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hgpu.org » Applications » Computer science

Fault Tree Analysis Speed-up with GPU Parallel Computing

Hadi Aghassi, Farrokh Aghassi

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Tags: Algorithms, Computer science, CUDA, Fault tolerance, Monte Carlo simulation, nVidia, nVidia GeForce 8600 GS

July 1, 2012 by hgpu

A Scheduling Framework for a Heterogeneous Parallel Architecture

Wei Zhang

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Tags: Computer science, CUDA, Distributed computing, Heterogeneous systems, nVidia, nVidia Quadro FX 1800, Task scheduling, Tesla C1060, Thesis

June 30, 2012 by hgpu

Point Based Color Bleeding with CUDA and Caching

Nick Feeney

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Tags: 3D Graphics and Realism, Algorithms, Computer science, CUDA, nVidia, Rasterization

June 30, 2012 by hgpu

An Optimized Large-Scale Hybrid DGEMM Design for CPUs and ATI GPUs

Jiajia Li, Xingjian Li, Guangming Tan, Mingyu Chen, Ninghui Sun

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Tags: Algorithms, ATI, ATI CAL, ATI Radeon HD 5970, ATI Stream, Computer science, Heterogeneous systems, Matrix multiplication, Optimization, Package

June 29, 2012 by hgpu

GPUs as an Opportunity for Offloading Garbage Collection

Martin Maas, Philip Reames, Jeffrey Morlan, Krste Asanovic, Anthony D. Joseph, John Kubiatowicz

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Tags: Algorithms, APU, Computer science, Memory, OpenCL, Performance

June 29, 2012 by hgpu

Explicit Shallow Water Simulations on GPUs: Guidelines and Best Practices

Andre R. Brodtkorb, Martin L. Saetra

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 580

June 27, 2012 by hgpu

Software Performance Analysis with Parallel Programming Approaches

Suneeta H. Angadi, G. T. Raju, Abhishek B

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Tags: Computer science, Matrix multiplication, OpenCL, OpenMP, Optimization, Performance

June 27, 2012 by hgpu

OpenCL Floating Point Software on Heterogeneous Architectures – Portable or Not?

Miriam Leeser, Jaideep Ramachandran, Thomas Wahl, Devon Yablonski

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Tags: Computer science, Heterogeneous systems, OpenCL

June 27, 2012 by hgpu

GreenGPU: A Holistic Approach to Energy Efficiency in GPU-CPU Heterogeneous Architectures

Kai Ma, Xue Li, Wei Chen, Chi Zhang, Xiaorui Wang

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Tags: Computer science, CUDA, Energy-efficient computing, Heterogeneous systems, nVidia, nVidia GeForce 8800 GTX

June 26, 2012 by hgpu

Multi-GPU Island-Based Genetic Algorithm for Solving the Knapsack Problem

Jiri Jaros

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Tags: Algorithms, Computer science, CUDA, Genetic programming, GPU cluster, MPI, nVidia, nVidia GeForce GTX 580, Package

June 26, 2012 by hgpu

Compiling a high-level language for GPUs: (via language support for architectures and compilers)

Christophe Dubach, Perry Cheng, Rodric Rabbah, David F. Bacon, Stephen J. Fink

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Tags: ATI, ATI Radeon HD 5970, Compilers, Computer science, CUDA, Heterogeneous systems, Java, nVidia, nVidia GeForce 8800 GTX, nVidia GeForce GTX 580, OpenCL, Programming Languages

June 26, 2012 by hgpu

A Parallel Algorithm Development Model for the GPU Architecture

J. Steven Kirtzic, Ovidiu Daescu

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Tags: Algorithms, Computer science, CUDA, nVidia

June 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

See all packages

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high performance computing on graphics processing units: hgpu.org

Applications

Fault Tree Analysis Speed-up with GPU Parallel Computing

A Scheduling Framework for a Heterogeneous Parallel Architecture

Point Based Color Bleeding with CUDA and Caching

An Optimized Large-Scale Hybrid DGEMM Design for CPUs and ATI GPUs

GPUs as an Opportunity for Offloading Garbage Collection

Explicit Shallow Water Simulations on GPUs: Guidelines and Best Practices

Software Performance Analysis with Parallel Programming Approaches

OpenCL Floating Point Software on Heterogeneous Architectures – Portable or Not?

GreenGPU: A Holistic Approach to Energy Efficiency in GPU-CPU Heterogeneous Architectures

Multi-GPU Island-Based Genetic Algorithm for Solving the Knapsack Problem

Compiling a high-level language for GPUs: (via language support for architectures and compilers)

A Parallel Algorithm Development Model for the GPU Architecture

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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