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Performance benchmarking of deep learning framework on Intel Xeon Phi

Chao-Tung Yang, Jung-Chun Liu, Yu-Wei Chan, Endah Kristiani, Chan-Fu Kuo

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Tags: Benchmarking, Caffe, Computer science, Deep learning, Image recognition, Intel Xeon Phi, MPI, OpenMP, Performance, TensorFlow

June 28, 2020 by hgpu

Autotuning for Automatic Parallelization on Heterogeneous Systems

Philip Pfaffe

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Tags: Auto-Tuning, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GTX 970, OpenMP, Performance, Tesla P100, Thesis

June 21, 2020 by hgpu

Evaluating the performance of HPC-style SYCL applications

Tom Deakin, Simon McIntosh-Smith

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Tags: Benchmarking, Computer science, Heterogeneous systems, OpenCL, OpenMP, Package, Performance, SYCL

May 31, 2020 by hgpu

Heterogeneous CPU/GPU co-execution of CFD simulations on the POWER9 architecture: Application to airplane aerodynamics

R. Borrell, D. Dosimont, M. Garcia-Gasulla, G. Houzeaux, O. Lehmkuhl, V. Mehta, H. Owen, M. Vazquez, G. Oyarzun

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Tags: cfd, CUDA, Fluid dynamics, Heterogeneous systems, MPI, nVidia, OpenMP, Tesla V100

May 17, 2020 by hgpu

From Constraint Programming to Heterogeneous Parallelism

Philip Ginsbach

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Tags: AMD Radeon R7, ATI, Code generation, Computer science, DSL, Heterogeneous systems, nVidia, nVidia GeForce GTX Titan X, OpenCL, OpenMP, Thesis

May 10, 2020 by hgpu

Pushing the limit of molecular dynamics with ab initio accuracy to 100 million atoms with machine learning

Weile Jia, Han Wang, Mohan Chen, Denghui Lu, Jiduan Liu, Lin Lin, Roberto Car, Weinan E, Linfeng Zhang

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Tags: Computational Physics, CUDA, HPC, Machine learning, Mixed precision, Molecular dynamics, Molecular simulation, MPI, nVidia, OpenMP, Physics, Tesla V100

May 10, 2020 by hgpu

AutoParBench: A Unified Test Framework for OpenMP-based Parallelizers

Gleison Souza Diniz Mendonça, Chunhua Liao, Fernando Magno Quintão Pereira

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Tags: Benchmarking, Code generation, Computer science, OpenMP, Package

May 3, 2020 by hgpu

Optimizing Streaming Parallelism on Heterogeneous Many-Core Architectures

Peng Zhang, Jianbin Fang, Canqun Yang, Chun Huang, Tao Tang, Zheng Wang

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Tags: Analytical model, Computer science, CUDA, Heterogeneous systems, Intel Xeon Phi, Machine learning, nVidia, nVidia GeForce GTX 1080 Ti, OpenCL, OpenMP, Package

March 22, 2020 by hgpu

EASYPAP: a Framework for Learning Parallel Programming

Alice Lasserre, Raymond Namyst, Pierre-André Wacrenier

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Tags: Algorithms, AVX, Computer science, Education, MPI, OpenCL, OpenMP, Package, Pthreads

February 16, 2020 by hgpu

Automated Runtime Analysis and Adaptation for Scalable Heterogeneous Computing

Ahmed E. Helal

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Tags: Computer science, CUDA, HPC, nVidia, OpenCL, OpenMP, Package, Performance, Tesla M2050, Thesis

February 9, 2020 by hgpu

Using Parallel Programming Models for Automotive Workloads on Heterogeneous Systems – a Case Study

Lukas Sommer, Florian Stock, Leonardo Solis-Vasquez, Andreas Koch

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Tags: Automotive, Benchmarking, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia Jetson AGX Xavier, nVidia Jetson TX2, OpenCL, OpenMP

January 26, 2020 by hgpu

Efficient Radial Pattern Keyword Search on Knowledge Graphs in Parallel

Yueji Yang, Anthony K. H. Tung

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Tags: Computer science, CUDA, Databases, nVidia, nVidia GeForce GTX 1080 Ti, OpenMP

January 26, 2020 by hgpu

Genomics-GPU: A Benchmark Suite for GPU-accelerated Genome Analysis

Dragon-Alpha&cu32: A Java-based Tensor Computing Framework With its High-Performance CUDA Library

Beehive SPIR-V Toolkit

Experiences in Building a Composable and Functional API for Runtime SPIR-V Code Generation

TorchBench: a collection of open source benchmarks used to evaluate PyTorch performance

TorchBench: Benchmarking PyTorch with High API Surface Coverage

Prediction of Performance and Power Consumption of GPGPU Applications

Descend: an attempt at safe imperative GPU programming

Descend: A Safe GPU Systems Programming Language

Redwood heterogeneous framework

Redwood: Flexible and Portable Heterogeneous Tree Traversal Workloads

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high performance computing on graphics processing units: hgpu.org

Performance benchmarking of deep learning framework on Intel Xeon Phi

Autotuning for Automatic Parallelization on Heterogeneous Systems

Evaluating the performance of HPC-style SYCL applications

Heterogeneous CPU/GPU co-execution of CFD simulations on the POWER9 architecture: Application to airplane aerodynamics

From Constraint Programming to Heterogeneous Parallelism

Pushing the limit of molecular dynamics with ab initio accuracy to 100 million atoms with machine learning

AutoParBench: A Unified Test Framework for OpenMP-based Parallelizers

Optimizing Streaming Parallelism on Heterogeneous Many-Core Architectures

EASYPAP: a Framework for Learning Parallel Programming

Automated Runtime Analysis and Adaptation for Scalable Heterogeneous Computing

Using Parallel Programming Models for Automotive Workloads on Heterogeneous Systems – a Case Study

Efficient Radial Pattern Keyword Search on Knowledge Graphs in Parallel

Recent source codes

Genomics-GPU: A Benchmark Suite for GPU-accelerated Genome Analysis

AutoDock for GPUs and other accelerators

spotPython: Sequential Parameter Optimization in Python

Test benchmarks for CELERITY

Dragon-Alpha&cu32: A Java-based Tensor Computing Framework With its High-Performance CUDA Library

Beehive SPIR-V Toolkit

TorchBench: a collection of open source benchmarks used to evaluate PyTorch performance

Prediction of Performance and Power Consumption of GPGPU Applications

Descend: an attempt at safe imperative GPU programming

Redwood heterogeneous framework

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