Papers Compiler Optimization
“Compiler Optimization” 태그가 달린 논문 45편 · 필터 해제
Compiler Optimization via LLM Reasoning for Efficient Model Serving
While model serving has unlocked unprecedented capabilities, the high cost of serving large-scale models continues to be a significant barrier to widespread accessibility and rapid innovation. Compiler optimizations have…
Compiler OptimizationLarge Language ModelBridging the PLC Binary Analysis Gap: A Cross-Compiler Dataset and Neural Framework for Industrial Control Systems
Industrial Control Systems (ICS) rely heavily on Programmable Logic Controllers (PLCs) to manage critical infrastructure, yet analyzing PLC executables remains challenging due to diverse proprietary compilers and limited…
Compiler OptimizationAccelerating Large Language Models through Partially Linear Feed-Forward Network
Large language models (LLMs) demonstrate remarkable capabilities but face deployment challenges due to their massive parameter counts. While existing compression techniques like pruning can reduce model size, it leads to…
Compiler OptimizationEnhancing Code LLMs with Reinforcement Learning in Code Generation: A Survey
Reinforcement learning (RL) has emerged as a powerful paradigm for enhancing large language models (LLMs) in code generation and optimization. This survey systematically reviews RL-driven techniques across the code devel…
Code GenerationCompiler Optimizationreinforcement-learningReinforcement Learning+2Automated Strategy Invention for Confluence of Term Rewrite Systems
Term rewriting plays a crucial role in software verification and compiler optimization. With dozens of highly parameterizable techniques developed to prove various system properties, automatic term rewriting tools work i…
Compiler OptimizationOptimizing Tensor Computation Graphs with Equality Saturation and Monte Carlo Tree Search
The real-world effectiveness of deep neural networks often depends on their latency, thereby necessitating optimization techniques that can reduce a model's inference time while preserving its performance. One popular ap…
Compiler OptimizationMeta Large Language Model Compiler: Foundation Models of Compiler Optimization
Large Language Models (LLMs) have demonstrated remarkable capabilities across a variety of software engineering and coding tasks. However, their application in the domain of code and compiler optimization remains underex…
Compiler OptimizationGPULanguage ModelingLanguage Modelling+1CATBench: A Compiler Autotuning Benchmarking Suite for Black-box Optimization
Bayesian optimization is a powerful method for automating tuning of compilers. The complex landscape of autotuning provides a myriad of rarely considered structural challenges for black-box optimizers, and the lack of st…
Bayesian OptimizationBenchmarkingCompiler Optimizationtensor algebraOptimization of Armv9 architecture general large language model inference performance based on Llama.cpp
This article optimizes the inference performance of the Qwen-1.8B model by performing Int8 quantization, vectorizing some operators in llama.cpp, and modifying the compilation script to improve the compiler optimization …
Compiler OptimizationLanguage ModelingLanguage ModellingLarge Language Model+1Automated Creation of Source Code Variants of a Cryptographic Hash Function Implementation Using Generative Pre-Trained Transformer Models
Generative pre-trained transformers (GPT's) are a type of large language machine learning model that are unusually adept at producing novel, and coherent, natural language. In this study the ability of GPT models to gene…
C++ codeCompiler OptimizationOffline Imitation Learning from Multiple Baselines with Applications to Compiler Optimization
This work studies a Reinforcement Learning (RL) problem in which we are given a set of trajectories collected with K baseline policies. Each of these policies can be quite suboptimal in isolation, and have strong perform…
Compiler OptimizationImitation LearningReinforcement Learning (RL)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. The model takes unoptimized LLVM IR as input and produces optimiz…
Compiler OptimizationNova: Generative Language Models for Assembly Code with Hierarchical Attention and Contrastive Learning
Binary code analysis is the foundation of crucial tasks in the security domain; thus building effective binary analysis techniques is more important than ever. Large language models (LLMs) although have brought impressiv…
Code GenerationCode TranslationCompiler OptimizationContrastive Learning+2WhiteFox: White-Box Compiler Fuzzing Empowered by Large Language Models
Compiler correctness is crucial, as miscompilation can falsify program behaviors, leading to serious consequences. Fuzzing has been studied to uncover compiler defects. However, compiler fuzzing remains challenging: Exis…
Code GenerationCompiler OptimizationLarge Language Models for Compiler Optimization
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 size. The model takes as input unoptimiz…
Auxiliary LearningCompiler OptimizationTarget-independent XLA optimization using Reinforcement Learning
An important challenge in Machine Learning compilers like XLA is multi-pass optimization and analysis. There has been recent interest chiefly in XLA target-dependent optimization on the graph-level, subgraph-level, and k…
Compiler OptimizationDeep Reinforcement Learningreinforcement-learningReinforcement Learning+1AMULET: Adaptive Matrix-Multiplication-Like Tasks
Many useful tasks in data science and machine learning applications can be written as simple variations of matrix multiplication. However, users have difficulty performing such tasks as existing matrix/vector libraries s…
Compiler OptimizationTensor Slicing and Optimization for Multicore NPUs
Although code generation for Convolution Neural Network (CNN) models has been extensively studied, performing efficient data slicing and parallelization for highly-constrai\-ned Multicore Neural Processor Units (NPUs) is…
Code GenerationCompiler OptimizationRobust Scheduling with GFlowNets
Finding the best way to schedule operations in a computation graph is a classical NP-hard problem which is central to compiler optimization. However, evaluating the goodness of a schedule on the target hardware can be ve…
Compiler OptimizationDiversitySchedulingLearning 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. Prior works on compilation pass ordering have two major drawbacks. They eithe…
Compiler OptimizationGraph LearningGraph Neural NetworkValue prediction