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Productive Performance Engineering for Weather and Climate Modeling with Python

Tal Ben-Nun, Linus Groner, Florian Deconinck, Tobias Wicky, Eddie Davis, Johann Dahm, Oliver Elbert, Rhea George, Jeremy McGibbon, Lukas Trümper, Elynn Wu, Oliver Fuhrer, Thomas Schulthess, Torsten Hoefler

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Tags: CUDA, Earth and Space Sciences, nVidia, Python, Tesla A100, Tesla P100, Weather Prediction Model

May 15, 2022 by hgpu

Experience of Migrating a Parallel Graph Coloring Program from CUDA to SYCL

Zheming Jin

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Tags: Benchmarking, Computer science, CUDA, Graph theory, nVidia, OpenCL, Package, Performance, SYCL

May 8, 2022 by hgpu

cuPSO: GPU Parallelization for Particle Swarm Optimization Algorithms

Chuan-Chi Wang, Chun-Yen Ho, Chia-Heng Tu, Shih-Hao Hung

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 1080 Ti, Package, Particle swarm optimization

May 8, 2022 by hgpu

GPUNet: Searching the Deployable Convolution Neural Networks for GPUs

Linnan Wang, Chenhan Yu, Satish Salian, Slawomir Kierat, Szymon Migacz, Alex Fit Florea

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Tags: Computer science, CUDA, Deep learning, Neural networks, nVidia, nVidia Quadro GV100, Performance, Tesla A100

May 8, 2022 by hgpu

Analytical Performance Estimation during Code Generation on Modern GPUs

Dominik Ernst, Markus Holzer, Georg Hager, Matthias Knorr, Gerhard Wellein

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Tags: Code generation, Computer science, CUDA, Lattice Boltzmann model, Machine learning, nVidia, OpenCL, Package, Tesla A100, Tesla V100

May 8, 2022 by hgpu

CASE: A Compiler-Assisted SchEduling Framework for Multi-GPU Systems

Chao Chen, Chris Porter, Santosh Pande

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Tags: Compilers, Computer science, CUDA, nVidia, Package, Task scheduling, Tesla P100, Tesla V100

May 1, 2022 by hgpu

Efficient Execution of OpenMP on GPUs

Joseph Huber, Melanie Cornelius, Giorgis Georgakoudis, Shilei Tian, Jose M Monsalve Diaz, Kuter Dinel, Barbara Chapman, Johannes Doerfert

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Tags: Code generation, Computer science, CUDA, Heterogeneous systems, HPC, LLVM, nVidia, OpenMP, Package, Tesla V100

May 1, 2022 by hgpu

Improving performance of SYCL applications on CPU architectures using LLVM-directed compilation flow

Pietro Ghiglio, Uwe Dolinsky, Mehdi Goli, Kumudha Narasimhan

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Tags: Computer science, CUDA, nVidia, OpenCL, Performance, performance portability, SYCL

May 1, 2022 by hgpu

Explicit caching HYB: a new high-performance SpMV framework on GPGPU

Chong Chen

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Tags: Computer science, CUDA, FEM, Finite element method, nVidia, Package, Sparse matrix, Tesla V100

April 17, 2022 by hgpu

Performance study on GPU offloading techniques using the Gauss matrix inverse algorithm

Yannik Könneker

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Tags: Computer science, CUBLAS, CUDA, Linear Algebra, nVidia, nVidia GeForce GTX 1080, OpenACC, OpenCL, Package, Performance, Tesla V100, Thesis

April 17, 2022 by hgpu

Performance Models for Heterogeneous Iterative Programs

Aparna Sasidharan

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, Performance, Tesla V100

April 10, 2022 by hgpu

Persistent Kernels for Iterative Memory-bound GPU Applications

Lingqi Zhang, Mohamed Wahib, Peng Chen, Jintao Meng, Xiao Wang, Satoshi Matsuoka

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Tags: Computer science, CUDA, nVidia, Package, Stencil computation, Tesla A100, Tesla V100

April 10, 2022 by hgpu

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A Container-Based Workflow for Distributed Training of Deep Learning Algorithms in HPC Clusters

ARMCL-PipeALL: an integrated high-throughput CPU-GPU CNN inference pipeline design for ARM-based Heterogeneous MPSoCs

CPU-GPU Layer-Switched Low Latency CNN Inference

EngineCL for Stratix10 MX

FPGA Accelerators on Heterogeneous Systems: An Approach Using High Level Synthesis

Theseus: A Library for Differentiable Nonlinear Optimization

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high performance computing on graphics processing units: hgpu.org

Productive Performance Engineering for Weather and Climate Modeling with Python

Experience of Migrating a Parallel Graph Coloring Program from CUDA to SYCL

cuPSO: GPU Parallelization for Particle Swarm Optimization Algorithms

GPUNet: Searching the Deployable Convolution Neural Networks for GPUs

Analytical Performance Estimation during Code Generation on Modern GPUs

CASE: A Compiler-Assisted SchEduling Framework for Multi-GPU Systems

Efficient Execution of OpenMP on GPUs

Improving performance of SYCL applications on CPU architectures using LLVM-directed compilation flow

Explicit caching HYB: a new high-performance SpMV framework on GPGPU

Performance study on GPU offloading techniques using the Gauss matrix inverse algorithm

Performance Models for Heterogeneous Iterative Programs

Persistent Kernels for Iterative Memory-bound GPU Applications

Recent source codes

A Container-Based Workflow for Distributed Training of Deep Learning Algorithms in HPC Clusters

ARMCL-PipeALL: an integrated high-throughput CPU-GPU CNN inference pipeline design for ARM-based Heterogeneous MPSoCs

EngineCL for Stratix10 MX

Theseus: A Library for Differentiable Nonlinear Optimization

OmpCluster

PipeCNN: An OpenCL-based FPGA Accelerator for Convolutional Neural Networks

hls4ml: Machine learning on FPGAs using HLS

MocCUDA: Prototype to run Pytorch/CUDA on Fugaku

Rodinia Benchmarks for DPC++

DCA++: a state of the art implementation of the dynamical cluster approximation

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