IThemal: Accurate, Portable and Fast Basic Block Throughput Estimation using Deep Neural Networks
Name
1808.07412.pdf
Description
Accepted version
Size
1.51 MB
Format
Adobe PDF
Checksum (MD5)
983b1623d33a1eba512b03cd0d382bd7
Author(s)
Mendis, C
Renda, A
Amarasinghe, S
Carbin, M
Date Issued
June 2019
Journal
36th International Conference on Machine Learning, ICML 2019
Citation
Mendis, C, Renda, A, Amarasinghe, S and Carbin, M. 2019. "IThemal: Accurate, Portable and Fast Basic Block Throughput Estimation using Deep Neural Networks." 36th International Conference on Machine Learning, ICML 2019, 2019-June.
Version
Author's final manuscript
Abstract
© 2019 by the author(s). 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.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
http://proceedings.mlr.press/v97/mendis19a/mendis19a.pdf
