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

Deep Learning on FPGAs: Past, Present, and Future

Griffin Lacey, Graham W. Taylor, Shawki Areibi

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Tags: Artificial intelligence, Computer science, Deep learning, FPGA, Machine learning, OpenCL, Overview

February 17, 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

Over-synchronization in GPU Programs

profile_util: simple C++ library to report memory, timing, thread affinity

Profile Util library: A quick and easy way to get MPI, OpenMP and GPU runtime information

MaC: Mamba CPU performance predictor

MambaCPU: Enhanced Correlation Mining with State Space Models for CPU Performance Prediction

Mixed-precision finite element kernels

Mixed-precision finite element kernels and assembly: Rounding error analysis and hardware acceleration

SHMT: The Simultaneous and Heterogenous Multithreading project

General-Purpose Computing on Tensor Processors

Superpipeline: A Universal Approach for Reducing GPU Memory Usage in Large Models

EnergyUCB-Bandit

Online Energy Optimization in GPUs: A Multi-Armed Bandit Approach

See all packages

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high performance computing on graphics processing units: hgpu.org

Applications

Sparse Convex Optimization on GPUs

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

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

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

Gravitational wave astrophysics, data analysis and multimessenger astronomy

Deep Feature-based Face Detection on Mobile Devices

Speeding Up Reinforcement Learning with Graphics Processing Units

Deep Learning on FPGAs: Past, Present, and Future

SABER: Window-Based Hybrid Stream Processing for Heterogeneous Architectures

CaffeLink: Mathematica binding for Caffe Deep Learning Framework

Recent source codes

Kokkidio: Kokkos+Eigen - write fast, readable multi-platform code

LLaMA-Inference-Bench

MATAR

Over-synchronization in GPU Programs

profile_util: simple C++ library to report memory, timing, thread affinity

MaC: Mamba CPU performance predictor

Mixed-precision finite element kernels

SHMT: The Simultaneous and Heterogenous Multithreading project

Superpipeline: A Universal Approach for Reducing GPU Memory Usage in Large Models

EnergyUCB-Bandit

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