
def debug_info(self):
return DebugInfo(scratch_map=self.scratch_debug)
def get_rw_addresses(self, engine, slot):
reads, writes = set(), set()
if engine == "valu":
if slot[0] == "vbroadcast":
_, dest, src = slot
reads.add(src)
writes.update(range(dest, dest + VLEN))
elif slot[0] == "multiply_add":
_, dest, a, b, c = slot
reads.update(range(a, a + VLEN))
reads.update(range(b, b + VLEN))
reads.update(range(c, c + VLEN))
writes.update(range(dest, dest + VLEN))
else:
_, dest, a1, a2 = slot
reads.update(range(a1, a1 + VLEN))
reads.update(range(a2, a2 + VLEN))
writes.update(range(dest, dest + VLEN))
elif engine == "alu":
_, dest, a1, a2 = slot
reads.add(a1); reads.add(a2); writes.add(dest)
elif engine == "load":
if slot[0] == "const":
writes.add(slot[1])
elif slot[0] == "load":
reads.add(slot[2]); writes.add(slot[1])
elif slot[0] == "vload":
reads.add(slot[2]); writes.update(range(slot[1], slot[1] + VLEN))
elif engine == "store":
if slot[0] == "store":
reads.add(slot[1]); reads.add(slot[2])
elif slot[0] == "vstore":
reads.add(slot[1]); reads.update(range(slot[2], slot[2] + VLEN))
return reads, writes
def build_vliw(self, ops):
if not ops: return []
n = len(ops)
writes_by_op, reads_by_op, deps = {}, {}, defaultdict(set)
for i, (engine, slot) in enumerate(ops):
reads, writes = self.get_rw_addresses(engine, slot)
reads_by_op[i], writes_by_op[i] = reads, writes
for j in range(i):
if writes_by_op[j] & reads: deps[i].add(j)
if writes_by_op[j] & writes: deps[i].add(j)
if reads_by_op[j] & writes: deps[i].add(j)
scheduled, done = [], set()
while len(done) < n:
bundle, slots_used, to_add = defaultdict(list), defaultdict(int), []
for i in range(n):
if i in done or not all(d in done for d in deps[i]): continue
engine, slot = ops[i]
if slots_used[engine] >= SLOT_LIMITS.get(engine, 1): continue
bundle[engine].append(slot)
slots_used[engine] += 1
to_add.append(i)
if not to_add: break
scheduled.append(dict(bundle))
done.update(to_add)
return scheduled
def add(self, engine, slot):
self.instrs.append({engine: [slot]})
def alloc_scratch(self, name=None, length=1):
addr = self.scratch_ptr
if name is not None:
self.scratch[name] = addr
self.scratch_debug[addr] = (name, length)
self.scratch_ptr += length
assert self.scratch_ptr <= SCRATCH_SIZE
return addr
def scratch_const(self, val, name=None):
if val not in self.const_map:
self.const_map[val] = self.alloc_scratch(name)
return self.const_map[val]
def vec_const(self, val, name=None):
if val not in self.vec_const_map:
self.scratch_const(val)
self.vec_const_map[val] = self.alloc_scratch(name, VLEN)
return self.vec_const_map[val]
def build_kernel(self, forest_height: int, n_nodes: int, batch_size: int, rounds: int):
n_groups = batch_size // VLEN
init_vars = ["rounds", "n_nodes", "batch_size", "forest_height", "forest_values_p", "inp_indices_p", "inp_values_p"]
header_addrs = [self.alloc_scratch(f"ha{i}") for i in range(len(init_vars))]
for v in init_vars: self.alloc_scratch(v)
init_ops = []
for i in range(len(init_vars)): init_ops.append(("load", ("const", header_addrs[i], i)))
for i, v in enumerate(init_vars): init_ops.append(("load", ("load", self.scratch[v], header_addrs[i])))
self.instrs.extend(self.build_vliw(init_ops))
const_ops = []
for val in [0, 1, 2]:
addr = self.alloc_scratch(f"c{val}")
self.const_map[val] = addr
const_ops.append(("load", ("const", addr, val)))
zero_v = self.alloc_scratch("zv", VLEN); one_v = self.alloc_scratch("ov", VLEN); two_v = self.alloc_scratch("tv", VLEN)
self.vec_const_map[0] = zero_v; self.vec_const_map[1] = one_v; self.vec_const_map[2] = two_v
const_ops.extend([("valu", ("vbroadcast", zero_v, self.const_map[0])), ("valu", ("vbroadcast", one_v, self.const_map[1])), ("valu", ("vbroadcast", two_v, self.const_map[2]))])
hash_c1, hash_c3, hash_mult = [], [], {}
for stage, (op1, c1, op2, op3, c3) in enumerate(HASH_STAGES):
c1a, c3a = self.alloc_scratch(f"hc1_{stage}", VLEN), self.alloc_scratch(f"hc3_{stage}", VLEN)
hash_c1.append(c1a); hash_c3.append(c3a)
c1s, c3s = self.scratch_const(c1), self.scratch_const(c3)
const_ops.extend([("load", ("const", c1s, c1)), ("load", ("const", c3s, c3)), ("valu", ("vbroadcast", c1a, c1s)), ("valu", ("vbroadcast", c3a, c3s))])
if op1 == "+" and op2 == "+" and op3 == "<<":
mult = (1 << c3) + 1
ma = self.alloc_scratch(f"hm{stage}", VLEN)
ms = self.scratch_const(mult)
const_ops.extend([("load", ("const", ms, mult)), ("valu", ("vbroadcast", ma, ms))])
hash_mult[stage] = ma
n_nodes_v = self.alloc_scratch("nnv", VLEN)
const_ops.append(("valu", ("vbroadcast", n_nodes_v, self.scratch["n_nodes"])))
