high performance computing on graphics processing units: hgpu.org

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Operating Systems Challenges for GPU Resource Management

Shinpei Kato, Scott Brandt, Yutaka Ishikawa, Ragunathan (Raj) Rajkumar

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Tags: Computer science, CUDA, GPU cluster, nVidia, OpenCL, Operating systems, Virtualization

October 15, 2011 by hgpu

Towards Utilizing Remote GPUs for CUDA Program Execution

Xiaonan Ji, Spencer Davis, Erikson Hardesty, Xu Liang, Sabuj Saha, Hai Jiang

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Tags: Algorithms, Computer science, CUDA, GPU cluster, Networks, nVidia

October 15, 2011 by hgpu

Dymaxion: Optimizing Memory Access Patterns for Heterogeneous Systems

Shuai Che, Jeremy W. Sheaffer, Kevin Skadron

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Tags: Computer science, CUDA, Heterogeneous systems, Memory, nVidia, nVidia GeForce GTX 285, nVidia GeForce GTX 480, Optimization, Performance

October 15, 2011 by hgpu

Effects of compression on data intensive algorithms

Ahmed Adnan Aqrawi

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Tags: Algorithms, Compression, Computer science, CUDA, Filtering, Image processing, nVidia, Seismology, Tesla C1060, Tesla C2050, Thesis

October 15, 2011 by hgpu

Bandwidth Reduction Through Multithreaded Compression of Seismic Images

Ahmed A. Aqrawi, Anne C. Elster

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Tags: Algorithms, Compression, Computer science, CUDA, Image processing, nVidia, Seismology, Tesla C2050

October 15, 2011 by hgpu

Speeding up the MATLAB complex networks package using graphic processors

Zhang Bai-Da, Tang Yu-Hua, Wu Jun-Jie, Li Xin

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Tags: Algorithms, Computer science, CUDA, Networks, nVidia, nVidia GeForce GTX 480

October 15, 2011 by hgpu

GPU fluids in production: a compiler approach to parallelism

Dan Bailey, Ian Masters, Matt Warner

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Tags: Code generation, Compilers, Computer science, Image processing, nVidia, Performance, Presentation, PTX, Tesla C2050

October 15, 2011 by hgpu

Efficient Mapping of Streaming Applications for Image Processing on Graphics Cards

Richard Membarth, Hritam Dutta, Frank Hannig, Jurgen Teich

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Tags: Algorithms, CUDA, Image processing, nVidia, Package, Tesla C1060

October 15, 2011 by hgpu

An Analysis of Programmer Productivity versus Performance for High Level Data Parallel Programming

Alex Cole, Alistair McEwan, Satnam Singh

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Tags: Computer science, CUDA, FPGA, nVidia, nVidia GeForce GTX 460, Programming techniques

October 14, 2011 by hgpu

Accelerating Large Scale Image Analyses on Parallel CPU-GPU Equipped Systems

George Teodoro, Tahsin M. Kurc, Tony Pan, Lee Cooper, Jun Kong, Patrick Widener, Joel H. Saltz

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Tags: CUDA, Heterogeneous systems, Image processing, nVidia, Tesla M2070

October 14, 2011 by hgpu

CudaDMA: Optimizing GPU Memory Bandwidth via Warp Specialization

Michael Bauer, Henry Cook, Brucek Khailany

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Tags: Benchmarking, Computer science, CUDA, Memory model, nVidia, Optimization, Package, Performance, Tesla C2050

October 14, 2011 by hgpu

OptiML: An implicitly parallel domain-specific language for machine learning

Arvind K. Sujeeth, HyoukJoong Lee, Kevin J. Brown, Tiark Rompf, Hassan Chafi, Michael Wu, Anand R. Atreya, Martin Odersky, Kunle Olukotun

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Tags: Computer science, Heterogeneous systems, Machine learning, nVidia, OpenCL, Optimization, Tesla C2050

October 14, 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

Operating Systems Challenges for GPU Resource Management

Towards Utilizing Remote GPUs for CUDA Program Execution

Dymaxion: Optimizing Memory Access Patterns for Heterogeneous Systems

Effects of compression on data intensive algorithms

Bandwidth Reduction Through Multithreaded Compression of Seismic Images

Speeding up the MATLAB complex networks package using graphic processors

GPU fluids in production: a compiler approach to parallelism

Efficient Mapping of Streaming Applications for Image Processing on Graphics Cards

An Analysis of Programmer Productivity versus Performance for High Level Data Parallel Programming

Accelerating Large Scale Image Analyses on Parallel CPU-GPU Equipped Systems

CudaDMA: Optimizing GPU Memory Bandwidth via Warp Specialization

OptiML: An implicitly parallel domain-specific language for machine learning

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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