Programming

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Dynamic Sparse-Matrix Allocation on GPUs

James King, Thomas Gilray, Robert M. Kirby, Matthew Might

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Tags: Benchmarking, Computer science, CUDA, Factorization, Linear Algebra, nVidia, Sparse matrix, Tesla K20

April 3, 2016 by hgpu

A Novel CSR-Based Sparse Matrix-Vector Multiplication on GPUs

Guixia He, Jiaquan Gao

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Tags: Algorithms, Computer science, CUDA, Linear Algebra, nVidia, Sparse matrix, Tesla C2050

March 29, 2016 by hgpu

A generalized GPU-based connected component labeling algorithm

Yukihiro Komura

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Tags: Algorithms, Computational Physics, CUDA, nVidia, nVidia GeForce GTX Titan X, Physics

March 29, 2016 by hgpu

Generic Inverted Index on the GPU

Jingbo Zhou, Qi Guo, H. V. Jagadish, Wenhao Luan, Anthony K. H. Tung, Yueji Yang, Yuxin Zheng

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Tags: Computer science, CUDA, Databases, Hashing, Nearest neighbour, nVidia, nVidia GeForce GTX Titan X, Tesla K80

March 29, 2016 by hgpu

A Stencil DSEL for Single Code Accelerated Computing with SYCL

Olivier Aumage, Denis Barthou, Alexandre Honorat

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Tags: Computer science, OpenCL, Stencil computation, SYCL

March 29, 2016 by hgpu

GPU Computing in Bayesian Inference of Realized Stochastic Volatility Model

Tetsuya Takaishi

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Tags: Bayesian, Computational finance, CUDA, Finance, Fortran, nVidia, nVidia GeForce GTX 760, OpenACC

March 29, 2016 by hgpu

Efficient Exact Gradient Update for training Deep Networks with Very Large Sparse Targets

Pascal Vincent, Alexandre de Brebisson, Xavier Bouthillier

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Tags: Algorithms, Computer science, CUDA, Deep learning, Neural networks, nVidia, nVidia GeForce GTX Titan Black, Package

March 25, 2016 by hgpu

Wanted: Floating-Point Add Round-off Error instruction

Marat Dukhan, Richard Vuduc, Jason Riedy

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Tags: Computer science, CUDA, Matrix multiplication, OpenCL, Package

March 25, 2016 by hgpu

An Efficient Implementation of the Longest Common Subsequence Algorithm with Bit-Parallelism on GPUs

Katsuya Kawanami

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce 8800 GTX, nVidia GeForce GTX 580, Thesis

March 25, 2016 by hgpu

Accelerating Deep Neural Network Training with Inconsistent Stochastic Gradient Descent

Linnan Wang, Yi Yang, Martin Renqiang Min, Srimat Chakradhar

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Tags: Caffe, Computer science, CUDA, Data parallelism, Deep learning, Heterogeneous systems, Machine learning, Neural networks, nVidia, nVidia GeForce GTX Titan X

March 25, 2016 by hgpu

A mixed precision semi-Lagrangian algorithm and its performance on accelerators

Lukas Einkemmer

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Tags: Computational Physics, Computer science, CUDA, Intel Xeon Phi, Mathematical Software, Mixed precision, nVidia, OpenMP, Tesla K80

March 25, 2016 by hgpu

Comparison of Technologies for General-Purpose Computing on Graphics Processing Units

Torbjorn Sorman

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Tags: AMD Radeon R7 260X, ATI, Benchmarking, Computer science, DirectCompute, DirectX, nVidia, nVidia GeForce GTX 670, OpenCL, OpenGL, Performance, Thesis

March 22, 2016 by hgpu

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

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

CONCUR: a benchmark designed to evaluate multithreaded Java code generated by LLMs

CONCUR: Benchmarking LLMs for Concurrent Code Generation

HIPRT: Ray Tracing using HIP

Ray Tracing using HIP

MXFP4 Training Support Codebase

Practical FP4 Training for Large-Scale MoE Models on Hopper GPUs

CUDA Agent: Large-Scale Agentic RL for High-Performance CUDA Kernel Generation

CUDABench: Benchmarking LLMs for Text-to-CUDA Generation

See all packages

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high performance computing on graphics processing units: hgpu.org

Programming

Dynamic Sparse-Matrix Allocation on GPUs

A Novel CSR-Based Sparse Matrix-Vector Multiplication on GPUs

A generalized GPU-based connected component labeling algorithm

Generic Inverted Index on the GPU

A Stencil DSEL for Single Code Accelerated Computing with SYCL

GPU Computing in Bayesian Inference of Realized Stochastic Volatility Model

Efficient Exact Gradient Update for training Deep Networks with Very Large Sparse Targets

Wanted: Floating-Point Add Round-off Error instruction

An Efficient Implementation of the Longest Common Subsequence Algorithm with Bit-Parallelism on GPUs

Accelerating Deep Neural Network Training with Inconsistent Stochastic Gradient Descent

A mixed precision semi-Lagrangian algorithm and its performance on accelerators

Comparison of Technologies for General-Purpose Computing on Graphics Processing Units

Recent source codes

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

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

CONCUR: a benchmark designed to evaluate multithreaded Java code generated by LLMs

HIPRT: Ray Tracing using HIP

MXFP4 Training Support Codebase

CUDA Agent: Large-Scale Agentic RL for High-Performance CUDA Kernel Generation

CUDABench: Benchmarking LLMs for Text-to-CUDA Generation

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