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Performance comparison of gauss-Jordan elimination method using OpenMP and CUDA

Baha Sen, Nesrin Aydin Atasoy, Caner Ozcan

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Tags: Computer science, CUDA, nVidia, nVidia GeForce 9500 GT, OpenMP, Performance, Programming techniques

January 7, 2013 by hgpu

uBench: Performance Impact of CUDA Block Geometry

Yuri Torres, Arturo Gonzalez, Diego R. Llanos

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Tags: Benchmarking, Computer science, CUDA, nVidia, nVidia GeForce GTX 480, nVidia GeForce GTX 680, Performance

January 3, 2013 by hgpu

KFusion: Obtaining Modularity and Performance with Regards to General Purpose GPU Computing and Co-processors

Liam Kiemele

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Tags: Compilers, Computer science, nVidia, OpenCL, Package, Performance, Tesla C2075, Thesis

December 20, 2012 by hgpu

An Automatic Host and Device Memory Allocation Method for OpenMPC

Hiroaki Uchiyama, Tomoaki Tsumura, Hiroshi Matsuo

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Tags: Computer science, CUDA, Memory model, nVidia, nVidia GeForce GTX 280, OpenMP, Performance, Tesla C1060

December 20, 2012 by hgpu

Productive High Performance Parallel Programming with Auto-tuned Domain-Specific Embedded Languages

Shoaib Ashraf Kamil

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 280, Performance, Programming techniques, Thesis

December 16, 2012 by hgpu

Vectorized Higher Order Finite Difference Kernels

Gerhard Zumbusch

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Tags: Algorithms, ATI, ATI Radeon HD 6990, ATI Radeon HD 7970, Computer science, Finite difference, nVidia, nVidia GeForce GTX 590, nVidia GeForce GTX 680, OpenCL, Performance, Tesla C2050, Tesla K20

December 10, 2012 by hgpu

Neither More Nor Less: Optimizing Thread-level Parallelism for GPGPUs

Onur Kayiran, Adwait Jog, Mahmut T. Kandemir, Chita R. Das

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Tags: Computer science, CUDA, nVidia, Performance

December 10, 2012 by hgpu

OpenMP Programming on Intel R Xeon Phi TM Coprocessors: An Early Performance Comparison

Tim Cramer, Dirk Schmidl, Michael Klemm, Dieter an Mey

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Tags: Benchmarking, Computer science, Intel, Intel Phi, OpenMP, Performance

December 8, 2012 by hgpu

Lightweight Modular Staging and Embedded Compilers: Abstraction Without Regret for High-Level High-Performance Programming

Tiark Rompf

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Tags: Code generation, Compilers, Computer science, CUDA, Heterogeneous systems, nVidia, Performance, Software Engineering, Tesla C2050, Thesis

December 8, 2012 by hgpu

FusionSim: Characterizing the Performance Benefits of Fused CPU/GPU Systems

Vitaly Zakharenko

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Tags: AMD Fusion, Analytical model, Benchmarking, Computer science, Package, Performance, Thesis

December 4, 2012 by hgpu

Characterization and Performance Analysis for 3D Benchmarks

Joseph Issa

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Tags: Analytical model, ATI, ATI Radeon HD 3470, Benchmarking, Computer science, nVidia, nVidia GeForce 8800 GTX, nVidia GeForce 9400 GT, nVidia GeForce 9500 GT, Performance

November 25, 2012 by hgpu

Location-based Matching in Publish/Subscribe Revisited

Mohammad Sadoghi, Hans-Arno Jacobsen

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Tags: Algorithms, Computer science, CUDA, Filtering, nVidia, nVidia GeForce GTX 460, Performance

November 25, 2012 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

Performance comparison of gauss-Jordan elimination method using OpenMP and CUDA

uBench: Performance Impact of CUDA Block Geometry

KFusion: Obtaining Modularity and Performance with Regards to General Purpose GPU Computing and Co-processors

An Automatic Host and Device Memory Allocation Method for OpenMPC

Productive High Performance Parallel Programming with Auto-tuned Domain-Specific Embedded Languages

Vectorized Higher Order Finite Difference Kernels

Neither More Nor Less: Optimizing Thread-level Parallelism for GPGPUs

OpenMP Programming on Intel R Xeon Phi TM Coprocessors: An Early Performance Comparison

Lightweight Modular Staging and Embedded Compilers: Abstraction Without Regret for High-Level High-Performance Programming

FusionSim: Characterizing the Performance Benefits of Fused CPU/GPU Systems

Characterization and Performance Analysis for 3D Benchmarks

Location-based Matching in Publish/Subscribe Revisited

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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