Search Results for author:
Found 21 papers, 9 papers with code
LOOPer: A Learned Automatic Code Optimizer For Polyhedral Compilers
In this paper, we introduce LOOPer, the first polyhedral autoscheduler that uses a deep-learning based cost model and covers a large set of affine transformations and programs.
Compiler generated feedback for Large Language Models
We introduce a novel paradigm in compiler optimization powered by Large Language Models with compiler feedback to optimize the code size of LLVM assembly.
Priority Sampling of Large Language Models for Compilers
Large language models show great potential in generating and optimizing code.
CRUXEval: A Benchmark for Code Reasoning, Understanding and Execution
The best setup, GPT-4 with chain of thought (CoT), achieves a pass@1 of 75% and 81% on input and output prediction, respectively.
Sieve: Multimodal Dataset Pruning Using Image Captioning Models
We propose a pruning signal, Sieve, that employs synthetic captions generated by image-captioning models pretrained on small, diverse, and well-aligned image-text pairs to evaluate the alignment of noisy image-text pairs.
Large Language Models for Compiler Optimization
We explore the novel application of Large Language Models to code optimization.
BenchDirect: A Directed Language Model for Compiler Benchmarks
We improve this with BenchDirect which utilizes a directed LM that infills programs by jointly observing source code context and the compiler features that are targeted.
Learning Compiler Pass Orders using Coreset and Normalized Value Prediction
Finding the optimal pass sequence of compilation can lead to a significant reduction in program size and/or improvement in program efficiency.
Contrastive Distillation Is a Sample-Efficient Self-Supervised Loss Policy for Transfer Learning
Traditional approaches to RL have focused on learning decision policies directly from episodic decisions, while slowly and implicitly learning the semantics of compositional representations needed for generalization.
BenchPress: A Deep Active Benchmark Generator
We develop BenchPress, the first ML benchmark generator for compilers that is steerable within feature space representations of source code.
Code Translation with Compiler Representations
Here we propose to augment code translation with IRs, specifically LLVM IR, with results on the C++, Java, Rust, and Go languages.
Progress Report: A Deep Learning Guided Exploration of Affine Unimodular Loop Transformations
In this paper, we present a work in progress about a deep learning based approach for automatic code optimization in polyhedral compilers.
LoopStack: a Lightweight Tensor Algebra Compiler Stack
We present LoopStack, a domain specific compiler stack for tensor operations, composed of a frontend, LoopTool, and an efficient optimizing code generator, LoopNest.
CompilerGym: Robust, Performant Compiler Optimization Environments for AI Research
What is needed is an easy, reusable experimental infrastructure for real world compiler optimization tasks that can serve as a common benchmark for comparing techniques, and as a platform to accelerate progress in the field.
Using Graph Neural Networks to model the performance of Deep Neural Networks
State-of-the-art deep-learning compilers like TVM and Halide incorporate a learning-based performance model to search the space of valid implementations of a given deep learning algorithm.
Value Function Based Performance Optimization of Deep Learning Workloads
As machine learning techniques become ubiquitous, the efficiency of neural network implementations is becoming correspondingly paramount.
Deep Data Flow Analysis
Compiler architects increasingly look to machine learning when building heuristics for compiler optimization.
ProGraML: Graph-based Deep Learning for Program Optimization and Analysis
We introduce ProGraML - Program Graphs for Machine Learning - a novel graph-based program representation using a low level, language agnostic, and portable format; and machine learning models capable of performing complex downstream tasks over these graphs.
End-to-End Deep Learning of Optimization Heuristics
We develop a deep neural network that learns heuristics over raw code, entirely without using code features.
Iterative compilation on mobile devices
Replaying the targeted functions allows us to evaluate the effectiveness of each set of optimizations for the actual way the user interacts with the application.
Programming Languages
Autotuning OpenCL Workgroup Size for Stencil Patterns
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 implementation of the kernel.
Distributed, Parallel, and Cluster Computing