self.instrs.extend(self.build_vliw(const_ops))
groups = [{"idx": self.alloc_scratch(f"g{g}i", VLEN), "val": self.alloc_scratch(f"g{g}v", VLEN), "nv": self.alloc_scratch(f"g{g}n", VLEN), "t1": self.alloc_scratch(f"g{g}t", VLEN)} for g in range(n_groups)]
N_ADDR_BUFS = 16
addr_bufs = [self.alloc_scratch(f"ab{i}", VLEN) for i in range(N_ADDR_BUFS)]
# Broadcast nodes for levels 0, 1, 2
bc_s0, bc_s1, bc_s2 = self.alloc_scratch("bs0"), self.alloc_scratch("bs1"), self.alloc_scratch("bs2")
bc_v1, bc_v2 = self.alloc_scratch("bv1", VLEN), self.alloc_scratch("bv2", VLEN)
# L2 nodes 3,4,5,6
bc_v3 = self.alloc_scratch("bv3", VLEN)
bc_v4 = self.alloc_scratch("bv4", VLEN)
bc_v5 = self.alloc_scratch("bv5", VLEN)
bc_v6 = self.alloc_scratch("bv6", VLEN)
three_v = self.alloc_scratch("thrv", VLEN)
l2_tmp = self.alloc_scratch("l2t") # scalar temp for loading
lb1 = self.vec_const(1, "lb1")
load_ops = []
for g in range(n_groups):
offset = self.scratch_const(g * VLEN)
load_ops.append(("load", ("const", offset, g * VLEN)))
for g in range(n_groups):
gr, off, buf = groups[g], self.const_map[g * VLEN], addr_bufs[g % N_ADDR_BUFS]
load_ops.extend([("alu", ("+", buf, self.scratch["inp_indices_p"], off)), ("load", ("vload", gr["idx"], buf)), ("alu", ("+", buf, self.scratch["inp_values_p"], off)), ("load", ("vload", gr["val"], buf))])
self.instrs.extend(self.build_vliw(load_ops))
self.add("flow", ("pause",))
pre_ops = [("load", ("load", bc_s0, self.scratch["forest_values_p"]))]
c1, c2 = self.scratch_const(1), self.scratch_const(2)
pre_ops.extend([("alu", ("+", bc_s1, self.scratch["forest_values_p"], c1)), ("alu", ("+", bc_s2, self.scratch["forest_values_p"], c2)), ("load", ("load", bc_s1, bc_s1)), ("load", ("load", bc_s2, bc_s2)), ("valu", ("vbroadcast", bc_v1, bc_s1)), ("valu", ("vbroadcast", bc_v2, bc_s2))])
# Load three_v constant for L2 selection
c3 = self.scratch_const(3)
pre_ops.extend([("load", ("const", c3, 3)), ("valu", ("vbroadcast", three_v, c3))])
# Load L2 nodes 3,4,5,6
for i, bv in enumerate([bc_v3, bc_v4, bc_v5, bc_v6]):
ci = self.scratch_const(3 + i)
pre_ops.extend([("load", ("const", ci, 3 + i)), ("alu", ("+", l2_tmp, self.scratch["forest_values_p"], ci)), ("load", ("load", l2_tmp, l2_tmp)), ("valu", ("vbroadcast", bv, l2_tmp))])
self.instrs.extend(self.build_vliw(pre_ops))
def get_level(r): return r if r <= 10 else r - 11
g_round, g_phase, g_buf = [0]*n_groups, [0]*n_groups, [-1]*n_groups
buf_avail = [True]*N_ADDR_BUFS
all_ops = []
while any(r < rounds for r in g_round):
c_ops = []
bc_adv = 0
for g in range(n_groups):
if g_round[g] >= rounds: continue
rnd, lvl, gr = g_round[g], get_level(g_round[g]), groups[g]
last = rnd == rounds - 1
if g_phase[g] == 0:
if lvl <= 2:
if bc_adv < 4: g_phase[g] = 1; bc_adv += 1
else:
bi = next((i for i in range(N_ADDR_BUFS) if buf_avail[i]), -1)
if bi >= 0:
buf_avail[bi] = False; g_buf[g] = bi; buf = addr_bufs[bi]
for lane in range(VLEN): c_ops.append(("alu", ("+", buf + lane, self.scratch["forest_values_p"], gr["idx"] + lane)))
for lane in range(VLEN): c_ops.append(("load", ("load", gr["nv"] + lane, buf + lane)))
g_phase[g] = 1
elif g_phase[g] == 1:
if lvl == 0:
c_ops.append(("valu", ("vbroadcast", gr["nv"], bc_s0)))
elif lvl == 1:
c_ops.extend([("valu", ("-", gr["t1"], gr["idx"], lb1)), ("valu", ("-", gr["t1"], zero_v, gr["t1"])), ("valu", ("^", gr["nv"], bc_v1, bc_v2)), ("valu", ("&", gr["t1"], gr["nv"], gr["t1"])), ("valu", ("^", gr["nv"], bc_v1, gr["t1"]))])
elif lvl == 2:
# 4-way selection from bc_v3,bc_v4,bc_v5,bc_v6 based on idx in [3,4,5,6]
# offset = idx - 3, bit1 = offset >> 1, bit0 = offset & 1
c_ops.extend([
("valu", ("-", gr["t1"], gr["idx"], three_v)), # t1 = offset
("valu", (">>", gr["nv"], gr["t1"], one_v)), # nv = bit1
("valu", ("-", gr["nv"], zero_v, gr["nv"])), # nv = mask1
("valu", ("^", gr["t2"], bc_v3, bc_v5)), # t2 = v3^v5
("valu", ("&", gr["t2"], gr["t2"], gr["nv"])), # t2 &= mask1
("valu", ("^", gr["t2"], bc_v3, gr["t2"])), # t2 = sel02
("valu", ("^", gr["t1"], bc_v4, bc_v6)), # t1 = v4^v6
("valu", ("&", gr["t1"], gr["t1"], gr["nv"])), # t1 &= mask1
("valu", ("^", gr["nv"], bc_v4, gr["t1"])), # nv = sel13
("valu", ("^", gr["t1"], gr["t2"], gr["nv"])), # t1 = sel02^sel13
("valu", ("-", gr["nv"], gr["idx"], three_v)), # nv = offset (recompute)
("valu", ("&", gr["nv"], gr["nv"], one_v)), # nv = bit0
("valu", ("-", gr["nv"], zero_v, gr["nv"])), # nv = mask0
("valu", ("&", gr["t1"], gr["t1"], gr["nv"])), # t1 = masked
("valu", ("^", gr["nv"], gr["t2"], gr["t1"])), # nv = result
])
c_ops.append(("valu", ("^", gr["val"], gr["val"], gr["nv"])))
for st, (o1, _, o2, o3, _) in enumerate(HASH_STAGES):
