high performance computing on graphics processing units: hgpu.org

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PTX2Kernel: Converting PTX Code into Compilable Kernels

Qiankun Dong, Tao Li, Shuai Zhang, Xiaofan Jiao, Jiabing Leng

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Tags: Computer science, CUDA, Matrix multiplication, nVidia, nVidia GeForce GTX 480, PTX, Tesla K40

March 22, 2015 by hgpu

Characterizing and Enhancing Global Memory Data Coalescing on GPUs

Naznin Fauzia, Louis-Noel Pouchet, P. Sadayappan

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Tags: Computer science, CUDA, Memory, nVidia, Performance, PTX, Tesla K20

February 2, 2015 by hgpu

GPU concurrency: Weak behaviours and programming assumptions

Jade Alglave, Mark Batty, Alastair F. Donaldson, Ganesh Gopalakrishnan, Jeroen Ketema, Daniel Poetzl, Tyler Sorensen, John Wickerson

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Tags: ATI, ATI Radeon HD 6570, ATI Radeon HD 7970, Computer science, Memory, nVidia, nVidia GeForce GTX 280, nVidia GeForce GTX 540 M, nVidia GeForce GTX 660, nVidia GeForce GTX 750, nVidia GeForce GTX Titan, OpenCL, Performance, PTX, Tesla C2075

January 21, 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

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

GPUODEBenchmarks: Comparsion of Julia's GPU Kernel based ODE solvers with other open-source GPU ODE solvers

Automating Heterogeneous Parallelism in Numerical Differential Equations

Dat3M: Memory Model Aware Verification

Towards Unified Analysis of GPU Consistency

NVIDIA Federated Learning Application Runtime Environment

Supercharging Federated Learning with Flower and NVIDIA FLARE

Chat AI: A Seamless Slurm-Native Solution for HPC-Based Services

PSCToolkit: solving sparse linear systems with a large number of GPUs

CATBench: Benchmarking Framework

CATBench: A Compiler Autotuning Benchmarking Suite for Black-box Optimization

General-purpose Polyhedral Compilers

A Survey of General-purpose Polyhedral Compilers

Optimal Kernel Orchestration for Tensor Programs with Korch

Astaroth: A Scalable Multi-GPU Library for Stencil Computations

Stencil Computations on AMD and Nvidia Graphics Processors: Performance and Tuning Strategies

Bag of Tricks: Benchmarking of Jailbreak Attacks on LLMs. Empirical tricks for LLM Jailbreaking

Bag of Tricks: Benchmarking of Jailbreak Attacks on LLMs

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high performance computing on graphics processing units: hgpu.org

PTX2Kernel: Converting PTX Code into Compilable Kernels

Characterizing and Enhancing Global Memory Data Coalescing on GPUs

GPU concurrency: Weak behaviours and programming assumptions

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

Recent source codes

GPUODEBenchmarks: Comparsion of Julia's GPU Kernel based ODE solvers with other open-source GPU ODE solvers

Dat3M: Memory Model Aware Verification

NVIDIA Federated Learning Application Runtime Environment

Chat AI: A Seamless Slurm-Native Solution for HPC-Based Services

PSCToolkit: solving sparse linear systems with a large number of GPUs

CATBench: Benchmarking Framework

General-purpose Polyhedral Compilers

Optimal Kernel Orchestration for Tensor Programs with Korch

Astaroth: A Scalable Multi-GPU Library for Stencil Computations

Bag of Tricks: Benchmarking of Jailbreak Attacks on LLMs. Empirical tricks for LLM Jailbreaking

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