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Spatter: A Benchmark Suite for Evaluating Sparse Access Patterns

Patrick Lavin, Jason Riedy, Rich Vuduc, Jeffrey Young

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Tags: Benchmarking, Computer science, CUDA, FPGA, Intel Xeon Phi, nVidia, nVidia GeForce GTX Titan XP, OpenCL, Package, Performance, Tesla P100

November 18, 2018 by hgpu

Workload-aware Automatic Parallelization for Multi-GPU DNN Training

Sungho Shin, Youngmin Jo, Jungwook Choi, Swagath Venkataramani, Vijayalakshmi Srinivasan, Wonyong Sung

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Tags: Artificial intelligence, Computer science, Deep learning, Neural networks, nVidia, nVidia DGX-1, nVidia GeForce GTX Titan XP, nVidia Tesla GP100

November 11, 2018 by hgpu

A Hybrid GPU-FPGA-based Computing Platform for Machine Learning

Xu Liu, Hibat Allah Ounifi, Abdelouahed Gherbi, Yves Lemieux, Wubin Li

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Tags: Algorithms, Benchmarking, CNN, Computer science, FPGA, Hybrid computing, Machine learning, nVidia, nVidia GeForce GTX Titan XP, OpenCL

November 11, 2018 by hgpu

Parallax: Automatic Data-Parallel Training of Deep Neural Networks

Soojeong Kim, Gyeong-In Yu, Hojin Park, Sungwoo Cho, Eunji Jeong, Hyeonmin Ha, Sanha Lee, Joo Seong Jeong, Byung-Gon Chun

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Tags: Computer science, CUDA, Deep learning, Machine learning, Neural networks, nVidia, nVidia GeForce GTX Titan XP, OpenMPI, Package

August 11, 2018 by hgpu

Efficient Large-scale Approximate Nearest Neighbor Search on OpenCL FPGA

Jialiang Zhang, Soroosh Khoram, Jing Li

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Tags: Computer science, FPGA, Nearest neighbour, nVidia, nVidia GeForce GTX Titan XP, OpenCL

June 13, 2018 by hgpu

TBD: Benchmarking and Analyzing Deep Neural Network Training

Hongyu Zhu, Mohamed Akrout, Bojian Zheng, Andrew Pelegris, Amar Phanishayee, Bianca Schroeder, Gennady Pekhimenko

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Tags: Benchmarking, Computer science, CUDA, Deep learning, Machine learning, Neural networks, nVidia, nVidia GeForce GTX Titan XP, nVidia Quadro P 4000

March 22, 2018 by hgpu

CuLDA_CGS: Solving Large-scale LDA Problems on GPUs

Xiaolong Xie, Yun Liang, Xiuhong Li, Wei Tan

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Tags: Algorithms, Computer science, CUDA, Latent Dirichlet allocation, Machine learning, nVidia, nVidia GeForce GTX Titan X, nVidia GeForce GTX Titan XP, Package, Tesla V100

March 17, 2018 by hgpu

CLort: High Throughput and Low Energy Network Intrusion Detection on IoT Devices with Embedded GPUs

SuperNeurons: FFT-based Gradient Sparsification in the Distributed Training of Deep Neural Networks

SWIFOLD: Smith-Waterman implementation on FPGA with OpenCL for long DNA sequences

Spatter: Benchmark for measuring the performance of sparse and irregular memory access

Spatter: A Benchmark Suite for Evaluating Sparse Access Patterns

StePS: Stereographically Projected Cosmological Simulations

StePS: A Multi-GPU Cosmological N-body Code for Compactified Simulations

AMGCL:C++ library for solving large sparse linear systems with algebraic multigrid method

AMGCL: an Efficient, Flexible, and Extensible Algebraic Multigrid Implementation

PAstudy

Double-precision FPUs in High-Performance Computing: an Embarrassment of Riches?

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high performance computing on graphics processing units: hgpu.org

Spatter: A Benchmark Suite for Evaluating Sparse Access Patterns

Workload-aware Automatic Parallelization for Multi-GPU DNN Training

A Hybrid GPU-FPGA-based Computing Platform for Machine Learning

Parallax: Automatic Data-Parallel Training of Deep Neural Networks

Efficient Large-scale Approximate Nearest Neighbor Search on OpenCL FPGA

TBD: Benchmarking and Analyzing Deep Neural Network Training

CuLDA_CGS: Solving Large-scale LDA Problems on GPUs

Recent source codes

schnaps: a finite element solver

GPU-benchmarks

Truchas: 3D Multiphysics Simulation of Metal Casting and Processing

CLort: High Throughput and Low Energy Network Intrusion Detection on IoT Devices with Embedded GPUs

SuperNeurons: FFT-based Gradient Sparsification in the Distributed Training of Deep Neural Networks

SWIFOLD: Smith-Waterman implementation on FPGA with OpenCL for long DNA sequences

Spatter: Benchmark for measuring the performance of sparse and irregular memory access

StePS: Stereographically Projected Cosmological Simulations

AMGCL:C++ library for solving large sparse linear systems with algebraic multigrid method

PAstudy

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

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

International Conference on Frontiers of Artificial Intelligence and Machine Learning (FAIML), 2019

3rd World Conference on Clinical Research and Biomarkers, 2019

3rd International Conference on Vision, Image and Signal Processing (ICVISP), 2019