high performance computing on graphics processing units: hgpu.org

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hgpu.org » Programming » CUDA

Contract-Based General-Purpose GPU Programming

Alexey Kolesnichenko, Christopher M. Poskitt, Sebastian Nanz, Bertrand Meyer

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Tags: Code generation, Computer science, CUDA, nVidia, nVidia Quadro K 2000 M, Package, Software Engineering

October 27, 2014 by hgpu

GPGPU Acceleration for Skeletal Animation-comparing OpenCL with CUDA and GLSL

Shousheng Liu, Ge Chen, Chunyong Ma, Yong Han

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Tags: ATI, ATI Radeon HD 7750, Computer science, CUDA, GLSL, nVidia, nVidia GeForce GTS 250, nVidia GeForce GTX 670, OpenCL, OpenGL, Performance, Rendering

October 25, 2014 by hgpu

On the Efficiency of CPU and Hybrid CPU-GPU Systems in Computational Biology Tasks

Nikolay Pydiura, Pavel Karpov, Yaroslav Blume

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Tags: AMD Radeon R9 280, ATI, Biology, Computational biology, CUDA, Molecular dynamics, nVidia, nVidia GeForce GTX 480, OpenCL

October 25, 2014 by hgpu

Medical imaging using CUDA

Thomas Kovac

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Tags: CUDA, Image registration, Medicine, MRI, nVidia, Thesis

October 25, 2014 by hgpu

CUVLE: Variable-Length Encoding on CUDA

Jose M Gonzalez-Linares, Antonio Fuentes-Alventosa, Juan Gomez-Luna, Nicolas Guil

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Tags: Algorithms, Compression, Computer science, CUDA, nVidia, nVidia GeForce GTX 550 Ti

October 25, 2014 by hgpu

cufftShift: High Performance CUDA-accelerated FFT-shift Library

Marwan Abdellah

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Tags: Algorithms, CUDA, FFT, Image processing, nVidia, nVidia GeForce GTX 580, Package

October 24, 2014 by hgpu

Query Optimization in Heterogeneous CPU/GPU Environment for Time Series Databases

Piotr Przymus

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Tags: Algorithms, Compression, Computer science, CUDA, Data mining, Databases, Heterogeneous systems, nVidia, nVidia GeForce GTX 280, Tesla C2070, Tesla M2070, Thesis

October 24, 2014 by hgpu

Monitoring Large-scale Microblog on GPUs

Yunpeng Cao

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce 9400 GT, String matchning

October 24, 2014 by hgpu

Improved Integral Histogram Algorithm for Big Sized Images in CUDA Environment

Chang Won Lee, Tae-Young Choe

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Tags: Algorithms, CUDA, Image processing, nVidia, nVidia GeForce GTX 670

October 24, 2014 by hgpu

Introducing CURRENNT – the Munich open-source CUDA RecurREnt Neural Network Toolkit

Felix Weninger, Johannes Bergmann, Bjorn Schuller

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Tags: Computer science, CUDA, Neural networks, nVidia, nVidia GeForce GTX 560, Package, Speech recognition

October 22, 2014 by hgpu

Optimization Techniques for Mapping Algorithms and Applications onto CUDA GPU Platforms and CPU-GPU Heterogeneous Platforms

Jing Wu

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Tags: Computer science, CUDA, FFT, Heterogeneous systems, Laplace and Poisson equation, nVidia, nVidia GeForce GTX 280, nVidia GeForce GTX 480, Poisson equation, Tesla C1060, Tesla C2050, Thesis

October 22, 2014 by hgpu

Performance Engineering of the Kernel Polynomial Method on Large-Scale CPU-GPU Systems

Moritz Kreutzer, Georg Hager, Gerhard Wellein, Andreas Pieper, Andreas Alvermann, Holger Fehske

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Tags: Computational Engineering, CUDA, Finance, Mesoscale and Nanoscale Physics, nVidia, Tesla K20

October 22, 2014 by hgpu

MATLAB Tensor Core models

Accurate Models of NVIDIA Tensor Cores

TritonForge: Transform PyTorch Operations into Optimized GPU Kernels with LLMs

TritonForge: Profiling-Guided Framework for Automated Triton Kernel Optimization

RLTune: Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

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

NVIDIA Nemotron Parse 1.1

ThunderKittens: Tile primitives for speedy kernels

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

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

Fortran xDSL dialects

An MLIR pipeline for offloading Fortran to FPGAs via OpenMP

See all packages

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high performance computing on graphics processing units: hgpu.org

Programming

Contract-Based General-Purpose GPU Programming

GPGPU Acceleration for Skeletal Animation-comparing OpenCL with CUDA and GLSL

On the Efficiency of CPU and Hybrid CPU-GPU Systems in Computational Biology Tasks

Medical imaging using CUDA

CUVLE: Variable-Length Encoding on CUDA

cufftShift: High Performance CUDA-accelerated FFT-shift Library

Query Optimization in Heterogeneous CPU/GPU Environment for Time Series Databases

Monitoring Large-scale Microblog on GPUs

Improved Integral Histogram Algorithm for Big Sized Images in CUDA Environment

Introducing CURRENNT – the Munich open-source CUDA RecurREnt Neural Network Toolkit

Optimization Techniques for Mapping Algorithms and Applications onto CUDA GPU Platforms and CPU-GPU Heterogeneous Platforms

Performance Engineering of the Kernel Polynomial Method on Large-Scale CPU-GPU Systems

Recent source codes

MATLAB Tensor Core models

TritonForge: Transform PyTorch Operations into Optimized GPU Kernels with LLMs

RLTune: Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

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

hls4ml: Machine learning on FPGAs using HLS

NVIDIA Nemotron Parse 1.1

ThunderKittens: Tile primitives for speedy kernels

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

HipKittens: Fast and Furious AMD Kernels

Fortran xDSL dialects

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