Ithemal: Accurate, Portable and Fast Basic Block Throughput Estimation using Deep Neural Networks

TL;DR

Ithemal is a deep learning-based tool that accurately and quickly predicts processor instruction throughput, outperforming traditional analytical models and easily adapting across different microarchitectures.

Contribution

It introduces a hierarchical LSTM model for throughput prediction that surpasses existing analytical tools in accuracy and portability.

Findings

Ithemal achieves less than half the error of state-of-the-art models.

It predicts throughput as fast as traditional tools.

It is easily adaptable to various processor microarchitectures.

Abstract

Predicting the number of clock cycles a processor takes to execute a block of assembly instructions in steady state (the throughput) is important for both compiler designers and performance engineers. Building an analytical model to do so is especially complicated in modern x86-64 Complex Instruction Set Computer (CISC) machines with sophisticated processor microarchitectures in that it is tedious, error prone, and must be performed from scratch for each processor generation. In this paper we present Ithemal, the first tool which learns to predict the throughput of a set of instructions. Ithemal uses a hierarchical LSTM--based approach to predict throughput based on the opcodes and operands of instructions in a basic block. We show that Ithemal is more accurate than state-of-the-art hand-written tools currently used in compiler backends and static machine code analyzers. In particular, our model has less than half the error of state-of-the-art analytical models (LLVM's llvm-mca and Intel's IACA). Ithemal is also able to predict these throughput values just as fast as the aforementioned tools, and is easily ported across a variety of processor microarchitectures with minimal developer effort.