high performance computing on graphics processing units: hgpu.org

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Implementing a Sparse Matrix Vector Product for the SELL-C/SELL-C-sigma formats on NVIDIA GPUs

Hartwig Anzt, Stanimire Tomov, Jack Dongarra

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Tags: Algorithms, Computer science, CUDA, Linear Algebra, nVidia, Sorting, Sparse matrix, Tesla K40

April 7, 2014 by hgpu

Optimizing Krylov Subspace Solvers on Graphics Processing Units

Hartwig Anzt, Stanimire Tomov, Piotr Luszczek, Ichitaro Yamazaki, Jack Dongarra, William Sawyer

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

April 6, 2014 by hgpu

A Low-Power Hybrid CPU-GPU Sort

Lawrence Tan

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia ION, Sorting, Thesis

April 6, 2014 by hgpu

High Performance Computing for Large Graphs of Internet Applications using GPU

Jia Uddin, Emmanuel Oyekanlu, Cheol-Hong Kim, Jong-Myon Kim

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Tags: Computer science, CUDA, Network communications, nVidia, nVidia GeForce GT 630 M, Software router

April 6, 2014 by hgpu

Parallel Support Vector Machines in Practice

Stephen Tyree, Jacob R. Gardner, Kilian Q. Weinberger, Kunal Agrawal, John Tran

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Tags: Algorithms, Benchmarking, Computer science, CUDA, Linear Algebra, Machine learning, nVidia, Package, Tesla C2075

April 6, 2014 by hgpu

An efficient GPU acceptance-rejection algorithm for the selection of the next reaction to occur for Stochastic Simulation Algorithms

Neri Mickael, Denis Mestivier

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Tags: Algorithms, Biology, Computer science, CUDA, nVidia, Optimization, Stochastic simulation, Tesla K10, Tesla K20, Tesla M2075, Tesla M2090

April 4, 2014 by hgpu

Petascale computations for Large-scale Atomic and Molecular collisions

Brendan M McLaughlin, Connor P Ballance

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Tags: BLAS, Computational Physics, Computer science, CUDA, Fortran, Molecular simulation, MPI, nVidia, Tesla K20

April 4, 2014 by hgpu

Optimising OpenCL kernels for the ARM Mali-T600 GPUs

Johan Gronqvist, Anton Lokhmotov

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Tags: Algorithms, ARM, Computer science, Matrix multiplication, OpenCL

March 18, 2014 by hgpu

A Unified Approach for Registration and Depth in Depth from Defocus

Rami Ben-Ari

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 460, Stereo vision, Thesis

March 18, 2014 by hgpu

Programming Frameworks for Distributed Smartphone Computing

Roelof Kemp

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Tags: Computer science, CUDA, Distributed computing, nVidia, nVidia Tegra, Thesis

March 18, 2014 by hgpu

Dynamic Load Balancing using Graphics Processors

R. Mohan, N. P. Gopalan

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Tags: Computer science, CUDA, nVidia, Performance, Task scheduling

March 18, 2014 by hgpu

Towards a Unified Sentiment Lexicon Based on Graphics Processing Units

Liliana Ibeth Barbosa-Santillan, Inmaculada Alvarez-de-Mon y-Rego

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia Quadro 600, Tesla C2075

March 18, 2014 by hgpu

Agentic Code Optimization via Compiler-LLM Cooperation

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

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

SOL-ExecBench: Speed-of-Light Benchmarking for Real-World GPU Kernels Against Hardware Limits

Triton-Sanitizer: A Fast and Device-Agnostic Memory Sanitizer for Triton with Rich Diagnostic Context

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high performance computing on graphics processing units: hgpu.org

Implementing a Sparse Matrix Vector Product for the SELL-C/SELL-C-sigma formats on NVIDIA GPUs

Optimizing Krylov Subspace Solvers on Graphics Processing Units

A Low-Power Hybrid CPU-GPU Sort

High Performance Computing for Large Graphs of Internet Applications using GPU

Parallel Support Vector Machines in Practice

An efficient GPU acceptance-rejection algorithm for the selection of the next reaction to occur for Stochastic Simulation Algorithms

Petascale computations for Large-scale Atomic and Molecular collisions

Optimising OpenCL kernels for the ARM Mali-T600 GPUs

A Unified Approach for Registration and Depth in Depth from Defocus

Programming Frameworks for Distributed Smartphone Computing

Dynamic Load Balancing using Graphics Processors

Towards a Unified Sentiment Lexicon Based on Graphics Processing Units

Recent source codes

Agentic Code Optimization via Compiler-LLM Cooperation

MegaTrain: Full Precision Training of 100B+ Parameter Large Language Models on a Single GPU

Device Virtual Machine (DVM)

AutoKernel: Autoresearch for GPU kernels. Give it any PyTorch model, go to sleep, wake up to optimized Triton kernels

SOL-ExecBench: Speed-of-Light Benchmarking for Real-World GPU Kernels Against Hardware Limits

Triton-Sanitizer: A Fast and Device-Agnostic Memory Sanitizer for Triton with Rich Diagnostic Context

LLM.Q: Quantized LLM training in pure CUDA/C++

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

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