if st in hash_mult:
c_ops.append(("valu", ("multiply_add", gr["val"], gr["val"], hash_mult[st], hash_c1[st])))
else:
c_ops.extend([("valu", (o1, gr["t1"], gr["val"], hash_c1[st])), ("valu", (o3, gr["nv"], gr["val"], hash_c3[st])), ("valu", (o2, gr["val"], gr["t1"], gr["nv"]))])
if not last:
c_ops.extend([("valu", ("&", gr["t1"], gr["val"], one_v)), ("valu", ("multiply_add", gr["idx"], gr["idx"], two_v, one_v)), ("valu", ("+", gr["idx"], gr["idx"], gr["t1"]))])
if rnd == 10 and not last:
c_ops.extend([("valu", ("<", gr["t1"], gr["idx"], n_nodes_v)), ("valu", ("-", gr["nv"], zero_v, gr["t1"])), ("valu", ("&", gr["idx"], gr["idx"], gr["nv"]))])
if g_buf[g] >= 0: buf_avail[g_buf[g]] = True; g_buf[g] = -1
g_round[g] += 1; g_phase[g] = 0
if rnd == 10:
c_ops.extend([("valu", ("vbroadcast", bc_v1, bc_s1)), ("valu", ("vbroadcast", bc_v2, bc_s2))])
all_ops.extend(c_ops)
st_ops = []
for g in range(n_groups):
gr, off, buf = groups[g], self.const_map[g * VLEN], addr_bufs[g % N_ADDR_BUFS]
st_ops.extend([("alu", ("+", buf, self.scratch["inp_values_p"], off)), ("store", ("vstore", buf, gr["val"]))])
all_ops.extend(st_ops)
self.instrs.extend(self.build_vliw(all_ops))
self.instrs.append({"flow": [("pause",)]})
BASELINE = 147734
def do_kernel_test(
forest_height: int,
rounds: int,
batch_size: int,
seed: int = 123,
trace: bool = False,
prints: bool = False,
):
print(f"{forest_height=}, {rounds=}, {batch_size=}")
random.seed(seed)
forest = Tree.generate(forest_height)
inp = Input.generate(forest, batch_size, rounds)
mem = build_mem_image(forest, inp)
kb = KernelBuilder()
kb.build_kernel(forest.height, len(forest.values), len(inp.indices), rounds)
# print(kb.instrs)
value_trace = {}
machine = Machine(
mem,
kb.instrs,
kb.debug_info(),
n_cores=N_CORES,
value_trace=value_trace,
trace=trace,
)
machine.prints = prints
for i, ref_mem in enumerate(reference_kernel2(mem, value_trace)):
machine.run()
inp_values_p = ref_mem[6]
if prints:
print(machine.mem[inp_values_p : inp_values_p + len(inp.values)])
print(ref_mem[inp_values_p : inp_values_p + len(inp.values)])
assert (
machine.mem[inp_values_p : inp_values_p + len(inp.values)]
== ref_mem[inp_values_p : inp_values_p + len(inp.values)]
), f"Incorrect result on round {i}"
inp_indices_p = ref_mem[5]
if prints:
print(machine.mem[inp_indices_p : inp_indices_p + len(inp.indices)])
print(ref_mem[inp_indices_p : inp_indices_p + len(inp.indices)])
# Updating these in memory isn't required, but you can enable this check for debugging
# assert machine.mem[inp_indices_p:inp_indices_p+len(inp.indices)] == ref_mem[inp_indices_p:inp_indices_p+len(inp.indices)]
print("CYCLES: ", machine.cycle)
print("Speedup over baseline: ", BASELINE / machine.cycle)
return machine.cycle
class Tests(unittest.TestCase):
def test_ref_kernels(self):
"""
Test the reference kernels against each other
"""
random.seed(123)
for i in range(10):
f = Tree.generate(4)
inp = Input.generate(f, 10, 6)
mem = build_mem_image(f, inp)
reference_kernel(f, inp)
for _ in reference_kernel2(mem, {}):
pass
assert inp.indices == mem[mem[5] : mem[5] + len(inp.indices)]
assert inp.values == mem[mem[6] : mem[6] + len(inp.values)]
def test_kernel_trace(self):
# Full-scale example for performance testing
do_kernel_test(10, 16, 256, trace=True, prints=False)
# Passing this test is not required for submission, see submission_tests.py for the actual correctness test
# You can uncomment this if you think it might help you debug
# def test_kernel_correctness(self):
# for batch in range(1, 3):
# for forest_height in range(3):
# do_kernel_test(
# forest_height + 2, forest_height + 4, batch * 16 * VLEN * N_CORES
# )
def test_kernel_cycles(self):
do_kernel_test(10, 16, 256)
# To run all the tests:
# python perf_takehome.py
# To run a specific test:
# python perf_takehome.py Tests.test_kernel_cycles
# To view a hot-reloading trace of all the instructions: **Recommended debug loop**
# NOTE: The trace hot-reloading only works in Chrome. In the worst case if things aren't working, drag trace.json onto https://ui.perfetto.dev/
# python perf_takehome.py Tests.test_kernel_trace
# Then run `python watch_trace.py` in another tab, it'll open a browser tab, then click "Open Perfetto"
# You can then keep that open and re-run the test to see a new trace.
# To run the proper checks to see which thresholds you pass:
# python tests/submission_tests.py
if __name__ == "__main__":
unittest.main()
Raw
import.sh
#!/bin/bash
# Import this Claude session: curl -sL <raw_url> | bash
if ! command -v uvx &> /dev/null; then
echo "Installing uv..."
