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Towards Automatic C Programs Optimization and Parallelization using the PIPS-PoCC Integration

Dounia Khaldi, Corinne Ancourt, Francois Irigoin

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Tags: Benchmarking, Code generation, Computer science, CUDA, FPGA, Heterogeneous systems, MPI, nVidia, OpenMP, Optimization, Package

December 19, 2011 by hgpu

Experiences Developing the OpenUH Compiler and Runtime Infrastructure

Barbara Chapman, Deepak Eachempati, Oscar Hernandez

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Tags: Code generation, Compilers, Computer science, nVidia, Package, PTX

December 19, 2011 by hgpu

A Translation Framework for Executing the Sequential Binary Code on CPU/GPU Based Architectures

Erzhou Zhu, Haibing Guan, Guoxing Dong, Yindong Yang, Hongbo Yang

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Tags: Benchmarking, Code generation, Computer science, CUDA, nVidia, nVidia GeForce GTX 260, PTX

December 18, 2011 by hgpu

Automatic code generation and tuning for stencil kernels on modern shared memory architectures

Matthias Christen, Olaf Schenk, Helmar Burkhart

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Tags: Code generation, Computer science, CUDA, nVidia, Package, Tesla C2050

December 18, 2011 by hgpu

Extendable pattern-oriented optimization directives

Huimin Cui, Jingling Xue, Lei Wang, Yang Yang, Xiaobing Feng, Dongrui Fan

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Tags: Algorithms, Code generation, Computer science, CUDA, nVidia, nVidia GeForce GTX 285, Optimization

December 17, 2011 by hgpu

Code Optimization Techniques for Graphics Processing Units

Fernando Magno Quintao Pereira

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Tags: Algorithms, Book, Code generation, Computer science, CUDA, nVidia, nVidia GeForce 9400 M, Optimization, Package, Programming techniques, PTX, Tutorial

December 17, 2011 by hgpu

Bringing Parallel Performance to Python with Domain-Specific Selective Embedded Just-in-Time Specialization

Shoaib Kamil, Derrick Coetzee, Armando Fox

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Tags: Code generation, Compilers, Computer science, CUDA, nVidia, Python

December 12, 2011 by hgpu

Contributions to Parallel Simulation of Equation-Based Models on Graphics Processing Units

Kristian Stavaker

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Tags: Code generation, Computer science, CUDA, nVidia, nVidia GeForce 8600, OpenCL, Tesla C1060, Tesla M2050, Thesis

December 9, 2011 by hgpu

A programming language interface to describe transformations and code generation

Gabe Rudy, Malik Murtaza Khan, Mary Hall, Chun Chen, Jacqueline Chame

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Tags: Algorithms, Code generation, Computer science, CUBLAS, CUDA, MPI, nVidia, nVidia GeForce GTX 280, Tesla C2050

December 1, 2011 by hgpu

A New Compilation Path: From Python/NumPy to OpenCL

Xunhao Li, Rahul Garg, Jose Nelson Amaral

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Tags: ATI, ATI Radeon HD 5850, Benchmarking, Code generation, Compilers, Computer science, nVidia, nVidia GeForce GTX 260, OpenCL, Python

November 26, 2011 by hgpu

Building-Blocks for Performance Oriented DSLs

Tiark Rompf, Arvind K. Sujeeth, HyoukJoong Lee, Kevin J. Brown, Hassan Chafi, Martin Odersky, Kunle Olukotun

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Tags: Code generation, Computer science, CUDA, Heterogeneous systems, nVidia, Performance, Programming Languages, Tesla C2050

November 23, 2011 by hgpu

Online Adaptive Code Generation and Tuning

Ananta Tiwari, Jeffrey K. Hollingsworth

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Tags: Code generation, Computer science, Optimization

November 21, 2011 by hgpu

MegaTrain: Full Precision Training of 100B+ Parameter Large Language Models on a Single GPU

Device Virtual Machine (DVM)

DVM: Real-Time Kernel Generation for Dynamic AI Models

Agentic Code Optimization via Compiler-LLM Cooperation

AutoKernel: Autoresearch for GPU kernels. Give it any PyTorch model, go to sleep, wake up to optimized Triton kernels

AutoKernel: Autonomous GPU Kernel Optimization via Iterative Agent-Driven Search

Triton-Sanitizer: A Fast and Device-Agnostic Memory Sanitizer for Triton with Rich Diagnostic Context

LLM.Q: Quantized LLM training in pure CUDA/C++

LLMQ: Efficient Lower-Precision LLM Training for Consumer GPUs

SOL-ExecBench: Speed-of-Light Benchmarking for Real-World GPU Kernels Against Hardware Limits

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high performance computing on graphics processing units: hgpu.org

Towards Automatic C Programs Optimization and Parallelization using the PIPS-PoCC Integration

Experiences Developing the OpenUH Compiler and Runtime Infrastructure

A Translation Framework for Executing the Sequential Binary Code on CPU/GPU Based Architectures

Automatic code generation and tuning for stencil kernels on modern shared memory architectures

Extendable pattern-oriented optimization directives

Code Optimization Techniques for Graphics Processing Units

Bringing Parallel Performance to Python with Domain-Specific Selective Embedded Just-in-Time Specialization

Contributions to Parallel Simulation of Equation-Based Models on Graphics Processing Units

A programming language interface to describe transformations and code generation

A New Compilation Path: From Python/NumPy to OpenCL

Building-Blocks for Performance Oriented DSLs

Online Adaptive Code Generation and Tuning

Recent source codes

MegaTrain: Full Precision Training of 100B+ Parameter Large Language Models on a Single GPU

Device Virtual Machine (DVM)

Agentic Code Optimization via Compiler-LLM Cooperation

AutoKernel: Autoresearch for GPU kernels. Give it any PyTorch model, go to sleep, wake up to optimized Triton kernels

Triton-Sanitizer: A Fast and Device-Agnostic Memory Sanitizer for Triton with Rich Diagnostic Context

LLM.Q: Quantized LLM training in pure CUDA/C++

SOL-ExecBench: Speed-of-Light Benchmarking for Real-World GPU Kernels Against Hardware Limits

True 4-Bit Quantized CNN Training on CPU

cuFuzz: A GPU-oriented coverage-guided fuzzer for userland CUDA application

KernelSkill: A Multi-Agent Framework for GPU Kernel Optimization

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