high performance computing on graphics processing units: hgpu.org

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Neural Architecture Search without Training

Joseph Mellor, Jack Turner, Amos Storkey, Elliot J. Crowley

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Tags: Computer science, Deep learning, Machine learning, Neural networks, nVidia, nVidia GeForce GTX 1080 Ti, Package

June 14, 2020 by hgpu

OpenABLext: An automatic code generation framework for agent-based simulations on CPU-GPU-FPGA heterogeneous platforms

Jiajian Xiao, Philipp Andelfinger, Wentong Cai, Paul Richmond, Alois Knoll, David Eckhoff

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Tags: Code generation, Computer science, FPGA, Heterogeneous systems, nVidia, nVidia GeForce GTX 1060, nVidia GeForce GTX 2080 Ti, OpenCL, Package

June 7, 2020 by hgpu

SOFF: An OpenCL High-Level Synthesis Framework for FPGAs

Gangwon Jo, Heehoon Kim, Jeesoo Lee, Jaejin Lee

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Tags: Compilers, Computer science, FPGA, OpenCL, Package, Performance

June 7, 2020 by hgpu

Investigating Single Precision Floating General Matrix Multiply in Heterogeneous

Steven Harris

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Tags: ASIC, Computer science, FPGA, Heterogeneous systems, Matrix multiplication, OpenCL, Performance, performance portability, Thesis

June 7, 2020 by hgpu

Efficient Sparse-Dense Matrix-Matrix Multiplication on GPUs Using the Customized Sparse Storage Format

Shaohuai Shi, Qiang Wang, Xiaowen Chu

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Tags: Algorithms, Computer science, CUBLAS, CUDA, Matrix multiplication, nVidia, nVidia GeForce GTX 980, nVidia GeForce GTX Titan X, Package, Sparse matrix, Tesla P100

June 7, 2020 by hgpu

Multi-GPU Performance Optimization of a CFD Code using OpenACC on Different Platforms

Weicheng Xue, Christoper J. Roy

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Tags: cfd, Fluid dynamics, MPI, nVidia, OpenACC, Performance, Tesla C2050, Tesla M2015, Tesla P100, Tesla V100

June 7, 2020 by hgpu

Evaluating the performance of HPC-style SYCL applications

Tom Deakin, Simon McIntosh-Smith

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Tags: Benchmarking, Computer science, Heterogeneous systems, OpenCL, OpenMP, Package, Performance, SYCL

May 31, 2020 by hgpu

Lessons learned in a decade of research software engineering GPU applications

Ben van Werkhoven, Willem Jan Palenstijn, Alessio Sclocco

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Tags: Computer science, CUDA, nVidia, OpenCL, Software Engineering

May 31, 2020 by hgpu

Character-level Transformer-based Neural Machine Translation

Nikolay Banar, Walter Daelemans, Mike Kestemont

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Tags: Computer science, CUDA, Deep learning, NLP, nVidia, nVidia GeForce GTX 1080 Ti

May 31, 2020 by hgpu

Brief Announcement: On the Limits of Parallelizing Convolutional Neural Networks on GPUs

Behnam Pourghassemi, Chenghao Zhang, Joo Hwan Lee, Aparna Chandramowlishwaran

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Tags: Computer science, CUDA, Deep learning, Neural networks, nVidia, TensorFlow, Tesla K40

May 31, 2020 by hgpu

HetPipe: Enabling Large DNN Training on (Whimpy) Heterogeneous GPU Clusters through Integration of Pipelined Model Parallelism and Data Parallelism

Jay H. Park, Gyeongchan Yun, Chang M. Yi, Nguyen T. Nguyen, Seungmin Lee, Jaesik Choi, Sam H. Noh, Young-ri Choi

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Tags: Computer science, CUDA, Data parallelism, Deep learning, GPU cluster, Heterogeneous systems, Neural networks, nVidia, nVidia GeForce GTX Titan V, nVidia GeForce RTX 2060, nVidia Quadro P 4000, nVidia Titan RTX

May 31, 2020 by hgpu

HaoCL: Harnessing Large-scale Heterogeneous Processors Made Easy

Yao Chen, Xin Long, Jiong He, Yuhang Chen, Hongshi Tan, Zhenxiang Zhang, Marianne Winslett, Deming Chen

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Tags: Computer science, Deep learning, FPGA, Heterogeneous systems, Machine learning, nVidia, OpenCL, Tesla P4

May 24, 2020 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

Applications

Neural Architecture Search without Training

OpenABLext: An automatic code generation framework for agent-based simulations on CPU-GPU-FPGA heterogeneous platforms

SOFF: An OpenCL High-Level Synthesis Framework for FPGAs

Investigating Single Precision Floating General Matrix Multiply in Heterogeneous

Efficient Sparse-Dense Matrix-Matrix Multiplication on GPUs Using the Customized Sparse Storage Format

Multi-GPU Performance Optimization of a CFD Code using OpenACC on Different Platforms

Evaluating the performance of HPC-style SYCL applications

Lessons learned in a decade of research software engineering GPU applications

Character-level Transformer-based Neural Machine Translation

Brief Announcement: On the Limits of Parallelizing Convolutional Neural Networks on GPUs

HetPipe: Enabling Large DNN Training on (Whimpy) Heterogeneous GPU Clusters through Integration of Pipelined Model Parallelism and Data Parallelism

HaoCL: Harnessing Large-scale Heterogeneous Processors Made Easy

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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