high performance computing on graphics processing units: hgpu.org

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hgpu.org » Applications » Computer science

Evolving Neural Networks on GPUs

Johannes Hofmann

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Tags: Algorithms, Computer science, CUDA, Evolutionary Computations, Finance, Neural and Evolutionary Computing, Neural networks, nVidia, nVidia GeForce GTX 480, Tesla C1060

December 7, 2011 by hgpu

GPU Implementation of the Keccak Hash Function Family

Pierre-Louis Cayrel, Gerhard Hoffmann, Michael Schneider

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Tags: Computer science, CUDA, Hashing, nVidia, nVidia GeForce GTX 295, Package, Security, Tesla C2050

December 7, 2011 by hgpu

Parallelizing AES on multicores and GPUs

Julian Ortega, Helmuth Trefftz, Christian Trefftz

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Tags: AES, Algorithms, Block Ciphers, Computer science, CUDA, nVidia, nVidia GeForce GT 330 M, nVidia Quadro FX 1800, OpenMP, Security

December 7, 2011 by hgpu

Sparse-Matrix-CG-Solver in CUDA

Dominik Michels

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Tags: Algorithms, Computer science, Conjugate gradient solver, CUDA, nVidia, nVidia GeForce GTX 480, Sparse matrix

December 7, 2011 by hgpu

Accelerating Braided B+ Tree Searches on a GPU with CUDA

Jordan Fix, Andrew Wilkes, Kevin Skadron

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Tags: Algorithms, Computer science, CUDA, Databases, nVidia, nVidia GeForce GTX 480

December 7, 2011 by hgpu

Effective Mapping of Grammatical Evolution to CUDA Hardware Model

Petr Pospichal

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Tags: Algorithms, Computer science, CUDA, Evolutionary Computations, nVidia, nVidia GeForce GTX 480, Performance

December 7, 2011 by hgpu

Automatic Fusions of CUDA-GPU Kernels for Parallel Map

Jan Fousek, Jiri Filipovic, Matus Madzin

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 480, Optimization, Performance

December 6, 2011 by hgpu

DTAM: Dense tracking and mapping in real-time

Richard A. Newcombe, Steven J. Lovegrove, Andrew J. Davison

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Tags: Algorithms, Computer science, Computer vision, Image alignment, nVidia, nVidia GeForce GTX 480

December 6, 2011 by hgpu

Massively Parallel Identification of Intersection Points for GPGPU Ray Tracing

Alexandre S. Nery, Nadia Nedjah, Felipe M. G. Franca, Lech Jozwiak

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 460, nVidia GeForce GTX 465, Raytracing

December 6, 2011 by hgpu

Performance Analysis of GPU compared to Single-core and Multi-core CPU for Natural Language Applications

Shubham Gupta, M.Rajasekhara Babu

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Tags: Computer science, CUDA, High-level Languages, nVidia, nVidia GeForce GT 210 M, OpenMP, Performance, String matching

December 6, 2011 by hgpu

Real-time Terrain Modeling using CPU-GPU Coupled Computation

Adrien Bernhardt, Andre Maximo, Luiz Velho, Houssam Hnaidi, Marie-Paule Cani

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Tags: Computer science, nVidia, nVidia GeForce GTS 480, OpenGL, Real-time graphics, Rendering, Tessellation

December 6, 2011 by hgpu

Soft Error Resilient QR Factorization for Hybrid System with GPGPU

Peng Du, Piotr Luszczek, Stan Tomov, Jack Dongarra

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Tags: Algorithms, Computer science, CUDA, Factorization, Hybrid computing, nVidia, Tesla C2050, Tesla M2070

December 5, 2011 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

Evolving Neural Networks on GPUs

GPU Implementation of the Keccak Hash Function Family

Parallelizing AES on multicores and GPUs

Sparse-Matrix-CG-Solver in CUDA

Accelerating Braided B+ Tree Searches on a GPU with CUDA

Effective Mapping of Grammatical Evolution to CUDA Hardware Model

Automatic Fusions of CUDA-GPU Kernels for Parallel Map

DTAM: Dense tracking and mapping in real-time

Massively Parallel Identification of Intersection Points for GPGPU Ray Tracing

Performance Analysis of GPU compared to Single-core and Multi-core CPU for Natural Language Applications

Real-time Terrain Modeling using CPU-GPU Coupled Computation

Soft Error Resilient QR Factorization for Hybrid System with GPGPU

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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