activity
20152026
most citedProGraML: Graph-based Deep Learning for Program Optimization and Analysis

24 citations · 59 across the 20 of their papers we have counts for

collaborators
Showing 2023Show all

5 papers · 1 filter

cs.PL2023★ 11 cited

Large Language Models for Compiler Optimization

Chris Cummins, Volker Seeker, Dejan Grubisic +8

We explore the novel application of Large Language Models to code optimization. We present a 7B-parameter transformer model trained from scratch to optimize LLVM assembly for code…

cs.LG2023

LoopTune: Optimizing Tensor Computations with Reinforcement Learning

Dejan Grubisic, Bram Wasti, Chris Cummins +2

Advanced compiler technology is crucial for enabling machine learning applications to run on novel hardware, but traditional compilers fail to deliver performance, popular auto-tun…

cs.PL2023

SLaDe: A Portable Small Language Model Decompiler for Optimized Assembly

Jordi Armengol-Estapé, Jackson Woodruff, Chris Cummins +1

Decompilation is a well-studied area with numerous high-quality tools available. These are frequently used for security tasks and to port legacy code. However, they regularly gener…

cs.LG2023

BenchDirect: A Directed Language Model for Compiler Benchmarks

Foivos Tsimpourlas, Pavlos Petoumenos, Min Xu +4

The exponential increase of hardware-software complexity has made it impossible for compiler engineers to find the right optimization heuristics manually. Predictive models have be…

cs.PL2023★ 2 cited

Learning Compiler Pass Orders using Coreset and Normalized Value Prediction

Youwei Liang, Kevin Stone, Ali Shameli +8

Finding the optimal pass sequence of compilation can lead to a significant reduction in program size and/or improvement in program efficiency. Prior works on compilation pass order…