curl -LsSf https://astral.sh/uv/install.sh | sh
export PATH="$HOME/.local/bin:$PATH"
fi
uvx one_claude gist import 84b4f2b04a430d3ed2107f1aea2e7e6f
Raw
session.json
{
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"export_type": "from_message",
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"exporter": "one_claude",
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"id": "31776356-e2fa-4177-9ee2-23fbcfc6bd25",
"project_path": "/home/tato/Desktop/original_performance_takehome",
"title": "Implement the following plan: # Global Wave Pipelining - Racing Across Round Boundaries ## Problem Summary - Current: 2128 cycles - Target: **< 1487 cycles** (opus45_11hr threshold) - Stretch: **< ...",
"created_at": "2026-01-25T06:02:41.656000+00:00",
"updated_at": "2026-01-25T14:48:16.613000+00:00"
},
"git_info": {
"branch": "main",
"commit": "5452f74bd977807ac2e74f3d29432b9df6f25197",
"remote": "git@github.com:anthropics/original_performance_takehome.git",
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"origin": "git@github.com:anthropics/original_performance_takehome.git"
}
},
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"slug": "toasty-wibbling-treehouse",
"message": {
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"stop_reason": null,
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"usage": {
"input_tokens": 8,
"cache_creation_input_tokens": 1943,
"cache_read_input_tokens": 99305,
"cache_creation": {
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"ephemeral_1h_input_tokens": 0
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"userType": "external",
"cwd": "/home/tato/Desktop/original_performance_takehome",
"sessionId": "31776356-e2fa-4177-9ee2-23fbcfc6bd25",
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"content": [
{
"type": "tool_use",
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"name": "Edit",
"input": {
"replace_all": false,
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"old_string": " elif lvl == 1:\n c_ops.extend([(\"valu\", (\"-\", gr[\"t1\"], gr[\"idx\"], lb1)), (\"valu\", (\"-\", gr[\"t1\"], zero_v, gr[\"t1\"])), (\"valu\", (\"^\", gr[\"nv\"], bc_v1, bc_v2)), (\"valu\", (\"&\", gr[\"t1\"], gr[\"nv\"], gr[\"t1\"])), (\"valu\", (\"^\", gr[\"nv\"], bc_v1, gr[\"t1\"]))])",
"new_string": " elif lvl == 1:\n c_ops.extend([(\"valu\", (\"-\", gr[\"t1\"], gr[\"idx\"], lb1)), (\"valu\", (\"-\", gr[\"t1\"], zero_v, gr[\"t1\"])), (\"valu\", (\"^\", gr[\"nv\"], bc_v1, bc_v2)), (\"valu\", (\"&\", gr[\"t1\"], gr[\"nv\"], gr[\"t1\"])), (\"valu\", (\"^\", gr[\"nv\"], bc_v1, gr[\"t1\"]))])\n elif lvl == 2:\n # L2: 4-way selection. idx in {3,4,5,6}\n # bit0 = idx&1: 3→1, 4→0, 5→1, 6→0\n # sel_low = v4 if bit0=0, v3 if bit0=1\n # sel_high = v6 if bit0=0, v5 if bit0=1\n # result = sel_low if idx<5, else sel_high\n c_ops.extend([\n (\"valu\", (\"&\", gr[\"t1\"], gr[\"idx\"], one_v)), # t1 = bit0\n (\"valu\", (\"-\", gr[\"t1\"], zero_v, gr[\"t1\"])), # t1 = mask0 (-1 when odd)\n (\"valu\", (\"^\", gr[\"nv\"], bc_v4, bc_v3)), # nv = v4^v3\n (\"valu\", (\"&\", gr[\"nv\"], gr[\"nv\"], gr[\"t1\"])), # nv &= mask0\n (\"valu\", (\"^\", gr[\"nv\"], bc_v4, gr[\"nv\"])), # nv = sel_low\n (\"valu\", (\"^\", sel_tmp, bc_v6, bc_v5)), # sel_tmp = v6^v5\n (\"valu\", (\"&\", sel_tmp, sel_tmp, gr[\"t1\"])), # sel_tmp &= mask0\n (\"valu\", (\"^\", sel_tmp, bc_v6, sel_tmp)), # sel_tmp = sel_high\n (\"valu\", (\"<\", gr[\"t1\"], gr[\"idx\"], five_v)), # t1 = blend_mask (-1 when idx<5)\n (\"valu\", (\"^\", gr[\"nv\"], gr[\"nv\"], sel_tmp)), # nv = sel_low ^ sel_high\n (\"valu\", (\"&\", gr[\"nv\"], gr[\"nv\"], gr[\"t1\"])), # nv &= blend_mask\n (\"valu\", (\"^\", gr[\"nv\"], gr[\"nv\"], sel_tmp)), # nv = result\n ])"
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"oldString": " elif lvl == 1:\n c_ops.extend([(\"valu\", (\"-\", gr[\"t1\"], gr[\"idx\"], lb1)), (\"valu\", (\"-\", gr[\"t1\"], zero_v, gr[\"t1\"])), (\"valu\", (\"^\", gr[\"nv\"], bc_v1, bc_v2)), (\"valu\", (\"&\", gr[\"t1\"], gr[\"nv\"], gr[\"t1\"])), (\"valu\", (\"^\", gr[\"nv\"], bc_v1, gr[\"t1\"]))])",
