11 citations · 22 across the 10 of their papers we have counts for
6 papers · 1 filter
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…
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…
LoopStack: a Lightweight Tensor Algebra Compiler Stack
Bram Wasti, José Pablo Cambronero, Benoit Steiner +2
We present LoopStack, a domain specific compiler stack for tensor operations, composed of a frontend, LoopTool, and an efficient optimizing code generator, LoopNest. This stack ena…
Using Graph Neural Networks to model the performance of Deep Neural Networks
Shikhar Singh, Benoit Steiner, James Hegarty +1
With the unprecedented proliferation of machine learning software, there is an ever-increasing need to generate efficient code for such applications. State-of-the-art deep-learning…
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…