11 citations · 23 across the 12 of their papers we have counts for
6 papers · 1 filter
Don't Transform the Code, Code the Transforms: Towards Precise Code Rewriting using LLMs
Chris Cummins, Volker Seeker, Jordi Armengol-Estapé +3
Tools for rewriting, refactoring and optimizing code should be fast and correct. Large language models (LLMs), by their nature, possess neither of these qualities. Yet, there remai…
Priority Sampling of Large Language Models for Compilers
Dejan Grubisic, Chris Cummins, Volker Seeker +1
Large language models show great potential in generating and optimizing code. Widely used sampling methods such as Nucleus Sampling increase the diversity of generation but often p…
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…
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…
Value Function Based Performance Optimization of Deep Learning Workloads
Benoit Steiner, Chris Cummins, Horace He +1
As machine learning techniques become ubiquitous, the efficiency of neural network implementations is becoming correspondingly paramount. Frameworks, such as Halide and TVM, separa…
ProGraML: Graph-based Deep Learning for Program Optimization and Analysis
Chris Cummins, Zacharias V. Fisches, Tal Ben-Nun +2
The increasing complexity of computing systems places a tremendous burden on optimizing compilers, requiring ever more accurate and aggressive optimizations. Machine learning offer…