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Modeling and Evaluation of Synchronous Stochastic Gradient Descent in Distributed Deep Learning on Multiple GPUs

Shaohuai Shi, Qiang Wang, Xiaowen Chu, Bo Li

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Tags: Artificial intelligence, Benchmarking, Computer science, CUDA, Deep learning, nVidia, nVidia GeForce GTX 1080, nVidia GeForce GTX 980, Package, Performance, Tesla K80

May 12, 2018 by hgpu

Parallel Programming for FPGAs

Ryan Kastner, Janarbek Matai, Stephen Neuendorffer

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Tags: Book, Computer science, FPGA, HLS

May 12, 2018 by hgpu

Efficient Hardware Acceleration on SoC-FPGA with OpenCL

Susmitha Gogineni

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Tags: Computer science, Design space exploration, FPGA, Heterogeneous systems, HLS, OpenCL, Thesis

May 12, 2018 by hgpu

Simulating Quantum Computers Using OpenCL

Adam Kelly

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Tags: Computer science, nVidia, OpenCL, Package, Quantum computing, Quantum Physics, Tesla V100

May 5, 2018 by hgpu

Auto-tuning Streamed Applications on Intel Xeon Phi

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

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Tags: Computer science, Intel Xeon Phi, Machine learning, Package, Performance

May 5, 2018 by hgpu

Tiramisu: A Code Optimization Framework for High Performance Systems

Riyadh Baghdadi, Jessica Ray, Malek Ben Romdhane, Emanuele Del Sozzo, Patricia Suriana, Shoaib Kamil, Saman Amarasinghe

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Tags: Algorithms, Code generation, Computer science, CUDA, Distributed computing, FPGA, Heterogeneous systems, Linear Algebra, LLVM, MPI, nVidia, OpenMPI, Tesla K40

May 5, 2018 by hgpu

Multidimensional Parallelization for Streaming Text Processing Applications Based on Parabix Framework

Dan Lin

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Tags: Algorithms, Computer science, Data parallelism, LLVM, nVidia, nVidia GeForce GTX 950, Package, PTX, Thesis

April 28, 2018 by hgpu

A Comparative Study on Exact Triangle Counting Algorithms on the GPU

Leyuan Wang, Yangzihao Wang, Carl Yang, John D. Owens

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Tags: Algorithms, Computer science, CUDA, Filtering, Graph theory, nVidia, Sparse matrix, Tesla K40

April 28, 2018 by hgpu

A Strategy for Automatic Performance Tuning of Stencil Computations on GPUs

Joseph D. Garvey, Tarek S. Abdelrahman

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Tags: AMD Radeon R9 390, ATI, Computer science, Machine learning, nVidia, nVidia GeForce GTX Titan, OpenCL, Performance, Stencil computation

April 28, 2018 by hgpu

Accelerating Blockchain Search of Full Nodes Using GPUs

Shin Morishima, Hiroki Matsutani

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Tags: Computer science, CUDA, Finance, nVidia, nVidia GeForce GTX 980 Ti

April 28, 2018 by hgpu

Automatic generation of CUDA code performing tensor manipulations using C++ expression templates

Adam G.M. Lewis, Harald P. Pfeiffer

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Tags: Benchmarking, Code generation, Computer science, CUDA, General Relativity and Quantum Cosmology, nVidia, Tesla K80, Tesla M2090, Tesla P100

April 28, 2018 by hgpu

BrainSlug: Transparent Acceleration of Deep Learning Through Depth-First Parallelism

Nicolas Weber, Florian Schmidt, Mathias Niepert, Felipe Huici

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Tags: AI, Artificial intelligence, cache, CNN, Computer science, cpu, CUDA, Deep learning, GPU, Machine learning, Neural and Evolutionary Computing, nVidia, nVidia GeForce GTX 1080 Ti

April 25, 2018 by hgpu

HPCToolkit

Refining HPCToolkit for application performance analysis at exascale

Open Catalog of Best Practices for Modernization and Optimization

Optimizing the Weather Research and Forecasting Model with OpenMP Offload and Codee

Owl: Differential-based Side-Channel Leakage Detection for CUDA Applications

pSTL-Bench: Micro-benchmark suite designed to evaluate the scalability and efficiency of parallel C++ STL implementations

Exploring Scalability in C++ Parallel STL Implementations

$3D-fractal-generators$

3D-fractal-generators

Double-Precision Floating-Point Data Visualizations Using Vulkan API

VerCors: a verification toolset for verifying parallel and concurrent software

The VerCors Verifier: A Progress Report

HiCCL: A hierarchical collective communications library with portable optimizations

HiCCL: A Hierarchical Collective Communication Library

See all packages

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high performance computing on graphics processing units: hgpu.org

Modeling and Evaluation of Synchronous Stochastic Gradient Descent in Distributed Deep Learning on Multiple GPUs

Parallel Programming for FPGAs

Efficient Hardware Acceleration on SoC-FPGA with OpenCL

Simulating Quantum Computers Using OpenCL

Auto-tuning Streamed Applications on Intel Xeon Phi

Tiramisu: A Code Optimization Framework for High Performance Systems

Multidimensional Parallelization for Streaming Text Processing Applications Based on Parabix Framework

A Comparative Study on Exact Triangle Counting Algorithms on the GPU

A Strategy for Automatic Performance Tuning of Stencil Computations on GPUs

Accelerating Blockchain Search of Full Nodes Using GPUs

Automatic generation of CUDA code performing tensor manipulations using C++ expression templates

BrainSlug: Transparent Acceleration of Deep Learning Through Depth-First Parallelism

Recent source codes

HPCToolkit

Open Catalog of Best Practices for Modernization and Optimization

Owl: Differential-based Side-Channel Leakage Detection for CUDA Applications

pSTL-Bench: Micro-benchmark suite designed to evaluate the scalability and efficiency of parallel C++ STL implementations

GPU-Smash: Compression abstraction library

Noarr Structures

SyncPerformance

3D-fractal-generators

VerCors: a verification toolset for verifying parallel and concurrent software

HiCCL: A hierarchical collective communications library with portable optimizations

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