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Comparison of OpenCL performance on different platforms using VexCL and Blaze

Hammad Mazhar, Dan Negrut

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Tags: AMD Radeon R7 260, ATI, Benchmarking, Computer science, Intel Xeon Phi, nVidia, nVidia GeForce GTX 680, nVidia GeForce GTX 770, OpenCL, Performance, Sparse matrix, Tesla K20

February 6, 2015 by hgpu

Thorough Evaluation of GPU Shared Memory Load and Store Instructions

Satoshi Okamoto, Yasuaki Ito, Koji Nakano, Jacir L. Bordim

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Tags: Benchmarking, Computer science, CUDA, nVidia, nVidia GeForce GTX 780 Ti, Performance, PTX

January 13, 2015 by hgpu

Exploring GPU Memory Performance Using Digital Image Processing Algorithms

Puya Memarzia, Farshad Khunjush

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Tags: Benchmarking, Computer science, CUDA, Image processing, nVidia, nVidia GeForce GTX 570

January 10, 2015 by hgpu

CHO: A Benchmark Suite for OpenCL-based FPGA Accelerators

Geoffrey Ndu, Mikel Lujan, Javier Navaridas

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Source codes

Tags: Benchmarking, Computer science, FPGA, OpenCL, Package

January 8, 2015 by hgpu

GPU: Power vs Performance

Siddhartha Sankar Mondal

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Tags: Benchmarking, Computer science, CUDA, Energy-efficient computing, nVidia, nVidia GeForce GTX 580, Thesis

January 5, 2015 by hgpu

Performance comparison of Lattice Boltzmann fluid flow simulation using OpenCL and CUDA frameworks

Jelena Tekic, Predrag Tekic, Milos Rackovic

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Tags: Benchmarking, CUDA, Fluid dynamics, Heterogeneous systems, Java, Lattice Boltzmann model, nVidia, nVidia GeForce GT 220, OpenCL

January 2, 2015 by hgpu

To Use or Not to Use: Graphics Processing Units for Pattern Matching Algorithms

Vajira Thambawita, Roshan Ragel, Dhammika Elkaduwe

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Tags: Algorithms, Benchmarking, Computer science, CUDA, nVidia, nVidia GeForce GT 740 M, Performance, String matching

December 30, 2014 by hgpu

Automatic Tuning of Local Memory Use on GPGPUs

Tianyi David Han, Tarek S. Abdelrahman

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Tags: Benchmarking, Computer science, Machine learning, nVidia, OpenCL, Performance, Tesla M2090

December 26, 2014 by hgpu

A CUDA implementation of the High Performance Conjugate Gradient benchmark

Everett Phillips, Massimiliano Fatica

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Tags: Algorithms, Benchmarking, Computer science, CUDA, Differential equations, nVidia, Partial differential equations, PDEs, Tesla K20

November 29, 2014 by hgpu

Scalable Verification Techniques for Data-Parallel Programs

Nathan Yong Seng Chong

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Tags: ARM, Benchmarking, Computer science, CUDA, nVidia, nVidia GeForce GTX 570, OpenCL, Package, Tesla M2050, Thesis

November 25, 2014 by hgpu

A Performance Comparison of Sort and Scan Libraries for GPUs

Bruce Merry

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Tags: Algorithms, AMD Radeon R9 270, ATI, Benchmarking, Computer science, CUDA, nVidia, nVidia GeForce GTX 480, OpenCL, Package, Sorting, Tesla K40

October 20, 2014 by hgpu

Leo: A Profile-Driven Dynamic Optimization Framework for GPU Applications

Naila Farooqui, Christopher J. Rossbach, Yuan Yu, Karsten Schwan

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Tags: Benchmarking, Computer science, CUDA, nVidia, PTX, Tesla M2075

October 11, 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

Comparison of OpenCL performance on different platforms using VexCL and Blaze

Thorough Evaluation of GPU Shared Memory Load and Store Instructions

Exploring GPU Memory Performance Using Digital Image Processing Algorithms

CHO: A Benchmark Suite for OpenCL-based FPGA Accelerators

GPU: Power vs Performance

Performance comparison of Lattice Boltzmann fluid flow simulation using OpenCL and CUDA frameworks

To Use or Not to Use: Graphics Processing Units for Pattern Matching Algorithms

Automatic Tuning of Local Memory Use on GPGPUs

A CUDA implementation of the High Performance Conjugate Gradient benchmark

Scalable Verification Techniques for Data-Parallel Programs

A Performance Comparison of Sort and Scan Libraries for GPUs

Leo: A Profile-Driven Dynamic Optimization Framework for GPU Applications

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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