high performance computing on graphics processing units: hgpu.org

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Scalable communication for high-order stencil computations using CUDA-aware MPI

Johannes Pekkilä, Miikka S. Väisälä, Maarit J. Käpylä, Matthias Rheinhardt, Oskar Lappi

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Tags: Computer science, CUDA, Finite difference, Magnetohydrodynamics, MPI, nVidia, Tesla V100

March 7, 2021 by hgpu

Neural Network Libraries: A Deep Learning Framework Designed from Engineers’ Perspectives

Akio Hayakawa, Masato Ishii, Yoshiyuki Kobayashi, Akira Nakamura, Takuya Narihira, Yukio Obuchi, Andrew Shin, Takuya Yashima, Kazuki Yoshiyama

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Tags: Computer science, Deep learning, Neural networks, nVidia, nVidia DGX-1, Package, Tesla V100

March 7, 2021 by hgpu

GPU-aware Communication with UCX in Parallel Programming Models: Charm++, MPI, and Python

Jaemin Choi, Zane Fink, Sam White, Nitin Bhat, David F. Richards, Laxmikant V. Kale

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Tags: Benchmarking, Computer science, CUDA, Distributed computing, MPI, nVidia, Package, Python, Tesla V100

February 28, 2021 by hgpu

cuFINUFFT: a load-balanced GPU library for general-purpose nonuniform FFTs

Yu-hsuan Shih, Garrett Wright, Joakim Andén, Johannes Blaschke, Alex H. Barnett

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Tags: CUDA, FFT, Image reconstruction, Mathematical Software, Numerical Analysis, nVidia, Package, Signal processing, Tesla V100

February 21, 2021 by hgpu

Serverless Computing Strategies on Cloud Platforms

Diana María Naranjo Delgado

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Tags: Cloud, Computer science, CUDA, Machine learning, nVidia, Tesla V100, Thesis, Virtualization

February 14, 2021 by hgpu

Sub-seasonal forecasting with a large ensemble of deep-learning weather prediction models

Jonathan A. Weyn, Dale R. Durran, Rich Caruana, Nathaniel Cresswell-Clay

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Tags: Deep learning, Earth and Space Sciences, Neural networks, nVidia, Tesla V100, Weather prediction, Weather Prediction Model

February 14, 2021 by hgpu

Computational Performance Predictions for Deep Neural Network Training: A Runtime-Based Approach

Geoffrey X. Yu, Yubo Gao, Pavel Golikov, Gennady Pekhimenko

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Tags: Computer science, CUDA, Deep learning, Machine learning, Neural networks, nVidia, nVidia GeForce RTX 2070, Performance, Python, Tesla P100, Tesla V100

February 7, 2021 by hgpu

Performance of CPU and GPU HPC Architectures for off-design aircraft simulation

Mike Turner, Jamil Appa, Neil Ashton

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Tags: cfd, CUDA, Fluid dynamics, MPI, nVidia, OpenMP, SYCL, Tesla A100, Tesla T4, Tesla V100

January 31, 2021 by hgpu

Efficient MPI-based Communication for GPU-Accelerated Dask Applications

Aamir Shafi, Jahanzeb Maqbool Hashmi, Hari Subramoni, Dhabaleswar K. (DK) Panda

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Tags: Computer science, CUDA, Distributed computing, MPI, nVidia, Package, Performance, Python, Tesla V100

January 31, 2021 by hgpu

CPU/GPU Code Acceleration on Heterogeneous Systems and Code Verification for CFD Applications

Weicheng Xue

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Tags: cfd, CUDA, Finite difference, Fluid dynamics, Heterogeneous systems, MPI, nVidia, OpenACC, Tesla C2075, Tesla P100, Tesla V100, Thesis

January 31, 2021 by hgpu

Non-Parametric Adaptive Network Pruning

Lin Mingbao, Ji Rongrong, Li Shaojie, Wang Yan, Wu Yongjian, Huang Feiyue, Ye Qixiang

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Tags: Computer science, CUDA, Deep learning, nVidia, Package, PyTorch, Tesla V100

January 24, 2021 by hgpu

Learning Massive Graph Embeddings on a Single Machine

Jason Mohoney, Roger Waleffe, Yiheng Xu, Theodoros Rekatsinas, Shivaram Venkataraman

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Tags: Computer science, CUDA, Databases, Graph theory, Machine learning, nVidia, Tesla V100

January 24, 2021 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

See all packages

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high performance computing on graphics processing units: hgpu.org

Scalable communication for high-order stencil computations using CUDA-aware MPI

Neural Network Libraries: A Deep Learning Framework Designed from Engineers’ Perspectives

GPU-aware Communication with UCX in Parallel Programming Models: Charm++, MPI, and Python

cuFINUFFT: a load-balanced GPU library for general-purpose nonuniform FFTs

Sub-seasonal forecasting with a large ensemble of deep-learning weather prediction models

Computational Performance Predictions for Deep Neural Network Training: A Runtime-Based Approach

Performance of CPU and GPU HPC Architectures for off-design aircraft simulation

Efficient MPI-based Communication for GPU-Accelerated Dask Applications

CPU/GPU Code Acceleration on Heterogeneous Systems and Code Verification for CFD Applications

Non-Parametric Adaptive Network Pruning

Learning Massive Graph Embeddings on a Single Machine

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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