high performance computing on graphics processing units: hgpu.org

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A Framework for Productive, Efficient and Portable Parallel Computing

Ekaterina I. Gonina

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Tags: Algorithms, Code generation, Computer science, CUDA, MapReduce, nVidia, nVidia GeForce GTX 480, Package, Python, Thesis

January 12, 2014 by hgpu

A tool for mapping Single Nucleotide Polymorphisms using Graphics Processing Units

Andrea Manconi, Alessandro Orro, Emanuele Manca, Giuliano Armano, Luciano Milanesi

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Tags: Algorithms, Bioinformatics, Biology, CUDA, nVidia, nVidia GeForce GTX 480, Package, Smith-Waterman algorithm, Tesla K20

January 12, 2014 by hgpu

Nengo: a Python tool for building large-scale functional brain models

Trevor Bekolay, James Bergstra, Eric Hunsberger, Travis DeWolf, Terrence C. Stewart, Daniel Rasmussen, Xuan Choo, Aaron Russell Voelker, Chris Eliasmith

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Tags: ATI, ATI Radeon HD 7970, Biology, Neuroscience, nVidia, nVidia GeForce GTX 280, OpenCL, Package, PyOpenCL, Python

January 9, 2014 by hgpu

The Parallel Bayesian Toolbox for High-performance Bayesian Filtering in Metrology

E. Garcia, T. Hausotte

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Tags: Algorithms, Bayesian, CUDA, Filtering, nVidia, Package, Particle filtering, Physics, Probability, Tesla C2075

January 6, 2014 by hgpu

PySPH: A Python framework for SPH

Kunal Puri, Prabhu Ramachandran, Pankaj Pandey, Chandrashekhar Kaushik, Pushkar Godbole

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Tags: Fluid dynamics, OpenCL, Package, Python

January 6, 2014 by hgpu

Parallel Irradiance Caching on the GPU

Nathaniel L. Jones

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Tags: Computer science, CUDA, nVidia, nVidia Quadro K4000, OptiX, Package, Raytracing

January 5, 2014 by hgpu

Accelerating Simulation Codes through the GeMTC Framework

Digvijay Singh Gahlot, Scott Krieder, Ioan Raicu

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

January 3, 2014 by hgpu

Nemo: A parallelized Lagrangian particle-tracking model

Benjamin Jones

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Tags: CUDA, Earth and Space Sciences, Fortran, MPI, nVidia, nVidia GeForce GTX 650 M, Package

January 3, 2014 by hgpu

HUGO: Hierarchical mUlti-reference Genome cOmpression for aligned reads

Pinghao Li, Xiaoqian Jiang, Shuang Wang, Jihoon Kim, Hongkai Xiong, Lucila Ohno-Machado

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Tags: Algorithms, Bioinformatics, Biology, Clustering, Compression, CUDA, nVidia, Package, Sequence alignment, Tesla M2090

December 27, 2013 by hgpu

BbmTTP: Beat-based Parallel Simulated Annealing Algorithm on GPGPUs for the Mirrored Traveling Tournament Problem

Vijay Menon, Saurabh Jha

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Tags: Algorithms, Computer science, CUDA, nVidia, Optimization, Package, Tesla M2050

December 27, 2013 by hgpu

Adaptive Task Size Control on High Level Programming for GPU/CPU Work Sharing

Tetsuya Odajima, Taisuke Boku, Mitsuhisa Sato, Toshihiro Hanawa, Yuetsu Kodama, Raymond Namyst, Samuel Thibault, Olivier Aumage

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, Package, Performance, Task scheduling, Tesla M2090

December 25, 2013 by hgpu

Graphics Processing Units in Acceleration of Bandwidth Selection for Kernel Density Estimation

Witold Andrzejewski, Artur Gramacki, Jaroslaw Gramacki

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Tags: Algorithms, CUDA, nVidia, nVidia GeForce GTX 480, Package, Probability, Statistics

December 24, 2013 by hgpu

tritonBLAS: A Lightweight Triton-based General Matrix Multiplication (GEMM) Library

tritonBLAS: Triton-based Analytical Approach for GEMM Kernel Parameter Selection

hls4ml: Machine learning on FPGAs using HLS

hls4ml: A Flexible, Open-Source Platform for Deep Learning Acceleration on Reconfigurable Hardware

ThunderKittens: Tile primitives for speedy kernels

ParallelKittens: Systematic and Practical Simplification of Multi-GPU AI Kernels

NVIDIA Nemotron Parse 1.1

Iris: AMD RAD's multi-GPU Triton-based framework for seamless multi-GPU programming

Iris: First-Class Multi-GPU Programming Experience in Triton

HipKittens: Fast and Furious AMD Kernels

CudaForge: An Agent Framework with Hardware Feedback for CUDA Kernel Optimization

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high performance computing on graphics processing units: hgpu.org

Packages

A Framework for Productive, Efficient and Portable Parallel Computing

A tool for mapping Single Nucleotide Polymorphisms using Graphics Processing Units

Nengo: a Python tool for building large-scale functional brain models

The Parallel Bayesian Toolbox for High-performance Bayesian Filtering in Metrology

PySPH: A Python framework for SPH

Parallel Irradiance Caching on the GPU

Accelerating Simulation Codes through the GeMTC Framework

Nemo: A parallelized Lagrangian particle-tracking model

HUGO: Hierarchical mUlti-reference Genome cOmpression for aligned reads

BbmTTP: Beat-based Parallel Simulated Annealing Algorithm on GPGPUs for the Mirrored Traveling Tournament Problem

Adaptive Task Size Control on High Level Programming for GPU/CPU Work Sharing

Graphics Processing Units in Acceleration of Bandwidth Selection for Kernel Density Estimation

Recent source codes

tritonBLAS: A Lightweight Triton-based General Matrix Multiplication (GEMM) Library

hls4ml: Machine learning on FPGAs using HLS

ThunderKittens: Tile primitives for speedy kernels

NVIDIA Nemotron Parse 1.1

Iris: AMD RAD's multi-GPU Triton-based framework for seamless multi-GPU programming

HipKittens: Fast and Furious AMD Kernels

Fortran xDSL dialects

mt4g: Memory Topology 4 GPUs

Falcon: GPU-Based Floating-point Adaptive Lossless Compression

CudaForge: An Agent Framework with Hardware Feedback for CUDA Kernel Optimization

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