24 citations · 57 across the 15 of their papers we have counts for
16 papers
Compiler generated feedback for Large Language Models
Dejan Grubisic, Chris Cummins, Volker Seeker +1
We introduce a novel paradigm in compiler optimization powered by Large Language Models with compiler feedback to optimize the code size of LLVM assembly. The model takes unoptimiz…
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…
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…
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…
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…
Contrastive Distillation Is a Sample-Efficient Self-Supervised Loss Policy for Transfer Learning
Chris Lengerich, Gabriel Synnaeve, Amy Zhang +4
Traditional approaches to RL have focused on learning decision policies directly from episodic decisions, while slowly and implicitly learning the semantics of compositional repres…
BenchPress: A Deep Active Benchmark Generator
Foivos Tsimpourlas, Pavlos Petoumenos, Min Xu +4
We develop BenchPress, the first ML benchmark generator for compilers that is steerable within feature space representations of source code. BenchPress synthesizes compiling functi…
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…
Caviar: An E-graph Based TRS for Automatic Code Optimization
Smail Kourta, Adel Namani, Fatima Benbouzid-Si Tayeb +4
Term Rewriting Systems (TRSs) are used in compilers to simplify and prove expressions. State-of-the-art TRSs in compilers use a greedy algorithm that applies a set of rewriting rul…
CompilerGym: Robust, Performant Compiler Optimization Environments for AI Research
Chris Cummins, Bram Wasti, Jiadong Guo +9
Interest in applying Artificial Intelligence (AI) techniques to compiler optimizations is increasing rapidly, but compiler research has a high entry barrier. Unlike in other domain…
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…
Deep Data Flow Analysis
Chris Cummins, Hugh Leather, Zacharias Fisches +3
Compiler architects increasingly look to machine learning when building heuristics for compiler optimization. The promise of automatic heuristic design, freeing the compiler engine…
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…
Iterative compilation on mobile devices
Paschalis Mpeis, Pavlos Petoumenos, Hugh Leather
The abundance of poorly optimized mobile applications coupled with their increasing centrality in our digital lives make a framework for mobile app optimization an imperative. Whil…
Autotuning OpenCL Workgroup Size for Stencil Patterns
Chris Cummins, Pavlos Petoumenos, Michel Steuwer +1
Selecting an appropriate workgroup size is critical for the performance of OpenCL kernels, and requires knowledge of the underlying hardware, the data being operated on, and the im…