"newString": " elif lvl == 1:\n c_ops.extend([(\"valu\", (\"-\", gr[\"t1\"], gr[\"idx\"], lb1)), (\"valu\", (\"-\", gr[\"t1\"], zero_v, gr[\"t1\"])), (\"valu\", (\"^\", gr[\"nv\"], bc_v1, bc_v2)), (\"valu\", (\"&\", gr[\"t1\"], gr[\"nv\"], gr[\"t1\"])), (\"valu\", (\"^\", gr[\"nv\"], bc_v1, gr[\"t1\"]))])\n elif lvl == 2:\n # L2: 4-way selection. idx in {3,4,5,6}\n # bit0 = idx&1: 3→1, 4→0, 5→1, 6→0\n # sel_low = v4 if bit0=0, v3 if bit0=1\n # sel_high = v6 if bit0=0, v5 if bit0=1\n # result = sel_low if idx<5, else sel_high\n c_ops.extend([\n (\"valu\", (\"&\", gr[\"t1\"], gr[\"idx\"], one_v)), # t1 = bit0\n (\"valu\", (\"-\", gr[\"t1\"], zero_v, gr[\"t1\"])), # t1 = mask0 (-1 when odd)\n (\"valu\", (\"^\", gr[\"nv\"], bc_v4, bc_v3)), # nv = v4^v3\n (\"valu\", (\"&\", gr[\"nv\"], gr[\"nv\"], gr[\"t1\"])), # nv &= mask0\n (\"valu\", (\"^\", gr[\"nv\"], bc_v4, gr[\"nv\"])), # nv = sel_low\n (\"valu\", (\"^\", sel_tmp, bc_v6, bc_v5)), # sel_tmp = v6^v5\n (\"valu\", (\"&\", sel_tmp, sel_tmp, gr[\"t1\"])), # sel_tmp &= mask0\n (\"valu\", (\"^\", sel_tmp, bc_v6, sel_tmp)), # sel_tmp = sel_high\n (\"valu\", (\"<\", gr[\"t1\"], gr[\"idx\"], five_v)), # t1 = blend_mask (-1 when idx<5)\n (\"valu\", (\"^\", gr[\"nv\"], gr[\"nv\"], sel_tmp)), # nv = sel_low ^ sel_high\n (\"valu\", (\"&\", gr[\"nv\"], gr[\"nv\"], gr[\"t1\"])), # nv &= blend_mask\n (\"valu\", (\"^\", gr[\"nv\"], gr[\"nv\"], sel_tmp)), # nv = result\n ])",
"originalFile": "\"\"\"\n# Anthropic's Original Performance Engineering Take-home (Release version)\n\nCopyright Anthropic PBC 2026. Permission is granted to modify and use, but not\nto publish or redistribute your solutions so it's hard to find spoilers.\n\n# Task\n\n- Optimize the kernel (in KernelBuilder.build_kernel) as much as possible in the\n available time, as measured by test_kernel_cycles on a frozen separate copy\n of the simulator.\n\nValidate your results using `python tests/submission_tests.py` without modifying\nanything in the tests/ folder.\n\nWe recommend you look through problem.py next.\n\"\"\"\n\nfrom collections import defaultdict\nimport random\nimport unittest\n\nfrom problem import (\n Engine,\n DebugInfo,\n SLOT_LIMITS,\n VLEN,\n N_CORES,\n SCRATCH_SIZE,\n Machine,\n Tree,\n Input,\n HASH_STAGES,\n reference_kernel,\n build_mem_image,\n reference_kernel2,\n)\n\n\nclass KernelBuilder:\n def __init__(self):\n self.instrs = []\n self.scratch = {}\n self.scratch_debug = {}\n self.scratch_ptr = 0\n self.const_map = {}\n self.vec_const_map = {}\n\n def debug_info(self):\n return DebugInfo(scratch_map=self.scratch_debug)\n\n def get_rw_addresses(self, engine, slot):\n reads, writes = set(), set()\n if engine == \"valu\":\n if slot[0] == \"vbroadcast\":\n _, dest, src = slot\n reads.add(src)\n writes.update(range(dest, dest + VLEN))\n elif slot[0] == \"multiply_add\":\n _, dest, a, b, c = slot\n reads.update(range(a, a + VLEN))\n reads.update(range(b, b + VLEN))\n reads.update(range(c, c + VLEN))\n writes.update(range(dest, dest + VLEN))\n else:\n _, dest, a1, a2 = slot\n reads.update(range(a1, a1 + VLEN))\n reads.update(range(a2, a2 + VLEN))\n writes.update(range(dest, dest + VLEN))\n elif engine == \"alu\":\n _, dest, a1, a2 = slot\n reads.add(a1); reads.add(a2); writes.add(dest)\n elif engine == \"load\":\n if slot[0] == \"const\":\n writes.add(slot[1])\n elif slot[0] == \"load\":\n reads.add(slot[2]); writes.add(slot[1])\n elif slot[0] == \"vload\":\n reads.add(slot[2]); writes.update(range(slot[1], slot[1] + VLEN))\n elif engine == \"store\":\n if slot[0] == \"store\":\n reads.add(slot[1]); reads.add(slot[2])\n elif slot[0] == \"vstore\":\n reads.add(slot[1]); reads.update(range(slot[2], slot[2] + VLEN))\n return reads, writes\n\n def build_vliw(self, ops):\n if not ops: return []\n n = len(ops)\n writes_by_op, reads_by_op, deps = {}, {}, defaultdict(set)\n for i, (engine, slot) in enumerate(ops):\n reads, writes = self.get_rw_addresses(engine, slot)\n reads_by_op[i], writes_by_op[i] = reads, writes\n for j in range(i):\n if writes_by_op[j] & reads: deps[i].add(j)\n if writes_by_op[j] & writes: deps[i].add(j)\n if reads_by_op[j] & writes: deps[i].add(j)\n scheduled, done = [], set()\n while len(done) < n:\n bundle, slots_used, to_add = defaultdict(list), defaultdict(int), []\n for i in range(n):\n if i in done or not all(d in done for d in deps[i]): continue\n engine, slot = ops[i]\n if slots_used[engine] >= SLOT_LIMITS.get(engine, 1): continue\n bundle[engine].append(slot)\n slots_used[engine] += 1\n to_add.append(i)\n if not to_add: break\n scheduled.append(dict(bundle))\n done.update(to_add)\n return scheduled\n\n def add(self, engine, slot):\n self.instrs.append({engine: [slot]})\n\n def alloc_scratch(self, name=None, length=1):\n addr = self.scratch_ptr\n if name is not None:\n self.scratch[name] = addr\n self.scratch_debug[addr] = (name, length)\n self.scratch_ptr += length\n assert self.scratch_ptr <= SCRATCH_SIZE\n return addr\n\n def scratch_const(self, val, name=None):\n if val not in self.const_map:\n self.const_map[val] = self.alloc_scratch(name)\n return self.const_map[val]\n\n def vec_const(self, val, name=None):\n if val not in self.vec_const_map:\n self.scratch_const(val)\n self.vec_const_map[val] = self.alloc_scratch(name, VLEN)\n return self.vec_const_map[val]\n\n def build_kernel(self, forest_height: int, n_nodes: int, batch_size: int, rounds: int):\n n_groups = batch_size // VLEN\n init_vars = [\"rounds\", \"n_nodes\", \"batch_size\", \"forest_height\", \"forest_values_p\", \"inp_indices_p\", \"inp_values_p\"]\n header_addrs = [self.alloc_scratch(f\"ha{i}\") for i in range(len(init_vars))]\n for v in init_vars: self.alloc_scratch(v)\n init_ops = []\n for i in range(len(init_vars)): init_ops.append((\"load\", (\"const\", header_addrs[i], i)))\n for i, v in enumerate(init_vars): init_ops.append((\"load\", (\"load\", self.scratch[v], header_addrs[i])))\n self.instrs.extend(self.build_vliw(init_ops))\n\n const_ops = []\n for val in [0, 1, 2]:\n addr = self.alloc_scratch(f\"c{val}\")\n self.const_map[val] = addr\n const_ops.append((\"load\", (\"const\", addr, val)))\n zero_v = self.alloc_scratch(\"zv\", VLEN); one_v = self.alloc_scratch(\"ov\", VLEN); two_v = self.alloc_scratch(\"tv\", VLEN)\n self.vec_const_map[0] = zero_v; self.vec_const_map[1] = one_v; self.vec_const_map[2] = two_v\n const_ops.extend([(\"valu\", (\"vbroadcast\", zero_v, self.const_map[0])), (\"valu\", (\"vbroadcast\", one_v, self.const_map[1])), (\"valu\", (\"vbroadcast\", two_v, self.const_map[2]))])\n\n hash_c1, hash_c3, hash_mult = [], [], {}\n for stage, (op1, c1, op2, op3, c3) in enumerate(HASH_STAGES):\n c1a, c3a = self.alloc_scratch(f\"hc1_{stage}\", VLEN), self.alloc_scratch(f\"hc3_{stage}\", VLEN)\n hash_c1.append(c1a); hash_c3.append(c3a)\n c1s, c3s = self.scratch_const(c1), self.scratch_const(c3)\n const_ops.extend([(\"load\", (\"const\", c1s, c1)), (\"load\", (\"const\", c3s, c3)), (\"valu\", (\"vbroadcast\", c1a, c1s)), (\"valu\", (\"vbroadcast\", c3a, c3s))])\n if op1 == \"+\" and op2 == \"+\" and op3 == \"<<\":\n mult = (1 << c3) + 1\n ma = self.alloc_scratch(f\"hm{stage}\", VLEN)\n ms = self.scratch_const(mult)\n const_ops.extend([(\"load\", (\"const\", ms, mult)), (\"valu\", (\"vbroadcast\", ma, ms))])\n hash_mult[stage] = ma\n n_nodes_v = self.alloc_scratch(\"nnv\", VLEN)\n const_ops.append((\"valu\", (\"vbroadcast\", n_nodes_v, self.scratch[\"n_nodes\"])))\n self.instrs.extend(self.build_vliw(const_ops))\n\n groups = [{\"idx\": self.alloc_scratch(f\"g{g}i\", VLEN), \"val\": self.alloc_scratch(f\"g{g}v\", VLEN), \"nv\": self.alloc_scratch(f\"g{g}n\", VLEN), \"t1\": self.alloc_scratch(f\"g{g}t\", VLEN)} for g in range(n_groups)]\n N_ADDR_BUFS = 16\n addr_bufs = [self.alloc_scratch(f\"ab{i}\", VLEN) for i in range(N_ADDR_BUFS)]\n\n # Broadcast nodes for levels 0, 1, 2\n bc_s0, bc_s1, bc_s2 = self.alloc_scratch(\"bs0\"), self.alloc_scratch(\"bs1\"), self.alloc_scratch(\"bs2\")\n bc_v1, bc_v2 = self.alloc_scratch(\"bv1\", VLEN), self.alloc_scratch(\"bv2\", VLEN)\n bc_v3, bc_v4 = self.alloc_scratch(\"bv3\", VLEN), self.alloc_scratch(\"bv4\", VLEN)\n bc_v5, bc_v6 = self.alloc_scratch(\"bv5\", VLEN), self.alloc_scratch(\"bv6\", VLEN)\n five_v = self.alloc_scratch(\"fv\", VLEN)\n sel_tmp = self.alloc_scratch(\"selt\", VLEN)\n lb1 = self.vec_const(1, \"lb1\")\n\n load_ops = []\n for g in range(n_groups):\n offset = self.scratch_const(g * VLEN)\n load_ops.append((\"load\", (\"const\", offset, g * VLEN)))\n for g in range(n_groups):\n gr, off, buf = groups[g], self.const_map[g * VLEN], addr_bufs[g % N_ADDR_BUFS]\n load_ops.extend([(\"alu\", (\"+\", buf, self.scratch[\"inp_indices_p\"], off)), (\"load\", (\"vload\", gr[\"idx\"], buf)), (\"alu\", (\"+\", buf, self.scratch[\"inp_values_p\"], off)), (\"load\", (\"vload\", gr[\"val\"], buf))])\n self.instrs.extend(self.build_vliw(load_ops))\n self.add(\"flow\", (\"pause\",))\n\n pre_ops = [(\"load\", (\"load\", bc_s0, self.scratch[\"forest_values_p\"]))]\n c1, c2 = self.scratch_const(1), self.scratch_const(2)\n