high performance computing on graphics processing units: hgpu.org

Styliani Loukatou, Louis Papageorgiou, Paraskevas Fakourelis, Arianna Filntisi, Eleftheria Polychronidou, Ioannis Bassis, Vasileios Megalooikonomou, Wojciech Makalowski, Dimitrios Vlachakis, Sophia Kossida

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Tags: Bioinformatics, Biology, Chemistry, CUDA, Medicine, Molecular dynamics, nVidia, OpenCL, Physics

July 4, 2014 by hgpu

A second generation of DEFG: Declarative Framework for GPUs

Robert Senser, Tom Altman

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Tags: Computer science, High-level Languages, nVidia, OpenCL, Tesla T20

July 4, 2014 by hgpu

The Design and Implementation of a GPU-enabled Multi-objective Tabu-search Intended for Real World and High-dimensional Applications

Christos Tsotskas, Timoleon Kipouros, Anthony Mark Savill

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Tags: Computer science, CUDA, Heterogeneous systems, Metaheuristics, nVidia, nVidia Quadro 1000 M

July 4, 2014 by hgpu

GPUvm: Why Not Virtualizing GPUs at the Hypervisor?

Yusuke Suzuki, Shinpei Kato, Hiroshi Yamada, Kenji Kono

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Tags: Cloud, Computer science, CUDA, nVidia, nVidia Quadro 6000, Operating systems, Virtualization

July 4, 2014 by hgpu

A Road Marking Extraction Method Using GPGPU

Dajun Ding, Jongsu Yoo, Jekyo Jung, Kwon Soon

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Tags: Computer science, Computer vision, nVidia, nVidia GeForce GTX 760, OpenCL, Vehicular traffic

July 4, 2014 by hgpu

Exploiting parallel features of modern computer architectures in bioinformatics: applications to genetics, structure comparison and large graph analysis

Guillaume Chapuis

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Tags: Algorithms, Bioinformatics, Biology, CUDA, Genetics, Graph theory, nVidia, Path problems, Tesla C2050, Thesis

July 3, 2014 by hgpu

The Framework and Compilation Techniques for Directive-based GPU Cluster Programming

Pisit Makpaisit, Putchong Uthayopas

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Tags: Compilers, Computer science, CUDA, GPU cluster, nVidia, OpenACC, Tesla M2050

July 3, 2014 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

See all packages

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high performance computing on graphics processing units: hgpu.org

Two-way partitioning of a recursive Gaussian filter in CUDA

SimCommSys: taking the errors out of error-correcting code simulations

A Parallelized Implementation for H. 264 Real-time Encoding Scheme

High-level Parallel Programming Support for Heterogeneous Systems

Writing self-adaptive codes for heterogeneous systems

Molecular dynamics simulations through GPU video games technologies

A second generation of DEFG: Declarative Framework for GPUs

The Design and Implementation of a GPU-enabled Multi-objective Tabu-search Intended for Real World and High-dimensional Applications

A Road Marking Extraction Method Using GPGPU

Exploiting parallel features of modern computer architectures in bioinformatics: applications to genetics, structure comparison and large graph analysis

The Framework and Compilation Techniques for Directive-based GPU Cluster Programming

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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