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Offloading Java to Graphics Processors

Peter Calvert

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

October 6, 2011 by hgpu

Parallelisation of Java for Graphics Processors

Peter Calvert

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Tags: Algorithms, Benchmarking, Code generation, Computer science, CUDA, Java, nVidia, nVidia GeForce 9600 GT, nVidia GeForce GTX 260, Package, Thesis

October 6, 2011 by hgpu

A Framework for Automated Performance Tuning and Code Verification on GPU Computing Platforms

Allison S. Gehrke, Ilkyeun Ra, Daniel A. Connors

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Tags: Code generation, Computer science, Numerical simulation, nVidia, Optimization, Performance, PTX

October 5, 2011 by hgpu

CheCL: Transparent Checkpointing and Process Migration of OpenCL Applications

Hiroyuki Takizawa, Kentaro Koyama, Katuto Sato, Kazuhiko Komatsu, Hiroaki Kobayashi

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Tags: ATI, ATI Radeon HD 5870, Code generation, Compilers, Computer science, MPI, nVidia, OpenCL, Tesla C1060

October 1, 2011 by hgpu

A PTX Code Generator for LLVM

Helge Rhodin

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Tags: Code generation, Compilers, Computer science, CUDA, nVidia, Optimization, PTX, Thesis, Virtualization

September 30, 2011 by hgpu

PIPS Is not (just) Polyhedral Software

Mehdi Amini, Corinne Ancourt, Fabien Coelho, Beatrice Creusillet, Serge Guelton, Francois Irigoin, Pierre Jouvelot, Ronan Keryell, Pierre Villalon

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Tags: Code generation, Computer science, CUDA, Heterogeneous systems, nVidia, OpenCL

September 27, 2011 by hgpu

Generating GPU Code from a High-level Representation for Image Processing Kernels

Richard Membarth, Anton Lokhmotov, Jurgen Teich

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Tags: ATI, ATI Radeon HD 5870, Code generation, Compilers, CUDA, Image processing, nVidia, nVidia GeForce GTX 280, nVidia Quadro FX 5800, OpenCL, Optimization, Tesla C2050

September 26, 2011 by hgpu

LLVM to PTX Backend

Marco Keller

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

September 26, 2011 by hgpu

Identifying scalar behavior in CUDA kernels

Sylvain Collange

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Tags: Benchmarking, Code generation, Compilers, Computer science, CUDA, nVidia, PTX

September 26, 2011 by hgpu

Putting Automatic Polyhedral Compilation for GPGPU to Work

Soufiane Baghdadi, Armin Grosslinger, Albert Cohen

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Tags: Algorithms, Code generation, Compilers, Computer science, CUDA, nVidia, Package

September 26, 2011 by hgpu

Optimizing OpenCL Kernels for Iterative Statistical Applications on GPUs

Thilina Gunarathne, Bimalee Salpitikorala, Arun Chauhan, Geoffrey Fox

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Tags: Algorithms, Code generation, Computer science, nVidia, OpenCL, Optimization, Statistics, Tesla C1060

September 25, 2011 by hgpu

Automatic Translation of CUDA to OpenCL and Comparison of Performance Optimizations on GPUs

Deepthi Nandakumar

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Tags: ATI, ATI Radeon HD 5870, Benchmarking, Code generation, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GTX 280, OpenCL, Optimization, Thesis

September 24, 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

Offloading Java to Graphics Processors

Parallelisation of Java for Graphics Processors

A Framework for Automated Performance Tuning and Code Verification on GPU Computing Platforms

CheCL: Transparent Checkpointing and Process Migration of OpenCL Applications

A PTX Code Generator for LLVM

PIPS Is not (just) Polyhedral Software

Generating GPU Code from a High-level Representation for Image Processing Kernels

LLVM to PTX Backend

Identifying scalar behavior in CUDA kernels

Putting Automatic Polyhedral Compilation for GPGPU to Work

Optimizing OpenCL Kernels for Iterative Statistical Applications on GPUs

Automatic Translation of CUDA to OpenCL and Comparison of Performance Optimizations on GPUs

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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