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dOpenCL – Evaluation of an API-Forwarding Implementation

Karsten Tausche, Max Plauth, Andreas Polze

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Tags: Computer science, Distributed computing, MPI, OpenCL, Package, Virtualization

December 31, 2016 by hgpu

dCUDA: hardware supported overlap of computation and communication

Tobias Gysi, Jeremia Bär, Torsten Hoefler

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Tags: Distributed computing, latency hiding, programming model, remote memory access

November 29, 2016 by gysit

Vispark: GPU-Accelerated Distributed Visual Computing Using Spark

Woohyuk Choi, Sumin Hong, Won-Ki Jeong

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Tags: Clustering, Computer science, CUDA, Distributed computing, DSL, GPU cluster, Image processing, MapReduce, nVidia, nVidia GeForce GTX Titan, Python, Rendering, Spark

November 8, 2016 by hgpu

Bridging the Semantic Gaps of GPU Acceleration for Scaleout CNN-based Big Data Processing: Think Big, See Small

Mingcong Song, Yang Hu, Yunlong Xu, Chao Li, Huixiang Chen, Jingling Yuan, Tao Li

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Tags: Analytical model, big data, Computer science, CUDA, Deep learning, Distributed computing, Neural networks, nVidia, Tesla K20

September 22, 2016 by hgpu

DeepSpark: Spark-Based Deep Learning Supporting Asynchronous Updates and Caffe Compatibility

Hanjoo Kim, Jaehong Park, Jaehee Jang, Sungroh Yoon

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Tags: Caffe, Computer science, CUDA, Deep learning, Distributed computing, Java, Machine learning, Neural networks, nVidia, nVidia GeForce GTX 970, Package

March 1, 2016 by hgpu

High Performance Adaptive Image Processing on Multi-scale Hybrid Architectures

Fangbin Liu

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Tags: Algorithms, CUDA, Distributed computing, Heterogeneous systems, Image processing, Matrix multiplication, nVidia, nVidia GeForce GTX 295, Thesis

November 10, 2015 by hgpu

Locality Aware Work-Stealing Based Scheduling in Hybrid CPU-GPU

A. Tarun Beri, B. Sorav Bansal, C. Subodh Kumar

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Tags: Computer science, CUDA, Distributed computing, nVidia, Task scheduling, Tesla M2070

August 21, 2015 by hgpu

SINGA: Putting Deep Learning in the Hands of Multimedia Users

Wei Wang, Gang Chen, Tien Tuan Anh Dinh, Jinyang Gao, Beng Chin Ooi, Kian-Lee Tan, Sheng Wang

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Tags: Computer science, Computer vision, Deep learning, Distributed computing, Package

August 11, 2015 by hgpu

Shortest-Path Queries in Planar Graphs on GPU-Accelerated Architectures

Guillaume Chapuis, Hristo Djidjev

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Tags: Computer science, Data Structures and Algorithms, Distributed computing, Distributed data structures, GPU cluster, nVidia, Path problems, Tesla M2090

March 28, 2015 by hgpu

Energy-efficient Computing on Distributed GPUs using Dynamic Parallelism and GPU-controlled Communication

Lena Oden, Benjamin Klenk, Holger Froning

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Tags: Cluster computing, Computer science, CUDA, Distributed computing, Energy-efficient computing, GPU cluster, nVidia, Tesla K20

March 25, 2015 by hgpu

Hybrid Multicore Algorithms for Some Semi-Numerical Applications and Graphs

Dip Sankar Banerjee

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Tags: Algorithms, Cell processor, Computer science, CUDA, Distributed computing, List ranking, nVidia, nVidia GeForce GTX 280, nVidia GeForce GTX 580, OpenCL, Sorting, Thesis

January 19, 2015 by hgpu

Legion: Programming Distributed Heterogeneous Architectures with Logical Regions

Michael Edward Bauer

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Tags: Computer science, CUDA, Distributed computing, Heterogeneous systems, nVidia, Package, Tesla K20, Tesla M2090, Thesis

December 22, 2014 by hgpu

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A Container-Based Workflow for Distributed Training of Deep Learning Algorithms in HPC Clusters

ARMCL-PipeALL: an integrated high-throughput CPU-GPU CNN inference pipeline design for ARM-based Heterogeneous MPSoCs

CPU-GPU Layer-Switched Low Latency CNN Inference

EngineCL for Stratix10 MX

FPGA Accelerators on Heterogeneous Systems: An Approach Using High Level Synthesis

Theseus: A Library for Differentiable Nonlinear Optimization

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high performance computing on graphics processing units: hgpu.org

dOpenCL – Evaluation of an API-Forwarding Implementation

dCUDA: hardware supported overlap of computation and communication

Vispark: GPU-Accelerated Distributed Visual Computing Using Spark

Bridging the Semantic Gaps of GPU Acceleration for Scaleout CNN-based Big Data Processing: Think Big, See Small

DeepSpark: Spark-Based Deep Learning Supporting Asynchronous Updates and Caffe Compatibility

High Performance Adaptive Image Processing on Multi-scale Hybrid Architectures

Locality Aware Work-Stealing Based Scheduling in Hybrid CPU-GPU

SINGA: Putting Deep Learning in the Hands of Multimedia Users

Shortest-Path Queries in Planar Graphs on GPU-Accelerated Architectures

Energy-efficient Computing on Distributed GPUs using Dynamic Parallelism and GPU-controlled Communication

Legion: Programming Distributed Heterogeneous Architectures with Logical Regions

Recent source codes

A Container-Based Workflow for Distributed Training of Deep Learning Algorithms in HPC Clusters

ARMCL-PipeALL: an integrated high-throughput CPU-GPU CNN inference pipeline design for ARM-based Heterogeneous MPSoCs

EngineCL for Stratix10 MX

Theseus: A Library for Differentiable Nonlinear Optimization

OmpCluster

PipeCNN: An OpenCL-based FPGA Accelerator for Convolutional Neural Networks

hls4ml: Machine learning on FPGAs using HLS

MocCUDA: Prototype to run Pytorch/CUDA on Fugaku

Rodinia Benchmarks for DPC++

DCA++: a state of the art implementation of the dynamical cluster approximation

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