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Scalability Study of Deep Learning Algorithms in High Performance Computer Infrastructures

Francesc Sastre Cabot

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Tags: Algorithms, Computer science, Computer vision, CUDA, Deep learning, GPU cluster, Machine learning, Neural networks, nVidia, Tesla K80, Tesla M2090, Thesis

July 22, 2017 by hgpu

A Comparison of Support Vector Machines Training GPU-Accelerated Open Source Implementations

Jan Vanek, Josef Michalek, Josef Psutka

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Tags: Benchmarking, Computer science, CUDA, Machine learning, nVidia, nVidia GeForce GTX 1080, OpenCL, Package

July 22, 2017 by hgpu

Automatically Selecting Profitable Thread Block Sizes Using Machine Learning

Tiffany A. Connors

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Tags: Computer science, CUDA, Machine learning, nVidia, Performance, Tesla K40, Thesis

July 22, 2017 by hgpu

Termination Analysis for GPU Kernels

Jeroen Ketema, Alastair F. Donaldson

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Tags: Computer science, CUDA, LLVM, OpenCL, Package

July 19, 2017 by hgpu

GPU LSM: A Dynamic Dictionary Data Structure for the GPU

Saman Ashkiani, Shengren Li, Martin Farach-Colton, Nina Amenta, John D. Owens

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Tags: Algorithms, Computer science, CUDA, Databases, nVidia, Sorting, Tesla K40

July 19, 2017 by hgpu

Batched QR and SVD Algorithms on GPUs with Applications in Hierarchical Matrix Compression

Wajih Halim Boukaram, George Turkiyyah, Hatem Ltaief, David E. Keyes

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Tags: Computer science, CUDA, Machine learning, Mathematical Software, Numerical Analysis, nVidia, Tesla P100

July 19, 2017 by hgpu

DeepProf: Performance Analysis for Deep Learning Applications via Mining GPU Execution Patterns

Jiazhen Gu, Huan Liu, Yangfan Zhou, Xin Wang

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Tags: Computer science, CUDA, Deep learning, nVidia, nVidia GeForce GTX 1080 Ti, Performance, Suffix trees

July 14, 2017 by hgpu

A Similarity Measure for GPU Kernel Subgraph Matching

Robert V. Lim, Boyana Norris, Allen D. Malony

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Tags: Computer science, CUDA, nVidia, Performance, Tesla K80, Tesla M40, Tesla P100

July 14, 2017 by hgpu

A Fast Method For Computing Principal Curvatures From Range Images

Andrew Spek, Wai Ho Li, Tom Drummond

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Tags: Computer science, Computer vision, CUDA, nVidia, Package, Tesla K40

July 5, 2017 by hgpu

A Linear Algebra Approach to Fast DNA Mixture Analysis Using GPUs

Siddharth Samsi, Brian Helfer, Jeremy Kepner, Albert Reuther, Darrell O. Ricke

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Tags: Algorithms, Computer science, CUDA, Genomics, Linear Algebra, Matrix multiplication, nVidia, Performance, Tesla K80

July 5, 2017 by hgpu

Parle: parallelizing stochastic gradient descent

Pratik Chaudhari, Carlo Baldassi, Riccardo Zecchina, Stefano Soatto, Ameet Talwalkar

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Tags: Algorithms, Computer science, CUDA, Deep learning, Lua, Machine learning, nVidia, Package, Performance, Torch

July 5, 2017 by hgpu

Deep neural networks for direct, featureless learning through observation: the case of 2d spin models

K. Mills, I. Tamblyn

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Tags: Condensed matter, CUDA, Deep learning, Ising model, Materials Science, Neural networks, nVidia, Phase transition, Physics, Tesla K40

July 2, 2017 by hgpu

Over-synchronization in GPU Programs

profile_util: simple C++ library to report memory, timing, thread affinity

Profile Util library: A quick and easy way to get MPI, OpenMP and GPU runtime information

MaC: Mamba CPU performance predictor

MambaCPU: Enhanced Correlation Mining with State Space Models for CPU Performance Prediction

Mixed-precision finite element kernels

Mixed-precision finite element kernels and assembly: Rounding error analysis and hardware acceleration

SHMT: The Simultaneous and Heterogenous Multithreading project

General-Purpose Computing on Tensor Processors

Superpipeline: A Universal Approach for Reducing GPU Memory Usage in Large Models

EnergyUCB-Bandit

Online Energy Optimization in GPUs: A Multi-Armed Bandit Approach

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high performance computing on graphics processing units: hgpu.org

Scalability Study of Deep Learning Algorithms in High Performance Computer Infrastructures

A Comparison of Support Vector Machines Training GPU-Accelerated Open Source Implementations

Automatically Selecting Profitable Thread Block Sizes Using Machine Learning

Termination Analysis for GPU Kernels

GPU LSM: A Dynamic Dictionary Data Structure for the GPU

Batched QR and SVD Algorithms on GPUs with Applications in Hierarchical Matrix Compression

DeepProf: Performance Analysis for Deep Learning Applications via Mining GPU Execution Patterns

A Similarity Measure for GPU Kernel Subgraph Matching

A Fast Method For Computing Principal Curvatures From Range Images

A Linear Algebra Approach to Fast DNA Mixture Analysis Using GPUs

Parle: parallelizing stochastic gradient descent

Deep neural networks for direct, featureless learning through observation: the case of 2d spin models

Recent source codes

Kokkidio: Kokkos+Eigen - write fast, readable multi-platform code

LLaMA-Inference-Bench

MATAR

Over-synchronization in GPU Programs

profile_util: simple C++ library to report memory, timing, thread affinity

MaC: Mamba CPU performance predictor

Mixed-precision finite element kernels

SHMT: The Simultaneous and Heterogenous Multithreading project

Superpipeline: A Universal Approach for Reducing GPU Memory Usage in Large Models

EnergyUCB-Bandit

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