high performance computing on graphics processing units: hgpu.org

hgpu.org » Intel

Assessing the feasibility of OpenCL CPU implementations for agent-based simulations

Nuno Fachada, Agostinho C. Rosa

View

Tags: Agent-based modeling, AMD, cpu, Intel, OpenCL, simulation

July 24, 2019 by fakenmc

Automated Testing of Graphics Shader Compilers

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

View

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

View

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

March 5, 2016 by hgpu

A Survey of Architectural Techniques For Improving Cache Power Efficiency

Sparsh Mittal

View

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

View

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

View

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

View

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

View

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

View

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

View

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

View

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

View

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

July 14, 2013 by hgpu

PASTA: A Parallel Sparse Tensor Algorithm Benchmark Suite

A Parallel Sparse Tensor Benchmark Suite on CPUs and GPUs

TACO: The Tensor Algebra Compiler (taco) computes sparse tensor expressions on CPUs and GPUs

A Unified Iteration Space Transformation Framework for Sparse and Dense Tensor Algebra

OpenCL-FPGA-LLVM: LLVM-based automation of memory decoupling for OpenCL applications on FPGAs

LLVM-based automation of memory decoupling for OpenCL applications on FPGAs

CUDAnative.jl: Julia support for native CUDA programming

Abstractions for Programming Graphics Processors in High-Level Programming Languages

GPU explicit-state model checking

Porting tree-based hash table compression to GPGPU model checking

OCLADock: OpenCL Accelerated Molecular Docking

Accelerating Molecular Docking by Parallelized Heterogeneous Computing – A Case Study of Performance, Quality of Results, and Energy-Efficiency using CPUs, GPUs, and FPGAs

jax-md: Differentiable, Hardware Accelerated, Molecular Dynamics

JAX, M.D.: End-to-End Differentiable, Hardware Accelerated, Molecular Dynamics in Pure Python

libmolgrid: Comprehensive library for fast, GPU accelerated molecular gridding for deep learning workflows

libmolgrid: GPU Accelerated Molecular Gridding for Deep Learning Applications

Single-Assignment C Base

Array Languages Make Neural Networks Fast

PyTorch: An Imperative Style, High-Performance Deep Learning Library

PyTorch: An Imperative Style, High-Performance Deep Learning Library

See all packages

* * *

* * *

* * *

2020

April

27-29

Munich, Germany

8th International Workshop on OpenCL, including SYCLCon, 2019

2019

May

13-15

Boston, USA

7th International Workshop on OpenCL, 2019

2019

May

13

Boston, MA, USA

Distributed Heterogeneous Programming in C/C++ (DHPCC++), 2019

2019

May

14

Larnaca, Cyprus

3rd International Workshop on Theoretical Approaches to Performance Evaluation, Modeling and Simulation (TAPEMS), 2019

2019

July

26-28

Rome, Italy

International Conference on Frontiers of Neural Networks (ICFNN), 2019

+

HGPU group © 2010-2020 hgpu.org

All rights belong to the respective authors

Login | Sitemap | Feedback | Policy

Contact us: