high performance computing on graphics processing units: hgpu.org

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Efficient Calculation of Pairwise Nonbonded Forces

Matt Chiu, Md. Ashfaquzzaman Khan, Martin C. Herbordt

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Tags: Chemistry, Electrostatics, FPGA, Molecular dynamics, Optimization, Performance

August 15, 2011 by hgpu

FPGA acceleration of rigid-molecule docking codes

Bharat Sukhwani, Martin C. Herbordt

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Tags: Biology, Biomolecules, CUDA, FPGA, Molecular modeling, nVidia, Performance, Tesla C1060

August 15, 2011 by hgpu

Performance Comparison with OpenMP Parallelization for Multi-core Systems

Chao-Tung Yang, Tzu-Chieh Chang, Hsien-Yi Wang, William C.C. Chu, Chih-Hung Chang

Tags: Code generation, Compilers, Computer science, CUDA, Data parallelism, Fortran, nVidia, OpenCL, OpenMP, Performance

August 8, 2011 by hgpu

The distributed diagonal force decomposition method for parallelizing molecular dynamics simulations

Urban Borstnik, Benjamin T. Miller, Bernard R. Brooks, Dusanka Janezic

Tags: Algorithms, Chemistry, Computational chemistry, Molecular dynamics, Performance

August 8, 2011 by hgpu

Hardware accelerators for biocomputing: A survey

Souradip Sarkar, Turbo Majumder, Ananth Kalyanaraman, Partha Pratim Pande

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Tags: Biology, Cell processor, CUDA, FPGA, Molecular dynamics, nVidia, nVidia GeForce 7800 GTX, Performance, Review, Sequence alignment

August 7, 2011 by hgpu

Efficient MIMD architectures for high-performance ray tracing

D. Kopta, J. Spjut, E. Brunvand, A. Davis

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Tags: Algorithms, Computer science, nVidia, nVidia GeForce GTX 285, Performance, Raytracing

August 4, 2011 by hgpu

PIR: PMaC’s Idiom Recognizer

Catherine Olschanowsky, Allan Snavely, Mitesh R. Meswani, Laura Carrington

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Tags: Algorithms, Computer science, Optimization, Pattern recognition, Performance

August 3, 2011 by hgpu

Hyperfast Parallel–Beam Backprojection

Marc Kachelriess, Michael Knaup, Olivier Bockenbach

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Tags: Algorithms, Cell processor, Computer science, Data mining, Distributed computing, Image reconstruction, Nearest neighbour, Performance, Tomography

August 2, 2011 by hgpu

Performance Debugging of GPGPU Applications with the Divergence Map

Bruno Coutinho, Diogo Sampaio, Fernando M. Q. Pereira, Wagner Meira Jr.

Tags: Code generation, Compilers, Computer science, CUDA, nVidia, nVidia GeForce GTX 260, Performance, PTX

August 1, 2011 by hgpu

Towards a More Efficient Use of GPUs

Pedro Valero, Fernando L. Pelayo

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Tags: Computer science, nVidia, nVidia GeForce 9600 M GT, nVidia GeForce GTX 285, Performance, Task scheduling

July 30, 2011 by hgpu

Tracking 3d Pose of Rigid Object by Sparse Template Matching

Yuki Oka, Toshiyuki Kuroda, Tsuyoshi Migita, Takeshi Shakunaga

Tags: Computer science, Computer vision, Particle filtering, Performance, Video tracking

July 29, 2011 by hgpu

FPGA Accelerated Simulation of Biologically Plausible Spiking Neural Networks

David B. Thomas, Wayne Luk

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Tags: Computer science, CUDA, FPGA, Neural networks, nVidia, nVidia GeForce GT 200, Performance

July 29, 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

Efficient Calculation of Pairwise Nonbonded Forces

FPGA acceleration of rigid-molecule docking codes

Performance Comparison with OpenMP Parallelization for Multi-core Systems

The distributed diagonal force decomposition method for parallelizing molecular dynamics simulations

Hardware accelerators for biocomputing: A survey

Efficient MIMD architectures for high-performance ray tracing

PIR: PMaC’s Idiom Recognizer

Hyperfast Parallel–Beam Backprojection

Performance Debugging of GPGPU Applications with the Divergence Map

Towards a More Efficient Use of GPUs

Tracking 3d Pose of Rigid Object by Sparse Template Matching

FPGA Accelerated Simulation of Biologically Plausible Spiking Neural Networks

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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