high performance computing on graphics processing units: hgpu.org

Oliver Hennigh, Susheela Narasimhan, Mohammad Amin Nabian, Akshay Subramaniam, Kaustubh Tangsali, Max Rietmann, Jose del Aguila Ferrandis, Wonmin Byeon, Zhiwei Fang, Sanjay Choudhry

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Tags: cfd, CUDA, Fluid dynamics, Linear Algebra, Machine learning, Neural networks, nVidia, nVidia A100, Partial differential equations, PDEs, Physics, Tesla V100, Video

December 20, 2020 by hgpu

ANGHABENCH: a Suite with One Million Compilable C Benchmarks for Code-Size Reduction

waLBerla: (widely applicable Lattice Boltzmann from Erlangen), massively parallel framework for multi physics applications

Highly Efficient Lattice-Boltzmann Multiphase Simulations of Immiscible Fluids at High-Density Ratios on CPUs and GPUs through Code Generation

NaturalCC: A Toolkit to Naturalize the Source Code Corpus

Systolic CNN

Systolic-CNN: An OpenCL-defined Scalable Run-time-flexible FPGA Accelerator Architecture for Accelerating Convolutional Neural Network Inference in Cloud/Edge Computing

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2021

April

27-29

Munich, Germany

9th International Workshop on OpenCL and SYCL, 2021

2020

December

10-14

Virtually in Barcelona, Spain

The 18th International Conference on High Performance Computing & Simulation (HPCS), 2020

2020

November

24-27

Naha, Okinawa, Japan

The Fifth International Workshop on GPU Computing and AI (GCA), 2020

2021

January

13-15

Shenzhen, China

2nd International Conference on Frontiers of Intelligent Manufacturing and Automation (CFIMA’21), 2021

2020

December

14-16

Bangkok, Thailand

International Conference on Cyber Physical Systems and IoT (CPSIOT’20), 2020

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high performance computing on graphics processing units: hgpu.org

Efficient Nearest-Neighbor Data Sharing in GPUs

Hardware Acceleration of HPC Computational Flow Dynamics using HBM-enabled FPGAs

Interactive Parallelization of C Programs in SAPFOR

I/O Lower Bounds for Auto-tuning of Convolutions in CNNs

Fast CUDA-Aware MPI Datatypes without Platform Support

When Machine Learning Meets Quantum Computers: A Case Study

Hardware and Software Optimizations for Accelerating Deep Neural Networks: Survey of Current Trends, Challenges, and the Road Ahead

Exploiting BSP Abstractions for Compiler Based Optimizations of GPU Applications on multi-GPU Systems

Directive-Based Data Partitioning and Pipelining and Auto-Tuning for High-Performance GPU Computing

Highly Efficient Lattice-Boltzmann Multiphase Simulations of Immiscible Fluids at High-Density Ratios on CPUs and GPUs through Code Generation

Solving large permutation flow-shop scheduling problems on GPU-accelerated supercomputers

NVIDIA SimNet: an AI-accelerated multi-physics simulation framework

Recent source codes

hls4ml: A package for machine learning inference in FPGAs

Adaptive Partial STEM

Linus: easy-to-share, interactive 3D figures

Compound Word Transformer

SAPFOR

CUDA Memtrace

ANGHABENCH: a Suite with One Million Compilable C Benchmarks for Code-Size Reduction

waLBerla: (widely applicable Lattice Boltzmann from Erlangen), massively parallel framework for multi physics applications

NaturalCC: A Toolkit to Naturalize the Source Code Corpus

Systolic CNN

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Featured events

9th International Workshop on OpenCL and SYCL, 2021

The 18th International Conference on High Performance Computing & Simulation (HPCS), 2020

The Fifth International Workshop on GPU Computing and AI (GCA), 2020

2nd International Conference on Frontiers of Intelligent Manufacturing and Automation (CFIMA’21), 2021

International Conference on Cyber Physical Systems and IoT (CPSIOT’20), 2020