high performance computing on graphics processing units: hgpu.org

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Performance Analysis of the OP2 Framework on Many-core Architectures

M.B. Giles, G.R. Mudalige, Z. Sharif, G. Markall and P.H.J Kelly

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Tags: CUDA, Fluid dynamics, GPU cluster, nVidia, nVidia GeForce GTX 260, OpenMP, Package, Performance, Tesla C2050

June 5, 2011 by hgpu

Mixed-Tool Performance Analysis on Hybrid Multicore Architectures

Peng Du, Piotr Luszczek, Stanimire Tomov, Jack Dongarra

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Tags: BLAS, Computer science, CUDA, Linear Algebra, nVidia, nVidia GeForce GTX 280, Performance

June 3, 2011 by hgpu

Distributed time, conservative parallel logic simulation on GPUs

Bo Wang, Yuhao Zhu, Yangdong Deng

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Tags: Computer science, CUDA, Electronic design automation, Hardware Architecture, nVidia, nVidia GeForce GTX 280, Performance

May 31, 2011 by hgpu

Parallel ant colony for nonlinear function optimization with graphics hardware acceleration

Weihang Zhu, J. Curry

Tags: Algorithms, Computer science, Nonlinear optimization, Optimization, Pattern Search, Performance

May 30, 2011 by hgpu

A Compute Unified System Architecture for Graphics Clusters Incorporating Data Locality

Christoph Muller, Steffen Frey, Magnus Strengert, Carsten Dachsbacher, Thomas Ertl

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Tags: Computer science, CUDA, GPU cluster, nVidia, nVidia GeForce 8800 GT, nVidia GeForce 8800 GTX, nVidia GeForce GTX 280, nVidia Quadro FX 5600, Performance

May 30, 2011 by hgpu

High performance comparison-based sorting algorithm on many-core GPUs

Xiaochun Ye, Dongrui Fan, Wei Lin, Nan Yuan, Paolo Ienne

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce 9800 GTX, Performance, Sorting

May 30, 2011 by hgpu

Performance Acceleration of Kernel Polynomial Method Applying Graphics Processing Units

Shixun Zhang, Shinichi Yamagiwa, Masahiko Okumura, Seiji Yunoki

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Tags: Computational Physics, Condensed matter, CUDA, nVidia, Performance, Physics, Tesla C2050

May 30, 2011 by hgpu

Performance comparison of GPU and FPGA architectures for the SVM training problem

Markos Papadonikolakis, Christos-Savvas Bouganis, George Constantinides

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Tags: Computer science, CUDA, FPGA, Machine learning, nVidia, nVidia GeForce 8500 GT, Performance

May 29, 2011 by hgpu

A Summary of Recent GPU Developments and Key Enabling Technologies for Digital Media Applications

I.M. Williams

Tags: Computer science, Performance, Review

May 29, 2011 by hgpu

Efficent multiple pass, multiple output algorithms on the GPU

Jay L. T. Cornwall

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Tags: Algorithms, Computer science, nVidia, nVidia GeForce 6800 GT, OpenGL, Performance, Programming techniques

May 29, 2011 by hgpu

Reducing IO bandwidth for GPU based moment invariant classifier systems

C.H. Messom, A.L.C. Barczak

Tags: Computer science, Image processing, Performance

May 29, 2011 by hgpu

GPU-enabled high performance feature modeling for ATR applications

M.P. Dessauer, J. Hitchens, S. Dua

Tags: Algorithms, Computer science, Computer vision, Performance

May 27, 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

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high performance computing on graphics processing units: hgpu.org

Performance Analysis of the OP2 Framework on Many-core Architectures

Mixed-Tool Performance Analysis on Hybrid Multicore Architectures

Distributed time, conservative parallel logic simulation on GPUs

Parallel ant colony for nonlinear function optimization with graphics hardware acceleration

A Compute Unified System Architecture for Graphics Clusters Incorporating Data Locality

High performance comparison-based sorting algorithm on many-core GPUs

Performance Acceleration of Kernel Polynomial Method Applying Graphics Processing Units

Performance comparison of GPU and FPGA architectures for the SVM training problem

A Summary of Recent GPU Developments and Key Enabling Technologies for Digital Media Applications

Efficent multiple pass, multiple output algorithms on the GPU

Reducing IO bandwidth for GPU based moment invariant classifier systems

GPU-enabled high performance feature modeling for ATR applications

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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