Applications

hgpu.org » Applications

GAMUT: GPU accelerated microRNA analysis to uncover target genes through CUDA-miRanda

Shuang Wang, Jihoon Kim, Xiaoqian Jiang, Stefan F Brunner, Lucila Ohno-Machado

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Tags: Bioinformatics, Biology, CUDA, nVidia, Package, RNA, Smith-Waterman algorithm, Tesla M2090

May 10, 2014 by hgpu

Managing the Topology of Heterogeneous Cluster Nodes with Hardware Locality (hwloc)

Brice Goglin

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Tags: Computer science, CUDA, GPU cluster, Heterogeneous systems, nVidia, OpenMPI, Package, Xeon Phi

May 7, 2014 by hgpu

Improving the Programmability of GPU Architectures

Cedric Nugteren

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Tags: Algorithms, Computer science, CUDA, nVidia, OpenACC, OpenCL, Package, Performance, Thesis

May 7, 2014 by hgpu

Non-separable 2D, 3D and 4D filtering with CUDA

Anders Eklund, Paul Dufort

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Tags: CUDA, FFT, Filtering, Image processing, nVidia, nVidia GeForce GTX 680, Package, Signal denoising

May 5, 2014 by hgpu

Accelerating Mixed-Abstraction SystemC Models on Multi-Core CPUs and GPUs

Anirudh Mohan Kaushik

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Tags: Compilers, Computer science, CUDA, Heterogeneous systems, nVidia, Package, Tesla C2075, Thesis

May 5, 2014 by hgpu

Coalition Structure Generation with the Graphics Processing Unit

Krzysztof Pawlowski, Karol Kurach, Kim Svensson, Sarvapali Ramchurn, Tomasz P. Michalak, Talal Rahwan

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 660, Package, Performance

May 3, 2014 by hgpu

SUPERGLUE: A Shared Memory Framework Using Data Versioning for Dependency-Aware Task-Based Parallelization

Martin Tillenius

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Tags: Algorithms, Computer science, Intel Xeon Phi, OpenMP, Package, Task scheduling

April 29, 2014 by hgpu

Efficient Acceleration of Mutual Information Computation for Nonrigid Registration using CUDA

Kei Ikeda, Fumihiko Ino, Kenichi Hagihara

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Tags: CUDA, Image processing, Image registration, nVidia, nVidia GeForce GTX 580, Package

April 27, 2014 by hgpu

On the Parallelization of Integer Polynomial Multiplication

Farnam Mansouri

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Tags: Algorithms, Computer science, CUDA, nVidia, Package, Symbolic Computation, Tesla C2050, Thesis

April 25, 2014 by hgpu

SWAPHI: Smith-Waterman Protein Database Search on Xeon Phi Coprocessors

Yongchao Liu, Bertil Schmidt

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Tags: Computer science, Intel Xeon Phi, Package, Smith-Waterman algorithm

April 21, 2014 by hgpu

Sparser, Better, Faster GPU Parsing

David Hall, Taylor Berg-Kirkpatrick, John Canny, Dan Klein

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Tags: Computer science, nVidia, nVidia GeForce GTX 680, OpenCL, Package, Performance

April 21, 2014 by hgpu

The Reconstruction Toolkit (RTK), an open-source cone-beam CT reconstruction toolkit based on the Insight Toolkit (ITK)

S. Rit, M. Vila Oliva, S. Brousmiche, R. Labarbe, D. Sarrut, G. C. Sharp

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Tags: Computed tomography, CT, CUDA, Medicine, nVidia, OpenCL, Package, Tesla M2090

April 18, 2014 by hgpu

AutoKernel: Autoresearch for GPU kernels. Give it any PyTorch model, go to sleep, wake up to optimized Triton kernels

AutoKernel: Autonomous GPU Kernel Optimization via Iterative Agent-Driven Search

Triton-Sanitizer: A Fast and Device-Agnostic Memory Sanitizer for Triton with Rich Diagnostic Context

LLM.Q: Quantized LLM training in pure CUDA/C++

LLMQ: Efficient Lower-Precision LLM Training for Consumer GPUs

SOL-ExecBench: Speed-of-Light Benchmarking for Real-World GPU Kernels Against Hardware Limits

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

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high performance computing on graphics processing units: hgpu.org

Applications

GAMUT: GPU accelerated microRNA analysis to uncover target genes through CUDA-miRanda

Managing the Topology of Heterogeneous Cluster Nodes with Hardware Locality (hwloc)

Improving the Programmability of GPU Architectures

Non-separable 2D, 3D and 4D filtering with CUDA

Accelerating Mixed-Abstraction SystemC Models on Multi-Core CPUs and GPUs

Coalition Structure Generation with the Graphics Processing Unit

SUPERGLUE: A Shared Memory Framework Using Data Versioning for Dependency-Aware Task-Based Parallelization

Efficient Acceleration of Mutual Information Computation for Nonrigid Registration using CUDA

On the Parallelization of Integer Polynomial Multiplication

SWAPHI: Smith-Waterman Protein Database Search on Xeon Phi Coprocessors

Sparser, Better, Faster GPU Parsing

The Reconstruction Toolkit (RTK), an open-source cone-beam CT reconstruction toolkit based on the Insight Toolkit (ITK)

Recent source codes

AutoKernel: Autoresearch for GPU kernels. Give it any PyTorch model, go to sleep, wake up to optimized Triton kernels

Triton-Sanitizer: A Fast and Device-Agnostic Memory Sanitizer for Triton with Rich Diagnostic Context

LLM.Q: Quantized LLM training in pure CUDA/C++

SOL-ExecBench: Speed-of-Light Benchmarking for Real-World GPU Kernels Against Hardware Limits

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

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