high performance computing on graphics processing units: hgpu.org

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hgpu.org » Applications » Computer science

A Highly Parallel Reuse Distance Analysis Algorithm on GPUs

Huimin Cui, Qing Yi, Jingling Xue, Lei Wang, Yang Yang, Xiaobing Feng

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Tags: Algorithms, Computer science, CUDA, Memory model, nVidia, Tesla C2050

March 30, 2012 by hgpu

Optimized Strategies for Mapping Three-dimensional FFTs onto CUDA GPUs

Jing Wu, Joseph JaJa

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Tags: Algorithms, Computer science, CUDA, FFT, nVidia, nVidia GeForce GTX 280, nVidia GeForce GTX 480, Performance, Tesla C1060, Tesla C2050

March 30, 2012 by hgpu

Performance evaluation of GPU memory hierarchy using the FFT

Jacobo Lobeiras, Margarita Amor, Ramon Doallo

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Tags: Benchmarking, Computer science, CUDA, FFT, nVidia, nVidia GeForce GTX 280, nVidia GeForce GTX 480, Performance, Signal processing

March 30, 2012 by hgpu

Scheduling Tasks over Multicore machines enhanced with Accelerators: a Runtime System’s Perspective

Cedric Augonnet

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Tags: Cell processor, Computer science, CUDA, Databases, nVidia, nVidia Quadro FX 4600, nVidia Quadro FX 5800, Task scheduling, Tesla C2050, Tesla M2070, Tesla S1070, Thesis

March 29, 2012 by hgpu

A computing origami: Optimized code generation for emerging parallel platforms

Hagiescu Miriste Andrei Mihai

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Tags: Code generation, Computer science, CUDA, FPGA, nVidia, nVidia GeForce 8800 GTX, Optimization, Tesla S1070, Thesis

March 29, 2012 by hgpu

Multicore Processing for Clustering Algorithms

RekhanshRao, Kapil Kumar Nagwanshi, SipiDubey

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Tags: Algorithms, ATI, ATI Radeon HD 5870, Clustering, Computer science, Data mining, nVidia, OpenCL

March 29, 2012 by hgpu

Machine Learning for Predictive Auto-Tuning with Boosted Regression Trees

James Bergstra, Nicolas Pinto, David Cox

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Tags: Computer science, CUDA, Filtering, Machine learning, Matrix multiplication, nVidia, nVidia GeForce GTX 295, nVidia GeForce GTX 480, nVidia GeForce GTX 580, Optimization, Tesla C1060, Tesla C2070

March 29, 2012 by hgpu

Auto-tuning a High-Level Language Targeted to GPU Codes

Scott Grauer-Gray, Lifan Xu, Robert Searles, Sudhee Ayalasomayajula, John Cavazos

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Tags: Computer science, CUDA, nVidia, nVidia GeForce 9800 GT, nVidia GeForce GTX 280, OpenCL, Optimization, Performance, Programming techniques, Stereo vision, Tesla C2050

March 28, 2012 by hgpu

Accelerating the FDTD Method Using SSE and Graphics Processing Units

Matthew J. Livesey

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Tags: Computer science, CUDA, Data parallelism, FDTD, Finite-difference time-domain, nVidia, Tesla M2050, Tesla T10, Thesis

March 28, 2012 by hgpu

Systematic construction, verification and implementation methodology for LDPC codes

Hui Yu, Jing Cui, Yixiang Wang, Yibin Yang

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Tags: Computer science, CUDA, FPGA, nVidia, nVidia GeForce GTX 260, Parity-check codes, Security

March 28, 2012 by hgpu

Fast, parallel and secure cryptography algorithm using Lorenz’s attractor

Anderson Goncalves Marco, Alexandre Souto Martinez, Odemir Martinez Bruno

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce 8800 GT, nVidia GeForce GTX 285, Security

March 28, 2012 by hgpu

Enabling and Scaling Matrix Computations on Heterogeneous Multi-Core and Multi-GPU Systems

Fengguang Song, Stanimire Tomov, Jack Dongarra

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Tags: Algorithms, Computer science, CUDA, Factorization, Heterogeneous systems, nVidia, Tesla M2070

March 28, 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

A Highly Parallel Reuse Distance Analysis Algorithm on GPUs

Optimized Strategies for Mapping Three-dimensional FFTs onto CUDA GPUs

Performance evaluation of GPU memory hierarchy using the FFT

Scheduling Tasks over Multicore machines enhanced with Accelerators: a Runtime System’s Perspective

A computing origami: Optimized code generation for emerging parallel platforms

Multicore Processing for Clustering Algorithms

Machine Learning for Predictive Auto-Tuning with Boosted Regression Trees

Auto-tuning a High-Level Language Targeted to GPU Codes

Accelerating the FDTD Method Using SSE and Graphics Processing Units

Systematic construction, verification and implementation methodology for LDPC codes

Fast, parallel and secure cryptography algorithm using Lorenz’s attractor

Enabling and Scaling Matrix Computations on Heterogeneous Multi-Core and Multi-GPU Systems

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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