high performance computing on graphics processing units: hgpu.org

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Automated Testing of Graphics Shader Compilers

Alastair F. Donaldson, Hugues Evrard, Andrei Lascu, Paul Thomson

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Tags: AMD Radeon R9 Fury, ARM, ATI, ATI Radeon HD 7700, Compilers, Computer science, GLSL, Intel, nVidia, nVidia GeForce GTX 770, nVidia GeForce GTX Titan X, OpenGL, Rendering, Security, Shaders, WebGL

September 21, 2017 by hgpu

Input Space Splitting for OpenCL

Simon Moll, Johannes Doerfert, Sebastian Hack

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Tags: Compilers, Computer science, Intel, LLVM, OpenCL

March 5, 2016 by hgpu

A Survey of Architectural Techniques For Improving Cache Power Efficiency

Sparsh Mittal

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Tags: Architecture, cache, cpu, Energy-efficient computing, GPU, Intel, Memory, Power-efficient computing

January 19, 2015 by sparsh0mittal

Preliminary Experiments with XKaapi on Intel Xeon Phi Coprocessor

Joao Vicente Ferreira Lima, Francois Broquedis, Thierry Gautier, Bruno Raffin

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Tags: Computer science, Factorization, Intel, Intel Phi, Intel Xeon Phi

November 11, 2013 by hgpu

Understanding the Costs of Many-Task Computing Workloads on Intel Xeon Phi Coprocessors

Jeffrey Johnson, Scott J. Krieder, Benjamin Grimmer, Justin M. Wozniaky, Michael Wildeyz, Ioan Raicu

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Tags: Computer science, Heterogeneous systems, Intel, Intel Phi

October 21, 2013 by hgpu

OpenCL Task Partitioning in the Presence of GPU Contention

Dominik Grewe, Zheng Wang, and Michael F.P. O'Boyle

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Tags: Computer science, Heterogeneous systems, Intel, Intel HD Graphics 4000, Machine learning, OpenCL, Task scheduling

September 25, 2013 by hgpu

Performance Evaluation of Intel Xeon Phi Coprocessor using XKaapi

Joao V. F. Lima, Nicolas Maillard, Franciois Broquedis, Thierry Gautier, Bruno Raffin

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Tags: Benchmarking, Computer science, Intel, Intel Phi, Performance

August 26, 2013 by hgpu

First experiences with the Intel MIC architecture at LRZ

Volker Weinberg, Momme Allalen

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Tags: Computer science, Intel, Intel Phi, OpenMP, Performance

August 15, 2013 by hgpu

Compiler-based Data Prefetching and Streaming Non-temporal Store Generation for the Intel Xeon Phi Coprocessor

Rakesh Krishnaiyer, Emre Kultursay, Pankaj Chawla, Serguei Preis, Anatoly Zvezdin, Hideki Saito

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Tags: Compilers, Computer science, Intel, Intel Phi, Prefetch

August 8, 2013 by hgpu

Lattice QCD on Intel Xeon Phi

Balint Joo, Dhiraj D. Kalamkar, Karthikeyan Vaidyanathan, Mikhail Smelyanskiy, Kiran Pamnany, Victor W Lee, Pradeep Dubey, William Watson III

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Tags: CUDA, High Energy Physics - Lattice, Intel, Intel Phi, nVidia, Physics, QCD, Tesla K20

July 20, 2013 by hgpu

Benchmarking Intel Xeon Phi to Guide Kernel Design

Jianbin Fang, Ana Lucia Varbanescu, Henk Sips, Lilun Zhang, Yonggang Che, Chuanfu Xu

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Tags: Benchmarking, Computer science, Intel, Intel Phi, Performance

July 14, 2013 by hgpu

Identifying the Key Features of Intel Xeon Phi: A Comparative Approach

Jianbin Fang, Ana Lucia Varbanescu, Henk Sips

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Tags: Computer science, Intel, Intel Phi, Performance

July 13, 2013 by hgpu

Spatter: Benchmark for measuring the performance of sparse and irregular memory access

Spatter: A Benchmark Suite for Evaluating Sparse Access Patterns

StePS: Stereographically Projected Cosmological Simulations

StePS: A Multi-GPU Cosmological N-body Code for Compactified Simulations

AMGCL:C++ library for solving large sparse linear systems with algebraic multigrid method

AMGCL: an Efficient, Flexible, and Extensible Algebraic Multigrid Implementation

PAstudy

Double-precision FPUs in High-Performance Computing: an Embarrassment of Riches?

hpx: The C++ Standard Library for Parallelism and Concurrency

Integration of CUDA Processing within the C++ library for parallelism and concurrency (HPX)

OpenDwarfs: a benchmark suite consisting of different computation/communication idioms, i.e., dwarfs, for state-of-art multicore CPUs, GPUs, Intel MICs and Altera FPGAs

OpenCL Performance Prediction using Architecture-Independent Features

Rodinia Benchmark Suite ported for OpenCL-capable Intel FPGAs

High Performance Computing with FPGAs and OpenCL

The Ocean Tensor Package

Tornado

Using Compiler Snippets to Exploit Parallelism on Heterogeneous Hardware: A Java Reduction Case Study

pocl: Portable Computing Language

Exploiting Task Parallelism with OpenCL: A Case Study

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high performance computing on graphics processing units: hgpu.org

Automated Testing of Graphics Shader Compilers

Input Space Splitting for OpenCL

A Survey of Architectural Techniques For Improving Cache Power Efficiency

Preliminary Experiments with XKaapi on Intel Xeon Phi Coprocessor

Understanding the Costs of Many-Task Computing Workloads on Intel Xeon Phi Coprocessors

OpenCL Task Partitioning in the Presence of GPU Contention

Performance Evaluation of Intel Xeon Phi Coprocessor using XKaapi

First experiences with the Intel MIC architecture at LRZ

Compiler-based Data Prefetching and Streaming Non-temporal Store Generation for the Intel Xeon Phi Coprocessor

Lattice QCD on Intel Xeon Phi

Benchmarking Intel Xeon Phi to Guide Kernel Design

Identifying the Key Features of Intel Xeon Phi: A Comparative Approach

Recent source codes

Spatter: Benchmark for measuring the performance of sparse and irregular memory access

StePS: Stereographically Projected Cosmological Simulations

AMGCL:C++ library for solving large sparse linear systems with algebraic multigrid method

PAstudy

hpx: The C++ Standard Library for Parallelism and Concurrency

OpenDwarfs: a benchmark suite consisting of different computation/communication idioms, i.e., dwarfs, for state-of-art multicore CPUs, GPUs, Intel MICs and Altera FPGAs

Rodinia Benchmark Suite ported for OpenCL-capable Intel FPGAs

The Ocean Tensor Package

Tornado

pocl: Portable Computing Language

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International Conference on Image, Video and Signal Processing (IVSP), 2019

International Joint Conference on Signals, Systems and Computers (CSSC), 2018

International Joint Conference on Robotics, Automation and Mechatronics (JCRAM), 2018

International Joint Conference on Robotics and Artificial Intelligence (JCRAI), 2018

International Joint Conference on Computer Vision and Pattern Recognition (CCVPR), 2018