high performance computing on graphics processing units: hgpu.org

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hgpu.org » Applications » Computer science

OpenCL and the 13 Dwarfs: A Work in Progress

W. Feng, H. Lin, T. Scogland, J. Zhang

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Tags: ATI, ATI Radeon HD 5450, Benchmarking, Computer science, Heterogeneous systems, nVidia, nVidia GeForce GT 520, OpenCL, Tesla C2050

May 1, 2012 by hgpu

Solving Stochastic Differential Equations Using General Purpose Graphics Processing Unit

Lev Alexandrovich Neiman

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Tags: Algorithms, Computer science, CUDA, Differential equations, nVidia, nVidia GeForce GTX 480, Package, Tesla C1060, Tesla C2075

April 28, 2012 by hgpu

High-Performance Code Generation for Stencil Computations on GPU Architectures

Justin Holewinski, Louis-Noel Pouchet, P. Sadayappan

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Tags: Algorithms, ATI, ATI Radeon HD 6550, Code generation, Compilers, Computer science, nVidia, nVidia GeForce GTX 280, nVidia GeForce GTX 580, OpenCL, Programming Languages, Programming techniques, Tesla C2050

April 28, 2012 by hgpu

Characterizing and Improving the Use of Demand-Fetched Caches in GPUs

Wenhao Jia, Kelly A. Shaw, Margaret Martonosi

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Tags: Algorithms, Computer science, CUDA, Memory model, nVidia, Optimization, Performance, Tesla C2070

April 28, 2012 by hgpu

Hybrid CPU/GPU KD-Tree Construction for Versatile Ray Tracing

Jean-Patrick Roccia, Mathias Paulin, Christophe Coustet

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Tags: 3D Graphics and Realism, Computer science, CUDA, KD-tree, nVidia, nVidia GeForce GTX 560, Raytracing

April 27, 2012 by hgpu

Matrix Multiplication with CUDA – A basic introduction to the CUDA programming model

Robert Hochberg

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Tags: Computer science, CUDA, Matrix multiplication, nVidia, nVidia GeForce 9600 M GT, nVidia GeForce GT 330 M, Tesla C1060

April 27, 2012 by hgpu

High-performance sparse matrix-vector multiplication on GPUs for structured grid computations

Jeswin Godwin, Justin Holewinski, P. Sadayappan

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Tags: Computer science, CUDA, Differential equations, nVidia, nVidia GeForce GTX 280, nVidia GeForce GTX 580, Partial differential equations, PDEs, Sparse matrix, Tesla C2050

April 25, 2012 by hgpu

Lynx: A Dynamic Instrumentation System for Data-Parallel Applications on GPGPU Architectures

Naila Farooqui, Andrew Kerr, Greg Eisenhauer, Karsten Schwan, Sudhakar Yalamanchili

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Tags: Code generation, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GTX 480, Optimization, Package, Tesla C2050

April 25, 2012 by hgpu

The Bones Source-to-Source Compiler Manual

Cedric Nugteren

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Tags: Algorithms, Code generation, Computer science, CUDA, MPI, nVidia, OpenCL, Package

April 25, 2012 by hgpu

Parboil: A Revised Benchmark Suite for Scientific and Commercial Throughput Computing

John A. Stratton, Christopher Rodrigues, I-Jui Sung, Nady Obeid, Li-Wen Chang, Nasser Anssari, Geng Daniel Liu, Wen-mei W. Hwu

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Tags: Algorithms, Benchmarking, Computer science, CUDA, Molecular simulation, MPI, nVidia, nVidia GeForce GTX 480, Optimization, Package

April 25, 2012 by hgpu

WebCL for Hardware-Accelerated Web Applications

Won Jeon, Tasneem Brutch, Simon Gibbs

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Tags: Algorithms, Computer science, Java, nVidia, nVidia GeForce GT 330 M, OpenCL, Package

April 25, 2012 by hgpu

True 4-Bit Quantized CNN Training on CPU

True 4-Bit Quantized Convolutional Neural Network Training on CPU: Achieving Full-Precision Parity

cuFuzz: A GPU-oriented coverage-guided fuzzer for userland CUDA application

Hunting CUDA Bugs at Scale with cuFuzz

KernelSkill: A Multi-Agent Framework for GPU Kernel Optimization

MSKernelBench & CUDAMaster

Making LLMs Optimize Multi-Scenario CUDA Kernels Like Experts

EvoScientist: Harness Vibe Research with Self-evolving AI Scientists

EvoScientist: Towards Multi-Agent Evolving AI Scientists for End-to-End Scientific Discovery

RepoLaunch: Automating Build and Test Pipeline of Code Repositories on ANY Language and ANY Platform

RepoLaunch: Automating Build & Test Pipeline of Code Repositories on ANY Language and ANY Platform

RepoLaunch: Automating Build and Test Pipeline of Code Repositories on ANY Language and ANY Platform

RepoLaunch: Automating Build & Test Pipeline of Code Repositories on ANY Language and ANY Platform

CONCUR: a benchmark designed to evaluate multithreaded Java code generated by LLMs

CONCUR: Benchmarking LLMs for Concurrent Code Generation

HIPRT: Ray Tracing using HIP

Ray Tracing using HIP

MXFP4 Training Support Codebase

Practical FP4 Training for Large-Scale MoE Models on Hopper GPUs

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high performance computing on graphics processing units: hgpu.org

Applications

OpenCL and the 13 Dwarfs: A Work in Progress

Solving Stochastic Differential Equations Using General Purpose Graphics Processing Unit

High-Performance Code Generation for Stencil Computations on GPU Architectures

Characterizing and Improving the Use of Demand-Fetched Caches in GPUs

Hybrid CPU/GPU KD-Tree Construction for Versatile Ray Tracing

Matrix Multiplication with CUDA – A basic introduction to the CUDA programming model

High-performance sparse matrix-vector multiplication on GPUs for structured grid computations

Lynx: A Dynamic Instrumentation System for Data-Parallel Applications on GPGPU Architectures

The Bones Source-to-Source Compiler Manual

Parboil: A Revised Benchmark Suite for Scientific and Commercial Throughput Computing

WebCL for Hardware-Accelerated Web Applications

Recent source codes

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

MSKernelBench & CUDAMaster

EvoScientist: Harness Vibe Research with Self-evolving AI Scientists

RepoLaunch: Automating Build and Test Pipeline of Code Repositories on ANY Language and ANY Platform

RepoLaunch: Automating Build and Test Pipeline of Code Repositories on ANY Language and ANY Platform

CONCUR: a benchmark designed to evaluate multithreaded Java code generated by LLMs

HIPRT: Ray Tracing using HIP

MXFP4 Training Support Codebase

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