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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

MLitB: Machine Learning in the Browser

Edward Meeds, Remco Hendriks, Said al Faraby, Magiel Bruntink, Max Welling

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Tags: Algorithms, Computer science, Distributed computing, Heterogeneous systems, Java, Machine learning, Neural networks, Package

December 9, 2014 by hgpu

Heterogeneous High Throughput Scientific Computing with APM X-Gene and Intel Xeon Phi

David Abdurachmanov, Brian Bockelman, Peter Elmer, Giulio Eulisse, Robert Knight, Shahzad Muzaffar

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Tags: ARM, Computational Physics, Computer science, Distributed computing, Heterogeneous systems, High Energy Physics - Experiment, Intel Xeon Phi, Physics

December 5, 2014 by hgpu

Dogwild! – Distributed Hogwild for CPU & GPU

Cyprien Noel, Simon Osindero

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Tags: Computer science, Computer vision, CUDA, Distributed computing, Machine learning, Neural networks, nVidia

November 9, 2014 by hgpu

A Software Framework for the Detection and Classification of Biological Targets in Bio-Nano Sensing

Abdul Hafeez

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Tags: Biology, Biophysics, CUDA, Distributed computing, Filtering, nVidia, nVidia Quadro FX 580, Sensing, Thesis

September 19, 2014 by hgpu

WarpDrive: Extremely Fast End-to-End Deep Multi-Agent Reinforcement Learning on a GPU

MLIR: Multi-Level Intermediate Representation

High Performance GPU Code Generation for Matrix-Matrix Multiplication using MLIR: Some Early Results

SeisSol: High Resolution Simulation os Seismic Wave Propagation in Realistic Media with Complex Geomerty

Performance Portability and Evaluation of Heterogeneous Components of SeisSol Targeted to Upcoming Intel HPC GPUs

perf4sight: A toolflow to model CNN training performance on Edge GPUs

Parament: Parallelized Matrix Exponentiation for Numerical Time evolution

Parallel time integration using Batched BLAS (Basic Linear Algebra Subprograms) routines

ndzip: A High-Throughput Parallel Lossless Compressor for Scientific Data

ndzip-gpu: Efficient Lossless Compression of Scientific Floating-Point Data on GPUs

ScaleHLS Project

ScaleHLS: Scalable High-Level Synthesis through MLIR

See all packages

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high performance computing on graphics processing units: hgpu.org

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

MLitB: Machine Learning in the Browser

Heterogeneous High Throughput Scientific Computing with APM X-Gene and Intel Xeon Phi

Dogwild! – Distributed Hogwild for CPU & GPU

A Software Framework for the Detection and Classification of Biological Targets in Bio-Nano Sensing

Recent source codes

HPCToolkit

NVPTX-SPIR-V Translator

Taichi Lattice Boltmann

WarpDrive: Extremely Fast End-to-End Deep Multi-Agent Reinforcement Learning on a GPU

MLIR: Multi-Level Intermediate Representation

SeisSol: High Resolution Simulation os Seismic Wave Propagation in Realistic Media with Complex Geomerty

perf4sight: A toolflow to model CNN training performance on Edge GPUs

Parament: Parallelized Matrix Exponentiation for Numerical Time evolution

ndzip: A High-Throughput Parallel Lossless Compressor for Scientific Data

ScaleHLS Project

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