high performance computing on graphics processing units: hgpu.org

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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

PDAWL: Profile-based Iterative Dynamic Adaptive WorkLoad Balance on Heterogeneous Architectures

Tongsheng Geng, Marcos Amaris, Stephane Zuckerman, Alfredo Goldman, Guang R. Gao, Jean-Luc Gaudiot

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Tags: Computer science, CUDA, Heterogeneous systems, Machine learning, nVidia, nVidia GeForce GTX Titan, Performance, Sparse matrix, Task scheduling, Tesla K20, Tesla K40

May 24, 2020 by hgpu

Literature Review and Implementation Overview: High Performance Computing with Graphics Processing Units for Classroom and Research Use

Nathan George

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Tags: Computer science, CUDA, Deep learning, Education, GPU cluster, HPC, nVidia, nVidia GeForce GTX 1080 Ti, nVidia Quadro RTX 5000, Overview

May 24, 2020 by hgpu

Deep Learning with GO

Derek L. Stinson

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Tags: Computer science, CUDA, Deep learning, Go, nVidia, nVidia GeForce GTX 1080 Ti, Package, Thesis

May 17, 2020 by hgpu

Task-Based Parallel Strategies for CFD Application in Heterogeneous CPU/GPU Resources

Lucas Leandro Nesi, Matheus S. Serpa, Lucas Mello Schnorr, Philippe Olivier Alexandre Navaux

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Tags: cfd, CUDA, Fluid dynamics, Heterogeneous systems, nVidia, nVidia GeForce GXT 1080 Ti, Package

May 17, 2020 by hgpu

Heterogeneous CPU/GPU co-execution of CFD simulations on the POWER9 architecture: Application to airplane aerodynamics

R. Borrell, D. Dosimont, M. Garcia-Gasulla, G. Houzeaux, O. Lehmkuhl, V. Mehta, H. Owen, M. Vazquez, G. Oyarzun

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Tags: cfd, CUDA, Fluid dynamics, Heterogeneous systems, MPI, nVidia, OpenMP, Tesla V100

May 17, 2020 by hgpu

Parallel Programming Models for Heterogeneous Many-Cores: A Survey

Jianbin Fang, Chun Huang, Tao Tang, Zheng Wang

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, OpenCL, Optimization, Programming Languages, Review

May 17, 2020 by hgpu

From Constraint Programming to Heterogeneous Parallelism

Philip Ginsbach

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Tags: AMD Radeon R7, ATI, Code generation, Computer science, DSL, Heterogeneous systems, nVidia, nVidia GeForce GTX Titan X, OpenCL, OpenMP, Thesis

May 10, 2020 by hgpu

Importance of Data Loading Pipeline in Training Deep Neural Networks

Mahdi Zolnouri, Xinlin Li, Vahid Partovi Nia

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Tags: Computer science, CUDA, Deep learning, Neural networks, nVidia, nVidia GeForce GTX Titan, Package, Tesla V100

May 10, 2020 by hgpu

Accurate Energy and Performance Prediction for Frequency-Scaled GPU Kernels

Kaijie Fan, Biagio Cosenza, Ben Juurlink

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Tags: Computer science, Energy-efficient computing, Machine learning, nVidia, nVidia GeForce GTX Titan X, OpenCL, Performance, Tesla P100

May 10, 2020 by hgpu

Pushing the limit of molecular dynamics with ab initio accuracy to 100 million atoms with machine learning

Weile Jia, Han Wang, Mohan Chen, Denghui Lu, Jiduan Liu, Lin Lin, Roberto Car, Weinan E, Linfeng Zhang

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Tags: Computational Physics, CUDA, HPC, Machine learning, Mixed precision, Molecular dynamics, Molecular simulation, MPI, nVidia, OpenMP, Physics, Tesla V100

May 10, 2020 by hgpu

Tools for GPU Computing – Debugging and Performance Analysis of Heterogenous HPC Applications

Michael Knobloch, Bernd Mohr

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Tags: Computer science, CUDA, HPC, nVidia, OpenACC, OpenCL, Performance

May 3, 2020 by hgpu

Efficient Graph Embedding at Scale: Optimizing CPU-GPU-SSD Integration

Can Large Language Models Predict Parallel Code Performance?

PELSI: Power-Efficient Layer-Switched Inference

Efficient deep learning inference on end devices

Ouroboros: Virtualized Queues for dynamic memory management

Dynamic Memory Management on GPUs with SYCL

MSCCL++: A GPU-driven communication stack for scalable AI applications

MSCCL++: Rethinking GPU Communication Abstractions for Cutting-edge AI Applications

Benchmark compute shader of Unity against InteropUnityCUDA

InteropUnityCUDA: A Tool for Interoperability Between Unity and CUDA

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high performance computing on graphics processing units: hgpu.org

HaoCL: Harnessing Large-scale Heterogeneous Processors Made Easy

PDAWL: Profile-based Iterative Dynamic Adaptive WorkLoad Balance on Heterogeneous Architectures

Literature Review and Implementation Overview: High Performance Computing with Graphics Processing Units for Classroom and Research Use

Deep Learning with GO

Task-Based Parallel Strategies for CFD Application in Heterogeneous CPU/GPU Resources

Heterogeneous CPU/GPU co-execution of CFD simulations on the POWER9 architecture: Application to airplane aerodynamics

Parallel Programming Models for Heterogeneous Many-Cores: A Survey

From Constraint Programming to Heterogeneous Parallelism

Importance of Data Loading Pipeline in Training Deep Neural Networks

Accurate Energy and Performance Prediction for Frequency-Scaled GPU Kernels

Pushing the limit of molecular dynamics with ab initio accuracy to 100 million atoms with machine learning

Tools for GPU Computing – Debugging and Performance Analysis of Heterogenous HPC Applications

Recent source codes

SYCL Container

CASS: Cuda-Amd aSSembly

Cluser of smartphones for edge computing application using TensorFlow

CFAL-bench

Efficient Graph Embedding at Scale: Optimizing CPU-GPU-SSD Integration

Can Large Language Models Predict Parallel Code Performance?

PELSI: Power-Efficient Layer-Switched Inference

Ouroboros: Virtualized Queues for dynamic memory management

MSCCL++: A GPU-driven communication stack for scalable AI applications

Benchmark compute shader of Unity against InteropUnityCUDA

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