high performance computing on graphics processing units: hgpu.org

hgpu.org » Performance

Real-time Semi-Global Matching on the CPU

Stefan K. Gehrig, Clemens Rabe

Tags: Algorithms, Computer science, Computer vision, FPGA, nVidia, nVidia GeForce 8800 Ultra, Performance, Stereo vision

July 28, 2011 by hgpu

Bridging the Gap between FPGAs and Multi-Processor Architectures: A Video Processing Perspective

Ben Cope, Peter Y.K. Cheung, Wayne Luk

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Tags: Algorithms, Cg, Computer science, FPGA, GLSL, nVidia, nVidia GeForce 7800 GTX, OpenGL, Performance

July 24, 2011 by hgpu

Exploring graphics processor performance for general purpose applications

Pedro Trancoso, Maria Charalambous

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Tags: Brook, Computer science, nVidia, nVidia GeForce FX 5700, Performance, Review

July 23, 2011 by hgpu

A History-Based Performance Prediction Model with Profile Data Classification for Automatic Task Allocation in Heterogeneous Computing Systems

Katsuto Sato, Kazuhiko Komatsu, Hiroyuki Takizawa, Hiroaki Kobayashi

Tags: Computer science, Heterogeneous systems, OpenCL, Performance, Programming techniques

July 22, 2011 by hgpu

Parallel simulation of Petri nets on desktop PC hardware

Robert Geist, Jacob Hicks, Mark Smotherman, James Westall

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Source codes

Tags: Algorithms, Cg, Computer science, nVidia, nVidia GeForce 6800 Ultra, nVidia GeForce FX 5950, Package, Performance

July 18, 2011 by hgpu

Linpack evaluation on a supercomputer with heterogeneous accelerators

Toshio Endo, Akira Nukada, Satoshi Matsuoka, Naoya Maruyama

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Tags: Benchmarking, Computer science, CUBLAS, CUDA, Heterogeneous systems, Linear Algebra, nVidia, Performance, Tesla S1070

July 17, 2011 by hgpu

Workload Characterization of 3D Games

Jordi Roca, Victor Moya, Carlos Gonzalez, Chema Solis, Agustin Fernandez, Roger Espasa

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Tags: ATI, ATI Radeon 9800, Computer science, DirectX, Games, OpenGL, Performance

July 17, 2011 by hgpu

A Sparse Matrix Personality for the Convey HC-1

Krishna K. Nagar, Jason D. Bakos

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Tags: Computer science, CUDA, FPGA, nVidia, Performance, Sparse matrix, Tesla S1070

July 14, 2011 by hgpu

Efficient visual hull computation for real-time 3D reconstruction using CUDA

Alexander Ladikos, Selim Benhimane, Nassir Navab

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Tags: Computer science, CUDA, Image reconstruction, nVidia, nVidia GeForce 8800 GTS, nVidia GeForce 8800 GTX, Performance, Real-time graphics

July 14, 2011 by hgpu

Option pricing with multi-dimensional quadrature architectures

Anson H.T. Tse, David B. Thomas, Wayne Luk

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Tags: Finance, FPGA, nVidia, nVidia GeForce 8600 GT, Performance, Tesla C1060

July 14, 2011 by hgpu

MATLAB Parallelization through Scalarization

Chun-Yu Shei, Adarsh Yoga, Madhav Ramesh, Arun Chauhan

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Tags: Compilers, Computer science, CUDA, nVidia, Performance, Tesla C1060

July 11, 2011 by hgpu

Accelerating Lossless Data Compression with GPUs

R. L. Cloud, M. L. Curry, H. L. Ward, A. Skjellum, P. Bangalore

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Tags: Algorithms, Compression, Computer science, CUDA, nVidia, nVidia GeForce GTX 285, Performance

July 11, 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

Real-time Semi-Global Matching on the CPU

Bridging the Gap between FPGAs and Multi-Processor Architectures: A Video Processing Perspective

Exploring graphics processor performance for general purpose applications

A History-Based Performance Prediction Model with Profile Data Classification for Automatic Task Allocation in Heterogeneous Computing Systems

Parallel simulation of Petri nets on desktop PC hardware

Linpack evaluation on a supercomputer with heterogeneous accelerators

Workload Characterization of 3D Games

A Sparse Matrix Personality for the Convey HC-1

Efficient visual hull computation for real-time 3D reconstruction using CUDA

Option pricing with multi-dimensional quadrature architectures

MATLAB Parallelization through Scalarization

Accelerating Lossless Data Compression with GPUs

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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