high performance computing on graphics processing units: hgpu.org

Applications

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IPMACC: Translating OpenACC API to OpenCL

Ahmad Lashgar, Alireza Majidi, Amirali Baniasadi

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Tags: Code generation, Computer science, CUDA, nVidia, OpenACC, OpenCL, Package, Tesla K20

May 3, 2015 by hgpu

PyTransit: Fast and Easy Exoplanet Transit Modelling in Python

Hannu Parviainen

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Tags: Astrophysics, Earth and Planetary Astrophysics, OpenCL, Package, Python

May 3, 2015 by hgpu

Speculative Segmented Sum for Sparse Matrix-Vector Multiplication on Heterogeneous Processors

Weifeng Liu, Brian Vinter

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Tags: ATI, Computer science, CUDA, Intel HD 5500, Mathematical Software, Numerical Analysis, nVidia, nVidia Jetson TK1, OpenCL, Package, Sparse matrix

April 27, 2015 by hgpu

Algorithm 9xx: Sparse QR Factorization on the GPU

Sencer Nuri Yeralan, Timothy A. Davis, Sanjay Ranka

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Tags: Algorithms, Computer science, CUDA, Factorization, Linear Algebra, nVidia, Package, Sparse matrix, Tesla C2070

April 25, 2015 by hgpu

A Framework for General Sparse Matrix-Matrix Multiplication on GPUs and Heterogeneous Processors

Weifeng Liu, Brian Vinter

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Tags: AMD Radeon R9 290X, ATI, Computer science, CUDA, Mathematical Software, Matrix multiplication, Numerical Analysis, nVidia, nVidia GeForce GTX 980, nVidia GeForce GTX Titan, OpenCL, Package, Path problems, Sparse matrix

April 23, 2015 by hgpu

Verification of Producer-Consumer Synchronization in GPU Programs

Rahul Sharma, Michael Bauer, Alex Aiken

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Tags: Computer science, CUDA, nVidia, Package, PTX

April 20, 2015 by hgpu

Optimizing ASP.NET with C++ AMP on the GPU

Scott Zimmerman

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Tags: C++ AMP, Cloud, Computer science, Matrix multiplication, nVidia, nVidia GRID K520, Package

April 17, 2015 by hgpu

NBODY6++GPU: Ready for the gravitational million-body problem

Long Wang, Rainer Spurzem, Sverre Aarseth, Keigo Nitadori, Peter Berczik, M.B.N. Kouwenhoven, Thorsten Naab

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Tags: Astrophysics, CUDA, Instrumentation and Methods for Astrophysics, nVidia, Package, Tesla K20

April 17, 2015 by hgpu

Model Coupling between the Weather Research and Forecasting Model and the DPRI Large Eddy Simulator for Urban Flows on GPU-accelerated Multicore Systems

Wim Vanderbauwhede

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Tags: Earth and Space Sciences, nVidia, nVidia GeForce GTX 480, OpenCL, Package, Weather prediction, Weather Prediction Model

April 12, 2015 by hgpu

GPU Accelerated Strong and Branching Bisimilarity Checking

Anton Wijs

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Tags: Algorithms, Computer science, CUDA, nVidia, Package, Tesla K20

April 8, 2015 by hgpu

Flexible, Fast and Accurate Sequence Alignment Profiling on GPGPU with PaSWAS

Sven Warris, Feyruz Yalcin, Katherine J. L. Jackson, Jan Peter Nap

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Tags: Bioinformatics, Biology, CUDA, nVidia, nVidia GeForce GTX 285, nVidia GeForce GTX 295, Package, Sequence alignment, Smith-Waterman algorithm, Tesla C1060

April 7, 2015 by hgpu

The Design and Implementation of a Verification Technique for GPU Kernels

Adam Betts, Nathan Chong, Alastair F. Donaldson, Jeroen Ketema, Shaz Qadeer, Paul Thomson, John Wickerson

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Tags: ARM, ATI, ATI Radeon HD 7970, Computer science, CUDA, nVidia, OpenCL, Package, Tesla C2050

April 4, 2015 by hgpu

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

True 4-Bit Quantized CNN Training on CPU

True 4-Bit Quantized Convolutional Neural Network Training on CPU: Achieving Full-Precision Parity

cuFuzz: A GPU-oriented coverage-guided fuzzer for userland CUDA application

Hunting CUDA Bugs at Scale with cuFuzz

KernelSkill: A Multi-Agent Framework for GPU Kernel Optimization

MSKernelBench & CUDAMaster

Making LLMs Optimize Multi-Scenario CUDA Kernels Like Experts

EvoScientist: Harness Vibe Research with Self-evolving AI Scientists

EvoScientist: Towards Multi-Agent Evolving AI Scientists for End-to-End Scientific Discovery

RepoLaunch: Automating Build and Test Pipeline of Code Repositories on ANY Language and ANY Platform

RepoLaunch: Automating Build & Test Pipeline of Code Repositories on ANY Language and ANY Platform

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high performance computing on graphics processing units: hgpu.org

Applications

IPMACC: Translating OpenACC API to OpenCL

PyTransit: Fast and Easy Exoplanet Transit Modelling in Python

Speculative Segmented Sum for Sparse Matrix-Vector Multiplication on Heterogeneous Processors

Algorithm 9xx: Sparse QR Factorization on the GPU

A Framework for General Sparse Matrix-Matrix Multiplication on GPUs and Heterogeneous Processors

Verification of Producer-Consumer Synchronization in GPU Programs

NBODY6++GPU: Ready for the gravitational million-body problem

Model Coupling between the Weather Research and Forecasting Model and the DPRI Large Eddy Simulator for Urban Flows on GPU-accelerated Multicore Systems

GPU Accelerated Strong and Branching Bisimilarity Checking

Flexible, Fast and Accurate Sequence Alignment Profiling on GPGPU with PaSWAS

The Design and Implementation of a Verification Technique for GPU Kernels

Recent source codes

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

MSKernelBench & CUDAMaster

EvoScientist: Harness Vibe Research with Self-evolving AI Scientists

RepoLaunch: Automating Build and Test Pipeline of Code Repositories on ANY Language and ANY Platform

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