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fairseq: A Fast, Extensible Toolkit for Sequence Modeling

Myle Ott, Sergey Edunov, Alexei Baevski, Angela Fan, Sam Gross, Nathan Ng, David Grangier, Michael Auli

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Tags: Computer science, CUDA, NLP, nVidia, Package, PyTorch, Video

April 7, 2019 by hgpu

Methods for Accelerating Machine Learning in High Performance Computing

Robert Lim

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Tags: Algorithms, Computer science, CUDA, Heterogeneous systems, Machine learning, nVidia, Tesla P100, Thesis

March 31, 2019 by hgpu

Machine Learning and Deep Learning frameworks and libraries for large-scale data mining: a survey

Giang Nguyen, Stefan Dlugolinsky, Martin Bobak, Viet Tran, Alvaro Lopez Garcia, Ignacio Heredia, Peter Malik, Ladislav Hluchy

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Tags: Algorithms, Artificial intelligence, Computer science, CUDA, Data mining, Deep learning, Machine learning, nVidia, OpenCL

March 31, 2019 by hgpu

Hybrid CPU-GPU execution support in the skeleton programming framework SkePU

Tomas Oehberg, August Ernstsson, Christoph Kessler

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Tags: Computer science, CUDA, Heterogeneous systems, Hybrid computing, MapReduce, nVidia, OpenCL, Package, Tesla K20

March 31, 2019 by hgpu

HeteroMap: A Runtime Performance Predictor for Efficient Processing of Graph Analytics on Heterogeneous Multi-Accelerators

Masab Ahmad, Halit Dogan, Christopher J. Michael, Omer Khan

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Tags: Algorithms, Computer science, Heterogeneous systems, Intel Xeon Phi, nVidia, nVidia GeForce GTX 750 Ti, nVidia GeForce GTX 970, OpenCL, Package, Performance

March 31, 2019 by hgpu

swCaffe: a Parallel Framework for Accelerating Deep Learning Applications on Sunway TaihuLight

Jiarui Fang, Liandeng Li, Haohuan Fu, Jinlei Jiang, Wenlai Zhao, Conghui He, Xin You, Guangwen Yang

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Tags: Computer science, CUDA, Deep learning, Heterogeneous systems, Neural networks, nVidia, OpenMPI, Package, Tesla K40

March 24, 2019 by hgpu

Dissecting the NVidia Turing T4 GPU via Microbenchmarking

Zhe Jia, Marco Maggioni, Jeffrey Smith, Daniele Paolo Scarpazza

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Tags: Benchmarking, Computer science, CUDA, Deep learning, nVidia, Performance, PTX, Tesla P100, Tesla V100

March 24, 2019 by hgpu

Novel Data-Partitioning Algorithms for Performance and Energy Optimization of Data-Parallel Applications on Modern Heterogeneous HPC Platforms

Hamidreza Khaleghzadeh

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Tags: Computer science, CUDA, FPGA, Heterogeneous systems, Matrix multiplication, nVidia, OpenCL, Package, Thesis

March 17, 2019 by hgpu

Performance Optimization of Memory Intensive Applications on FPGA Accelerator

Arslan Arif

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Tags: Artificial intelligence, Computer science, Electronic design automation, FPGA, nVidia, nVidia GeForce GTX 960, OpenCL, Thesis

March 17, 2019 by hgpu

Analyzing GPU Tensor Core Potential for Fast Reductions

Roberto Carrasco, Raimundo Vega, Cristóbal A. Navarro

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

March 17, 2019 by hgpu

TensorFlow Doing HPC

Steven W. D. Chien, Stefano Markidis, Vyacheslav Olshevsky, Yaroslav Bulatov, Erwin Laure, Jeffrey S. Vetter

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Tags: Benchmarking, Computer science, CUDA, Deep learning, FFT, Heterogeneous systems, HPC, Machine learning, nVidia, nVidia Quadro K420, OpenMPI, Package, Performance, Python, TensorFlow, Tesla K80, Tesla V100

March 17, 2019 by hgpu

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

Paul Sathre, Mark Gardner, Wu-chun Feng

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

March 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

fairseq: A Fast, Extensible Toolkit for Sequence Modeling

Methods for Accelerating Machine Learning in High Performance Computing

Machine Learning and Deep Learning frameworks and libraries for large-scale data mining: a survey

Hybrid CPU-GPU execution support in the skeleton programming framework SkePU

HeteroMap: A Runtime Performance Predictor for Efficient Processing of Graph Analytics on Heterogeneous Multi-Accelerators

swCaffe: a Parallel Framework for Accelerating Deep Learning Applications on Sunway TaihuLight

Dissecting the NVidia Turing T4 GPU via Microbenchmarking

Novel Data-Partitioning Algorithms for Performance and Energy Optimization of Data-Parallel Applications on Modern Heterogeneous HPC Platforms

Performance Optimization of Memory Intensive Applications on FPGA Accelerator

Analyzing GPU Tensor Core Potential for Fast Reductions

TensorFlow Doing HPC

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

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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