high performance computing on graphics processing units: hgpu.org

Claudio Bonati, Enrico Calore, Simone Coscetti, Massimo D'Elia, Michele Mesiti, Francesco Negro, Sebastiano Fabio Schifano, Giorgio Silvi, Raffaele Tripiccione

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Tags: AMD FirePro W9100, ATI, Computational Physics, Heterogeneous systems, High Energy Physics - Lattice, nVidia, OpenACC, performance portability, Physics, QCD, Tesla K80, Tesla P100

January 4, 2017 by hgpu

Accelerating Lattice QCD Multigrid on GPUs Using Fine-Grained Parallelization

M. A. Clark, Balint Joo, Alexei Strelchenko, Michael Cheng, Arjun Gambhir, Richard Brower

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Tags: Algorithms, Computational Physics, CUDA, High Energy Physics - Lattice, nVidia, Physics, QCD, Tesla K20

December 26, 2016 by hgpu

An EoS-meter of QCD transition from deep learning

Long-Gang Pang, Kai Zhou, Nan Su, Hannah Petersen, Horst Stocker, Xin-Nian Wang

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Tags: CUDA, Deep learning, High Energy Physics - Phenomenology, Keras, Machine learning, Neural networks, Nuclear Theory, nVidia, Physics, QCD, TensorFlow, Tesla K20

December 20, 2016 by hgpu

Performance Optimisation of Smoothed Particle Hydrodynamics Algorithms for Multi/Many-Core Architectures

Fabio Baruffa, Luigi Iapichino, Nicolay J. Hammer, Vasileios Karakasis

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Tags: Algorithms, Astrophysics, Computational Physics, Computer science, Instrumentation and Methods for Astrophysics, Intel Xeon Phi, OpenMP, Performance, Physics

December 20, 2016 by hgpu

gpuSPHASE – A shared memory caching implementation for 2D SPH using CUDA

Daniel Winkler, Michael Meister, Massoud Rezavand, Wolfgang Rauch

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Tags: Algorithms, CUDA, Fluid dynamics, nVidia, nVidia GeForce GTX 750, nVidia GeForce GTX Titan, nVidia GeForce GTX Titan X, Physics

December 20, 2016 by hgpu

Speedup for quantum optimal control from GPU-based automatic differentiation

Nelson Leung, Mohamed Abdelhafez, Jens Koch, David I. Schuster

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Source codes

Tags: Algorithms, CUDA, Deep learning, Mesoscale and Nanoscale Physics, Numerical simulation, nVidia, Package, Physics, Python, Quantum Physics, TensorFlow, Tesla K40

December 17, 2016 by hgpu

Deep Learning for Compilers

PyMatting: A Python Library for Alpha Matting

vilib: CUDA Visual Library by RPG

Faster than FAST: GPU-Accelerated Frontend for High-Speed VIO

Error-bounded Lossy Data Compressor (for floating-point/integer datasets)

Understanding GPU-Based Lossy Compression for Extreme-Scale Cosmological Simulations

ProGraML: Graph-based Deep Learning for Program Optimization and Analysis

ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators

Optimizing Streaming Parallelism on Heterogeneous Many-Core Architectures

Learnergy: a Python library for energy-based machine learning models

Learnergy: Energy-based Machine Learners

Towards automated kernel selection in machine learning systems: A SYCL case study

CLFuzz

Automated test generation for OpenCL kernels using fuzzing and constraint solving

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high performance computing on graphics processing units: hgpu.org

Applications

Introduction to the Special Issue on Digital Signal Processing in Radio Astronomy

GraviDy: a GPU modular, parallel N-body integrator

Astrophysical-oriented Computational multi-Architectural Framework

An FPGA Accelerator for Molecular Dynamics Simulation Using OpenCL

Deep Neural Networks to Enable Real-time Multimessenger Astrophysics

Massively Parallel Computation of Accurate Densities for N-body Dark Matter Simulations using the Phase-Space-Element Method

Design and optimization of a portable LQCD Monte Carlo code using OpenACC

Accelerating Lattice QCD Multigrid on GPUs Using Fine-Grained Parallelization

An EoS-meter of QCD transition from deep learning

Performance Optimisation of Smoothed Particle Hydrodynamics Algorithms for Multi/Many-Core Architectures

gpuSPHASE – A shared memory caching implementation for 2D SPH using CUDA

Speedup for quantum optimal control from GPU-based automatic differentiation

Recent source codes

Deep Learning for Compilers

PyMatting: A Python Library for Alpha Matting

vilib: CUDA Visual Library by RPG

Error-bounded Lossy Data Compressor (for floating-point/integer datasets)

ProGraML: Graph-based Deep Learning for Program Optimization and Analysis

ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators

Optimizing Streaming Parallelism on Heterogeneous Many-Core Architectures

Learnergy: a Python library for energy-based machine learning models

Towards automated kernel selection in machine learning systems: A SYCL case study

CLFuzz

Most viewed papers (last 30 days)

Featured events

8th International Workshop on OpenCL, including SYCLCon, 2019

7th International Workshop on OpenCL, 2019

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

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

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