high performance computing on graphics processing units: hgpu.org

Hyung Mok Lee, Eric-Olivier Le Bigot, ZhiHui Du, ZhangXi Lin, XiangYu Guo, LinQing Wen, Khun Sang Phukon, Vihan Pandey, Sukanta Bose, Xi-Long Fan, Martin Hendry

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Tags: Astrophysics, CUDA, Gravitation, Gravitational waves, nVidia, nVidia GeForce GTX 480, nVidia GeForce GTX 980, Tesla K20

February 19, 2016 by hgpu

Deep Feature-based Face Detection on Mobile Devices

Sayantan Sarkar, Vishal M. Patel, Rama Chellappa

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Tags: Computer science, Computer vision, Deep learning, nVidia, nVidia GeForce GTX 850 M, OpenCL, RenderScript

February 18, 2016 by hgpu

Speeding Up Reinforcement Learning with Graphics Processing Units

Jorn H. Postma

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Tags: Algorithms, Computer science, KD-tree, Machine learning, nVidia, OpenCL, Tesla C2075, Thesis

February 18, 2016 by hgpu

SABER: Window-Based Hybrid Stream Processing for Heterogeneous Architectures

Alexandros Koliousis, Matthias Weidlich, Raul Castro Fernandez, Alexander L. Wolf, Paolo Costa, Peter Pietzuch

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Tags: Computer science, Databases, Heterogeneous systems, nVidia, nVidia Quadro K5200, OpenCL

February 16, 2016 by hgpu

CaffeLink: Mathematica binding for Caffe Deep Learning Framework

Martin Kerhart, Jan Drchal

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Tags: Caffe, Computer science, CUDA, Deep learning, Machine learning, Neural networks, nVidia, Package

February 16, 2016 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

* * *

high performance computing on graphics processing units: hgpu.org

VirtCL: a framework for OpenCL device abstraction and management

Deep Learning At Scale and At Ease

Sparse Convex Optimization on GPUs

Togpu: Automatic Source Transformation from C++ to CUDA using Clang/LLVM

A GPU-based Large-scale Monte Carlo Simulation Method for Systems with Long-range Interactions

HeSP: a simulation framework for solving the task scheduling-partitioning problem on heterogeneous architectures

Automatic and portable mapping of data parallel programs to OpenCL for GPU-based heterogeneous systems

Gravitational wave astrophysics, data analysis and multimessenger astronomy

Deep Feature-based Face Detection on Mobile Devices

Speeding Up Reinforcement Learning with Graphics Processing Units

SABER: Window-Based Hybrid Stream Processing for Heterogeneous Architectures

CaffeLink: Mathematica binding for Caffe Deep Learning Framework

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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