high performance computing on graphics processing units: hgpu.org

hgpu.org » Benchmarking

Benchmarking the Intel Xeon Phi Coprocessor

F. Masci

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

February 11, 2014 by hgpu

Benchmarks for Intel MIC Architecture

Emprise Y.K. Chan

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Tags: Benchmarking, Computer science, Intel Xeon Phi, Monte Carlo simulation

February 9, 2014 by hgpu

Developmental Directions in Parallel Accelerators

K.A. Hawick, D.P. Playne

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Tags: Benchmarking, Computer science, CUDA, Intel Xeon Phi, nVidia, nVidia GeForce GTX Titan

February 8, 2014 by hgpu

Simulating and Benchmarking the Shallow-Water Fluid Dynamical Equations on Multiple Graphical Processing Units

D.P. Playne, K.A. Hawick, M.G.B. Johnson

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Tags: Benchmarking, CUDA, Fluid dynamics, nVidia, Tesla K20

February 8, 2014 by hgpu

clpeak – peak performance of your opencl device

Krishnaraj Bhat

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Tags: ATI, Benchmarking, Computer science, nVidia, OpenCL, Package, Performance

January 23, 2014 by hgpu

Supporting x86-64 Address Translation for 100s of GPU Lanes

Jason Power, Mark D. Hill, David A. Wood

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Tags: Benchmarking, Computer science, CUDA, GPGPU-sim, Hardware Architecture, nVidia

January 18, 2014 by hgpu

Performance Engineering for a Medical Imaging Application on the Intel Xeon Phi Accelerator

Johannes Hofmann, Jan Treibig, Georg Hager, Gerhard Wellein

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Tags: Benchmarking, Computed tomography, Computer science, CT, Image reconstruction, Intel Xeon Phi, Medicine, Performance

January 17, 2014 by hgpu

BIDMach: Large-scale Learning with Zero Memory Allocation

John Canny, Huasha Zhao

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Tags: Benchmarking, Computer science, CUDA, Machine learning, nVidia, nVidia GeForce GTX 690

December 25, 2013 by hgpu

Warps and Atomics: Beyond Barrier Synchronization in the Verification of GPU Kernels

Ethel Bardsley, Alastair F. Donaldson

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Tags: Benchmarking, Computer science, CUDA, nVidia, OpenCL, Package, Python

December 20, 2013 by hgpu

The Comparisons of OpenCL and OpenMP Computing Paradigm

Slo-Li Chu, Chih-Chieh Hsiao

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Tags: ATI, ATI Radeon HD 5850, Benchmarking, Computer science, nVidia GeForce GTX 285, OpenCL, OpenMP, Performance

December 9, 2013 by hgpu

Multithreaded Transposition of Square Matrices with Common Code for Intel Xeon Processors and Intel Xeon Phi Coprocessors

Andrey Vladimirov

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Tags: Algorithms, Benchmarking, Computer science, Intel Phi, Intel Xeon Phi, Linear Algebra

December 3, 2013 by hgpu

SESH framework: A Space Exploration Framework for GPU Application and Hardware Codesign

Joo Hwan Lee, Jiayuan Meng, Hyesoon Kim

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Tags: Benchmarking, Computer science, nVidia, nVidia GeForce GTX 580, Performance

December 3, 2013 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

Benchmarking the Intel Xeon Phi Coprocessor

Benchmarks for Intel MIC Architecture

Developmental Directions in Parallel Accelerators

Simulating and Benchmarking the Shallow-Water Fluid Dynamical Equations on Multiple Graphical Processing Units

clpeak – peak performance of your opencl device

Supporting x86-64 Address Translation for 100s of GPU Lanes

Performance Engineering for a Medical Imaging Application on the Intel Xeon Phi Accelerator

BIDMach: Large-scale Learning with Zero Memory Allocation

Warps and Atomics: Beyond Barrier Synchronization in the Verification of GPU Kernels

The Comparisons of OpenCL and OpenMP Computing Paradigm

Multithreaded Transposition of Square Matrices with Common Code for Intel Xeon Processors and Intel Xeon Phi Coprocessors

SESH framework: A Space Exploration Framework for GPU Application and Hardware Codesign

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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