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Benchmarking TPU, GPU, and CPU Platforms for Deep Learning

Yu (Emma)Wang, Gu-Yeon Wei, David Brooks

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Tags: Benchmarking, Cloud, Computer science, CUDA, Deep learning, Machine learning, Neural networks, nVidia, Performance, Tesla V100, TPU

July 28, 2019 by hgpu

MagmaDNN: Towards High-Performance Data Analytics and Machine Learning for Data-Driven Scientific Computing

Daniel Nichols, Nathalie-Sofia Tomov, Frank Betancourt, Stanimire Tomov, Kwai Wong, Jack Dongarra

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Tags: Algorithms, Computer science, CUDA, Heterogeneous systems, HPC, Linear Algebra, Machine learning, Neural networks, nVidia, nVidia GeForce GTX 1050Ti, Package

July 26, 2019 by hgpu

A Versatile Software Systolic Execution Model for GPU Memory-Bound Kernels

Peng Chen, Mohamed Wahib, Shinichiro Takizawa, Ryousei Takano, Satoshi Matsuoka

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Tags: Computer science, CUDA, nVidia, Package, Stencil computation, Tesla P100, Tesla V100

July 21, 2019 by hgpu

GRN: Gated Relation Network to Enhance Convolutional Neural Network for Named Entity Recognition

Hui Chen, Zijia Lin, Guiguang Ding, Jianguang Lou, Yusen Zhang, Borje Karlsson

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Tags: Computer science, Neural networks, NLP, nVidia, Tesla P100

July 21, 2019 by hgpu

A Highly Efficient Distributed Deep Learning System For Automatic Speech Recognition

Wei Zhang, Xiaodong Cui, Ulrich Finkler, George Saon, Abdullah Kayi, Alper Buyuktosunoglu, Brian Kingsbury, David Kung, Michael Picheny

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Tags: Algorithms, Computer science, CUDA, Deep learning, Distributed computing, GPU cluster, nVidia, OpenMPI, Speech recognition, Tesla V100

July 21, 2019 by hgpu

Block based Singular Value Decomposition approach to matrix factorization for recommender systems

Prasad Bhavana, Vikas Kumar, Vineet Padmanabhan

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Tags: AI, Algorithms, Computer science, CUDA, Factorization, Machine learning, nVidia, Package, Tesla M40

July 21, 2019 by hgpu

On the Portability of CPU-Accelerated Applications via Automated Source-to-Source Translation

Paul Sathre, Mark Gardner, Wu-chun Feng

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Tags: AMD FirePro S9150, ATI, Compilers, Computer science, CUDA, FPGA, nVidia, OpenCL, Package, performance portability, Tesla K80

July 14, 2019 by hgpu

HashGraph – Scalable Hash Tables Using A Sparse Graph Data Structure

Oded Green

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Tags: Algorithms, Computer science, CUDA, Databases, Graph theory, nVidia, nVidia Quadro GV100

July 14, 2019 by hgpu

A Translation Framework from RVC-CAL Dataflow Programs to OpenCL/SYCL based Implementations

Florian Krebs

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Tags: Compilers, Computer science, Heterogeneous systems, nVidia, nVidia GeForce GTX 950 M, OpenCL, SYCL, Thesis

July 14, 2019 by hgpu

Implementation of high speed hash function Keccak on GPU

Thuong Nguyen Dat, Keisuke Iwai, Takashi Matsubara, Takakazu Kurokawa

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Tags: Computer science, CUDA, Hashing, nVidia, nVidia GeForce GTX 1080, Security

July 14, 2019 by hgpu

Profiling based Out-of-core Hybrid Method for Large Neural Networks

Yuki Ito, Haruki Imai, Tung Le Duc, Yasushi Negishi, Kiyokuni Kawachiya, Ryo Matsumiya, Toshio Endo

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Tags: Computer science, CUDA, Deep learning, Machine learning, Neural networks, nVidia, Performance, Tesla V100

July 14, 2019 by hgpu

GPU-based Parallel Computation Support for Stan

Rok Cesnovar, Steve Bronder, Davor Sluga, Jure Demsar, Tadej Ciglaric, Sean Talts, Erik Strumbelj

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Tags: AMD Radeon R9 Fury, ATI, Computer science, Heterogeneous systems, nVidia, nVidia GeForce GTX Titan XP, OpenCL, Package, Tesla V100

July 10, 2019 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

MagmaDNN: Towards High-Performance Data Analytics and Machine Learning for Data-Driven Scientific Computing

A Versatile Software Systolic Execution Model for GPU Memory-Bound Kernels

GRN: Gated Relation Network to Enhance Convolutional Neural Network for Named Entity Recognition

A Highly Efficient Distributed Deep Learning System For Automatic Speech Recognition

Block based Singular Value Decomposition approach to matrix factorization for recommender systems

On the Portability of CPU-Accelerated Applications via Automated Source-to-Source Translation

HashGraph – Scalable Hash Tables Using A Sparse Graph Data Structure

A Translation Framework from RVC-CAL Dataflow Programs to OpenCL/SYCL based Implementations

Implementation of high speed hash function Keccak on GPU

Profiling based Out-of-core Hybrid Method for Large Neural Networks

GPU-based Parallel Computation Support for Stan

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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