pre_ops.extend([(\"alu\", (\"+\", bc_s1, self.scratch[\"forest_values_p\"], c1)), (\"alu\", (\"+\", bc_s2, self.scratch[\"forest_values_p\"], c2)), (\"load\", (\"load\", bc_s1, bc_s1)), (\"load\", (\"load\", bc_s2, bc_s2)), (\"valu\", (\"vbroadcast\", bc_v1, bc_s1)), (\"valu\", (\"vbroadcast\", bc_v2, bc_s2))])\n # Load L2 nodes and constant 5\n # Use dedicated temps to avoid conflicting with hash constants\n l2_idx = self.alloc_scratch(\"l2i\") # for index loading\n l2_addr = self.alloc_scratch(\"l2a\") # for address computation\n pre_ops.extend([(\"load\", (\"const\", l2_idx, 5)), (\"valu\", (\"vbroadcast\", five_v, l2_idx))])\n for ci_val, bv in [(3, bc_v3), (4, bc_v4), (5, bc_v5), (6, bc_v6)]:\n pre_ops.extend([(\"load\", (\"const\", l2_idx, ci_val)), (\"alu\", (\"+\", l2_addr, self.scratch[\"forest_values_p\"], l2_idx)), (\"load\", (\"load\", l2_addr, l2_addr)), (\"valu\", (\"vbroadcast\", bv, l2_addr))])\n self.instrs.extend(self.build_vliw(pre_ops))\n\n def get_level(r): return r if r <= 10 else r - 11\n g_round, g_phase, g_buf = [0]*n_groups, [0]*n_groups, [-1]*n_groups\n buf_avail = [True]*N_ADDR_BUFS\n all_ops = []\n while any(r < rounds for r in g_round):\n c_ops = []\n bc_adv = 0\n for g in range(n_groups):\n if g_round[g] >= rounds: continue\n rnd, lvl, gr = g_round[g], get_level(g_round[g]), groups[g]\n last = rnd == rounds - 1\n if g_phase[g] == 0:\n if lvl <= 2:\n if bc_adv < 4: g_phase[g] = 1; bc_adv += 1\n else:\n bi = next((i for i in range(N_ADDR_BUFS) if buf_avail[i]), -1)\n if bi >= 0:\n buf_avail[bi] = False; g_buf[g] = bi; buf = addr_bufs[bi]\n for lane in range(VLEN): c_ops.append((\"alu\", (\"+\", buf + lane, self.scratch[\"forest_values_p\"], gr[\"idx\"] + lane)))\n for lane in range(VLEN): c_ops.append((\"load\", (\"load\", gr[\"nv\"] + lane, buf + lane)))\n g_phase[g] = 1\n elif g_phase[g] == 1:\n if lvl == 0:\n c_ops.append((\"valu\", (\"vbroadcast\", gr[\"nv\"], bc_s0)))\n elif lvl == 1:\n c_ops.extend([(\"valu\", (\"-\", gr[\"t1\"], gr[\"idx\"], lb1)), (\"valu\", (\"-\", gr[\"t1\"], zero_v, gr[\"t1\"])), (\"valu\", (\"^\", gr[\"nv\"], bc_v1, bc_v2)), (\"valu\", (\"&\", gr[\"t1\"], gr[\"nv\"], gr[\"t1\"])), (\"valu\", (\"^\", gr[\"nv\"], bc_v1, gr[\"t1\"]))])\n c_ops.append((\"valu\", (\"^\", gr[\"val\"], gr[\"val\"], gr[\"nv\"])))\n for st, (o1, _, o2, o3, _) in enumerate(HASH_STAGES):\n if st in hash_mult:\n c_ops.append((\"valu\", (\"multiply_add\", gr[\"val\"], gr[\"val\"], hash_mult[st], hash_c1[st])))\n else:\n c_ops.extend([(\"valu\", (o1, gr[\"t1\"], gr[\"val\"], hash_c1[st])), (\"valu\", (o3, gr[\"nv\"], gr[\"val\"], hash_c3[st])), (\"valu\", (o2, gr[\"val\"], gr[\"t1\"], gr[\"nv\"]))])\n if not last:\n c_ops.extend([(\"valu\", (\"&\", gr[\"t1\"], gr[\"val\"], one_v)), (\"valu\", (\"multiply_add\", gr[\"idx\"], gr[\"idx\"], two_v, one_v)), (\"valu\", (\"+\", gr[\"idx\"], gr[\"idx\"], gr[\"t1\"]))])\n if rnd == 10 and not last:\n c_ops.extend([(\"valu\", (\"<\", gr[\"t1\"], gr[\"idx\"], n_nodes_v)), (\"valu\", (\"-\", gr[\"nv\"], zero_v, gr[\"t1\"])), (\"valu\", (\"&\", gr[\"idx\"], gr[\"idx\"], gr[\"nv\"]))])\n if g_buf[g] >= 0: buf_avail[g_buf[g]] = True; g_buf[g] = -1\n g_round[g] += 1; g_phase[g] = 0\n if rnd == 10:\n c_ops.extend([(\"valu\", (\"vbroadcast\", bc_v1, bc_s1)), (\"valu\", (\"vbroadcast\", bc_v2, bc_s2))])\n all_ops.extend(c_ops)\n\n st_ops = []\n for g in range(n_groups):\n gr, off, buf = groups[g], self.const_map[g * VLEN], addr_bufs[g % N_ADDR_BUFS]\n st_ops.extend([(\"alu\", (\"+\", buf, self.scratch[\"inp_values_p\"], off)), (\"store\", (\"vstore\", buf, gr[\"val\"]))])\n all_ops.extend(st_ops)\n self.instrs.extend(self.build_vliw(all_ops))\n self.instrs.append({\"flow\": [(\"pause\",)]})\n\nBASELINE = 147734\n\ndef do_kernel_test(\n forest_height: int,\n rounds: int,\n batch_size: int,\n seed: int = 123,\n trace: bool = False,\n prints: bool = False,\n):\n print(f\"{forest_height=}, {rounds=}, {batch_size=}\")\n random.seed(seed)\n forest = Tree.generate(forest_height)\n inp = Input.generate(forest, batch_size, rounds)\n mem = build_mem_image(forest, inp)\n\n kb = KernelBuilder()\n kb.build_kernel(forest.height, len(forest.values), len(inp.indices), rounds)\n # print(kb.instrs)\n\n value_trace = {}\n machine = Machine(\n mem,\n kb.instrs,\n kb.debug_info(),\n n_cores=N_CORES,\n value_trace=value_trace,\n trace=trace,\n )\n machine.prints = prints\n for i, ref_mem