high performance computing on graphics processing units: hgpu.org

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Impact of Floating-Point Precision on Boundary Layer Instabilities Modeled on Fermi GPU

David A. Sanchez, David A. Yuen, Yujun Sun, Grady B. Wright

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Tags: CUBLAS, CUDA, Earth and Space Sciences, Geoscience, nVidia, Tesla C2070

December 3, 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

High Rayleigh Number Mantle Convection on GPU

David A. Sanchez, Christopher Gonzalez, David A. Yuen, Grady B. Wright, Gregory A. Barnett

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Tags: CUBLAS, CUDA, Earth and Space Sciences, Finite difference, nVidia, Tesla C2070

November 25, 2011 by hgpu

Optimizing Symmetric Dense Matrix-Vector Multiplication on GPUs

Rajib Nath, Stanimire Tomov, Tingxing "Tim" Dong, Jack Dongarra

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Tags: Algorithms, Computer science, CUBLAS, CUDA, Linear Algebra, nVidia, nVidia GeForce GTX 280, Package, Performance, Tesla C2050

November 20, 2011 by hgpu

Sustainable GPU Computing at Scale

Justin Y. Shi, Moussa Taifi, Abdallah Khreishah, Jie Wu

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Tags: Computer science, CUBLAS, CUDA, Energy-efficient computing, Fault tolerance, nVidia, OpenMPI, Tesla S1070

November 12, 2011 by hgpu

Acceleration of Hessenberg Reduction for Nonsymmetric Eigenvalue Problems in a Hybrid CPU-GPU Computing Environment

Jun-ichi Muramatsu, Takeshi Fukaya, Shao-Liang Zhang, Kinji Kimura, Yusaku Yamamoto

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Tags: Algorithms, Computer science, CUBLAS, CUDA, Linear Algebra, nVidia, Tesla C1060

November 8, 2011 by hgpu

Quantum chemical many-body theory on heterogeneous nodes

A. Eugene DePrince, Jeff R. Hammond

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Tags: Algorithms, Computational chemistry, CUBLAS, CUDA, Heterogeneous systems, nVidia, Presentation, Tesla C1060, Tesla C2050

November 1, 2011 by hgpu

Fast Implementation of DGEMM on Fermi GPU

Guangming Tan, Linchuan Li, Sean Triechle, Everett Phillips, Yungang Bao, Ninghui Sun

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Tags: Algorithms, Benchmarking, Computer science, CUBLAS, CUDA, Matrix multiplication, nVidia, Optimization, Performance, Tesla C2050

October 17, 2011 by hgpu

Collaborative execution environment for heterogeneous parallel systems

Aleksandar Ilic, Leonel Sousa

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Tags: Computer science, CUBLAS, CUDA, Data parallelism, FFT, Heterogeneous systems, Matrix multiplication, nVidia, nVidia GeForce GTX 285, Presentation

August 28, 2011 by hgpu

A mapping path for multi-GPGPU accelerated computers from a portable high level programming abstraction

Allen Leung, Nicolas Vasilache, Benoit Meister, Muthu Baskaran, David Wohlford, Cedric Bastoul, Richard Lethin

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Tags: Algorithms, Code generation, Compilers, Computer science, CUBLAS, CUDA, nVidia, nVidia GeForce 9800 GX2, nVidia GeForce GTX 285, Programming Languages

August 24, 2011 by hgpu

Accelerating Haskell array codes with multicore GPUs

Manuel M.T. Chakravarty, Gabriele Keller, Sean Lee, Trevor L. McDonell, Vinod Grover

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Tags: Code generation, Computer science, CUBLAS, CUDA, Data parallelism, nVidia, Performance, Programming Languages, Tesla T10

August 22, 2011 by hgpu

Using Graphics Processors to Accelerate the Solution of Out-of-Core Linear Systems

Mercedes Marques, Gregorio Quintana-Orti, Enrique S. Quintana-Orti, Robert van de Geijn

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Tags: Computer science, CUBLAS, CUDA, Factorization, Linear Algebra, nVidia, nVidia GeForce 9800 GX2

August 4, 2011 by hgpu

KernelBenchX: A Comprehensive Benchmark for Evaluating LLM-Generated GPU Kernels

CuTile Benchmark Suite: Performance and Productivity Tradeoffs for GPU Kernel Programming on Blackwell Architecture

Evaluating CUDA Tile for AI Workloads on Hopper and Blackwell GPUs

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

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

LLMQ: Efficient Lower-Precision LLM Training for Consumer GPUs

See all packages

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high performance computing on graphics processing units: hgpu.org

Impact of Floating-Point Precision on Boundary Layer Instabilities Modeled on Fermi GPU

A programming language interface to describe transformations and code generation

High Rayleigh Number Mantle Convection on GPU

Optimizing Symmetric Dense Matrix-Vector Multiplication on GPUs

Sustainable GPU Computing at Scale

Acceleration of Hessenberg Reduction for Nonsymmetric Eigenvalue Problems in a Hybrid CPU-GPU Computing Environment

Quantum chemical many-body theory on heterogeneous nodes

Fast Implementation of DGEMM on Fermi GPU

Collaborative execution environment for heterogeneous parallel systems

A mapping path for multi-GPGPU accelerated computers from a portable high level programming abstraction

Accelerating Haskell array codes with multicore GPUs

Using Graphics Processors to Accelerate the Solution of Out-of-Core Linear Systems

Recent source codes

KernelBenchX: A Comprehensive Benchmark for Evaluating LLM-Generated GPU Kernels

CUDA Kernel Fusion Benchmarks

IntelliKit: Agent-first tooling for AMD hardware

DITRON: Distributed Compiler based on Triton for Parallel Systems

CuTile Benchmark Suite: Performance and Productivity Tradeoffs for GPU Kernel Programming on Blackwell Architecture

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

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

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