This benchmark evaluates execution performance of JAM's Polkadot Virtual Machine (PVM) using two modes: an interpreter and an ahead-of-time (AOT) recompiler. All tests were performed on Tessera, a clean-room Python implementation of the PVM developed by ChainScore Labs. Benchmarks include synthetic (ADD_JUMP) and real-world (Conway's Game of Life) workloads.
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| pragma solidity ^0.5.1; | |
| contract Context { | |
| // Empty internal constructor, to prevent people from mistakenly deploying | |
| // an instance of this contract, which should be used via inheritance. | |
| constructor () internal { } | |
| // solhint-disable-previous-line no-empty-blocks | |
| function _msgSender() internal view returns (address payable) { | |
| return msg.sender; |
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| pragma solidity ^0.5.0; | |
| // import "./metatx/NativeMetaTransaction.sol"; | |
| // import "./metatx/ContextMixin.sol"; | |
| import "./metatx/EIP712MetaTransaction.sol"; | |
| contract Create2CloneFactory is EIP712MetaTransaction | |
| { |
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| pragma solidity ^0.5.0; | |
| // import "./metatx/NativeMetaTransaction.sol"; | |
| // import "./metatx/ContextMixin.sol"; | |
| import "./metatx/EIP712MetaTransaction.sol"; | |
| contract Create2CloneFactory is EIP712MetaTransaction | |
| { |
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| pragma solidity ^0.5.0; | |
| // import "./metatx/NativeMetaTransaction.sol"; | |
| // import "./metatx/ContextMixin.sol"; | |
| import "./metatx/EIP712MetaTransaction.sol"; | |
| contract Create2CloneFactory is EIP712MetaTransaction | |
| { |
With this tutorial, new para-chain developers would benefit for building custom pallets from scratch and adding that to their existing runtime. I am going to use a pallets template, modify and add custom functions to it, check for errors and give instructions about how to implement it. I love substrate framework, and would be very happy to help other developers get into substrate.
Beginners into learning substrate framework, who has an understanding of blockchain runtime, storage, calls. And atleast has experience running the substrate node template with predefined pallets.
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| import numpy as np | |
| class ELM(object): | |
| def __init__(self, inputSize, outputSize, hiddenSize): | |
| """ | |
| Initialize weight and bias between input layer and hidden layer | |
| Parameters: | |
| inputSize: int | |
| The number of input layer dimensions or features in the training data |