high performance computing on graphics processing units: hgpu.org

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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

Analysis and Optimization Techniques for Massively Parallel Processors

Wenhao Jia

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Tags: Algorithms, ATI, ATI Radeon HD 7970, Computer science, CUDA, Heterogeneous systems, nVidia, OpenCL, PTX, Tesla C2070, Thesis, Virtualization

December 15, 2014 by hgpu

Testing and Exposing Weak Graphics Processing Unit Memory Models

Tyler Rey Sorensen

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Tags: Computer science, CUDA, Memory model, nVidia, nVidia GeForce GTX 750, nVidia GeForce GTX Titan, PTX, Tesla C2075, Thesis

October 27, 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

Parallel Hierarchical Clustering on the GPU

Ursula Reiterer

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Tags: Algorithms, Clustering, Computer science, nVidia, nVidia GeForce GT 650 M, OpenCL, PTX, Thesis

September 20, 2014 by hgpu

A Framework for Lattice QCD Calculations on GPUs

F. T. Winter, M. A. Clark, R. G. Edwards, B. Joo

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Tags: Algorithms, Code generation, Computational Physics, CUDA, High Energy Physics - Lattice, Mathematical Software, nVidia, Physics, PTX, QCD, Tesla K20

August 27, 2014 by hgpu

Understanding the ISA impact on GPU Architecture

Mayank Vinodbhai Kothiya

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Tags: Computer science, CUDA, GPGPU-sim, Hardware Architecture, nVidia, PTX, Tesla, Thesis

July 28, 2014 by hgpu

Cofactorization on Graphics Processing Units

Andrea Miele, Joppe W. Bos, Thorsten Kleinjung, Arjen K. Lenstra

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Tags: Computer science, CUDA, Factorization, nVidia, nVidia GeForce GTX 580, PTX, Security

June 3, 2014 by hgpu

Performance Optimization of GPU ELF-Codes

Fausto Artico

Tags: Algorithms, Computer science, CUDA, nVidia, Optimization, PTX, Tesla C2070, Thesis

May 15, 2014 by hgpu

Piko: A Design Framework for Programmable Graphics Pipelines

Anjul Patney, Stanley Tzeng, Kerry A. Seitz Jr., John D. Owens

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Tags: 3D Graphics and Realism, Code generation, Computer science, nVidia, nVidia GeForce GTX 680, OpenCL, PTX, Rasterization, Raytracing, Rendering

April 29, 2014 by hgpu

The Promises of Hybrid Hexagonal/Classical Tiling for GPU

Tobias Grosser, Sven Verdoolaege, Albert Cohen, P. Sadayappan

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Tags: Code generation, Compilers, Computer science, CUDA, nVidia, nVidia GeForce GTX 470, nVidia Quadro NVS 5200 M, PTX

July 31, 2013 by hgpu

KernelGen – the design and implementation of a next generation compiler platform for accelerating numerical models on GPUs

Dmitry Mikushin, Nikolay Likhogrud, Eddy Zheng Zhang, Christopher Bergstrom

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Source codes

Tags: Code generation, Compilers, Computer science, CUDA, Fortran, nVidia, OpenACC, Package, PTX, Tesla K20

July 15, 2013 by hgpu

KernelZero: Co-Evolving Proposer and Coder for Continuously Improved GPU Kernel Generation

Introduction to Computer Vision

ACT: Accelerator Compiler Toolkit

Automatically Generating ML Compiler Backends from Tensor Accelerator ISA Descriptions

pytest-gpu-proof: A Simple Pytest Extension for Local GPU Code Testing

pytest-gpu-proof: Enabling Cloud-CPU Continuous Integration for GPU Code with Local GPU Attestation

Accelerating the Mitigation of LLM Inference Nondeterminism Across GPU Architectures

AutoTuneBench: Trustworthy Measurement for Agent Auto-Tuning of LLM Serving Engines

nvsass-disassembler

Automated Instruction Encoding Synthesis for Modern GPU ISA Compression

PrefixBench-H100: Characterizing Prefix Reuse and Time-to-First-Token in H100 LLM Serving

EinsummableV3 - graph-based computation with tensor operations support

Every Kernel Is a Join: Automatic Multi-GPU Parallelism for AI Computations in Einsummable

MaxKernel: Intelligent TPU Kernel Engineering Agents

MaxKernel: Agentic Kernel Generation for TPUs

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high performance computing on graphics processing units: hgpu.org

Thorough Evaluation of GPU Shared Memory Load and Store Instructions

Analysis and Optimization Techniques for Massively Parallel Processors

Testing and Exposing Weak Graphics Processing Unit Memory Models

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

Parallel Hierarchical Clustering on the GPU

A Framework for Lattice QCD Calculations on GPUs

Understanding the ISA impact on GPU Architecture

Cofactorization on Graphics Processing Units

Performance Optimization of GPU ELF-Codes

Piko: A Design Framework for Programmable Graphics Pipelines

The Promises of Hybrid Hexagonal/Classical Tiling for GPU

KernelGen – the design and implementation of a next generation compiler platform for accelerating numerical models on GPUs

Recent source codes

KernelZero: Co-Evolving Proposer and Coder for Continuously Improved GPU Kernel Generation

Introduction to Computer Vision

ACT: Accelerator Compiler Toolkit

pytest-gpu-proof: A Simple Pytest Extension for Local GPU Code Testing

Accelerating the Mitigation of LLM Inference Nondeterminism Across GPU Architectures

AutoTuneBench: Trustworthy Measurement for Agent Auto-Tuning of LLM Serving Engines

nvsass-disassembler

PrefixBench-H100: Characterizing Prefix Reuse and Time-to-First-Token in H100 LLM Serving

EinsummableV3 - graph-based computation with tensor operations support

MaxKernel: Intelligent TPU Kernel Engineering Agents

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