in enumerate(reference_kernel2(mem, value_trace)):\n machine.run()\n inp_values_p = ref_mem[6]\n if prints:\n print(machine.mem[inp_values_p : inp_values_p + len(inp.values)])\n print(ref_mem[inp_values_p : inp_values_p + len(inp.values)])\n assert (\n machine.mem[inp_values_p : inp_values_p + len(inp.values)]\n == ref_mem[inp_values_p : inp_values_p + len(inp.values)]\n ), f\"Incorrect result on round {i}\"\n inp_indices_p = ref_mem[5]\n if prints:\n print(machine.mem[inp_indices_p : inp_indices_p + len(inp.indices)])\n print(ref_mem[inp_indices_p : inp_indices_p + len(inp.indices)])\n # Updating these in memory isn't required, but you can enable this check for debugging\n # assert machine.mem[inp_indices_p:inp_indices_p+len(inp.indices)] == ref_mem[inp_indices_p:inp_indices_p+len(inp.indices)]\n\n print(\"CYCLES: \", machine.cycle)\n print(\"Speedup over baseline: \", BASELINE / machine.cycle)\n return machine.cycle\n\n\nclass Tests(unittest.TestCase):\n def test_ref_kernels(self):\n \"\"\"\n Test the reference kernels against each other\n \"\"\"\n random.seed(123)\n for i in range(10):\n f = Tree.generate(4)\n inp = Input.generate(f, 10, 6)\n mem = build_mem_image(f, inp)\n reference_kernel(f, inp)\n for _ in reference_kernel2(mem, {}):\n pass\n assert inp.indices == mem[mem[5] : mem[5] + len(inp.indices)]\n assert inp.values == mem[mem[6] : mem[6] + len(inp.values)]\n\n def test_kernel_trace(self):\n # Full-scale example for performance testing\n do_kernel_test(10, 16, 256, trace=True, prints=False)\n\n # Passing this test is not required for submission, see submission_tests.py for the actual correctness test\n # You can uncomment this if you think it might help you debug\n # def test_kernel_correctness(self):\n # for batch in range(1, 3):\n # for forest_height in range(3):\n # do_kernel_test(\n # forest_height + 2, forest_height + 4, batch * 16 * VLEN * N_CORES\n # )\n\n def test_kernel_cycles(self):\n do_kernel_test(10, 16, 256)\n\n\n# To run all the tests:\n# python perf_takehome.py\n# To run a specific test:\n# python perf_takehome.py Tests.test_kernel_cycles\n# To view a hot-reloading trace of all the instructions: **Recommended debug loop**\n# NOTE: The trace hot-reloading only works in Chrome. In the worst case if things aren't working, drag trace.json onto https://ui.perfetto.dev/\n# python perf_takehome.py Tests.test_kernel_trace\n# Then run `python watch_trace.py` in another tab, it'll open a browser tab, then click \"Open Perfetto\"\n# You can then keep that open and re-run the test to see a new trace.\n\n# To run the proper checks to see which thresholds you pass:\n# python tests/submission_tests.py\n\nif __name__ == \"__main__\":\n unittest.main()\n",
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" elif lvl == 1:",
" c_ops.extend([(\"valu\", (\"-\", gr[\"t1\"], gr[\"idx\"], lb1)), (\"valu\", (\"-\", gr[\"t1\"], zero_v, gr[\"t1\"])), (\"valu\", (\"^\", gr[\"nv\"], bc_v1, bc_v2)), (\"valu\", (\"&\", gr[\"t1\"], gr[\"nv\"], gr[\"t1\"])), (\"valu\", (\"^\", gr[\"nv\"], bc_v1, gr[\"t1\"]))])",
"+ elif lvl == 2:",
"+ # L2: 4-way selection. idx in {3,4,5,6}",
"+ # bit0 = idx&1: 3→1, 4→0, 5→1, 6→0",
"+ # sel_low = v4 if bit0=0, v3 if bit0=1",
"+ # sel_high = v6 if bit0=0, v5 if bit0=1",
"+ # result = sel_low if idx<5, else sel_high",
"+ c_ops.extend([",
"+ (\"valu\", (\"&\", gr[\"t1\"], gr[\"idx\"], one_v)), # t1 = bit0",
"+ (\"valu\", (\"-\", gr[\"t1\"], zero_v, gr[\"t1\"])), # t1 = mask0 (-1 when odd)",
"+ (\"valu\", (\"^\", gr[\"nv\"], bc_v4, bc_v3)), # nv = v4^v3",
"+ (\"valu\", (\"&\", gr[\"nv\"], gr[\"nv\"], gr[\"t1\"])), # nv &= mask0",
"+ (\"valu\", (\"^\", gr[\"nv\"], bc_v4, gr[\"nv\"])), # nv = sel_low",
"+ (\"valu\", (\"^\", sel_tmp, bc_v6, bc_v5)), # sel_tmp = v6^v5",
"+ (\"valu\", (\"&\", sel_tmp, sel_tmp, gr[\"t1\"])), # sel_tmp &= mask0",
"+ (\"valu\", (\"^\", sel_tmp, bc_v6, sel_tmp)), # sel_tmp = sel_high",
"+ (\"valu\", (\"<\", gr[\"t1\"], gr[\"idx\"], five_v)), # t1 = blend_mask (-1 when idx<5)",
"+ (\"valu\", (\"^\", gr[\"nv\"], gr[\"nv\"], sel_tmp)), # nv = sel_low ^ sel_high",
"+ (\"valu\", (\"&\", gr[\"nv\"], gr[\"nv\"], gr[\"t1\"])), # nv &= blend_mask",
"+ (\"valu\", (\"^\", gr[\"nv\"], gr[\"nv\"], sel_tmp)), # nv = result",
"+ ])",
" c_ops.append((\"valu\", (\"^\", gr[\"val\"], gr[\"val\"], gr[\"nv\"])))",
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