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ARK: GPU-driven Code Execution for Distributed Deep Learning

Changho Hwang, KyoungSoo Park, Ran Shu, Xinyuan Qu, Peng Cheng, Yongqiang Xiong

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Tags: Computer science, Deep learning, Distributed computing, nVidia, nVidia V100

March 12, 2023 by hgpu

AutoDDL: Automatic Distributed Deep Learning with Asymptotically Optimal Communication

Jinfan Chen, Shigang Li, Ran Gun, Jinhui Yuan, Torsten Hoefler

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Tags: Computer science, CUDA, Deep learning, Distributed computing, nVidia, nVidia A100, Package, Tesla P100

January 22, 2023 by hgpu

Distributed Calculations with Algorithmic Skeletons for Heterogeneous Computing Environments

Nina Herrmann, Herbert Kuchen

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Tags: Computer science, CUDA, Distributed computing, Heterogeneous systems, nVidia, nVidia GeForce GTX 750 Ti, nVidia GeFroce RTX 2080 Ti, nVidia Quadro K620

January 15, 2023 by hgpu

Kernel-as-a-Service: A Serverless Interface to GPUs

Nathan Pemberton, Anton Zabreyko, Zhoujie Ding, Randy Katz, Joseph Gonzalez

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Tags: Cloud, Computer science, CUDA, Deep learning, Distributed computing, nVidia, Package, Tesla V100

December 25, 2022 by hgpu

Integrating SkePU’s algorithmic skeletons with GPI on a cluster

Joel Almqvist

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Tags: Computer science, CUDA, Distributed computing, HPC, MPI, nVidia, OpenCL, OpenMP, Thesis

March 6, 2022 by hgpu

Lightning: Scaling the GPU Programming Model Beyond a Single GPU

Stijn Heldens, Pieter Hijma, Ben van Werkhoven, Jason Maassen, Rob V. van Nieuwpoort

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Tags: Computer science, CUDA, Distributed computing, nVidia, OpenCL, Package, Performance, Tesla P100

February 20, 2022 by hgpu

HeterPS: Distributed Deep Learning With Reinforcement Learning Based Scheduling in Heterogeneous Environments

Ji Liu, Zhihua Wu, Dianhai Yu, Yanjun Ma, Danlei Feng, Minxu Zhang, Xinxuan Wu, Xuefeng Yao, Dejing Dou

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Tags: Computer science, Deep learning, Distributed computing, Heterogeneous systems, Neural networks, nVidia, Package, Tesla V100

November 28, 2021 by hgpu

Performance Evaluation of Python ParallelProgramming Models: Charm4Py and mpi4py

Zane Fink, Simeng Liu, Jaemin Choi, Matthias Diener, Laxmikant V. Kale

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Tags: Benchmarking, Computer science, CUDA, Distributed computing, HPC, Machine learning, MPI, nVidia, Package, Performance, Python, Tesla V100

November 14, 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

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

StencilFlow: Mapping Large Stencil Programs to Distributed Spatial Computing Systems

Johannes de Fine Licht, Andreas Kuster, Tiziano De Matteis, Tal Ben-Nun, Dominic Hofer, Torsten Hoefler

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Tags: Code generation, Computer science, CUDA, Distributed computing, FPGA, Heterogeneous systems, nVidia, OpenCL, Package, Tesla P100, Tesla V100

January 24, 2021 by hgpu

JSDoop and TensorFlow.js: Volunteer Distributed Web Browser-Based Neural Network Training

José Á. Morell, Andrés Camero, Enrique Alba

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Tags: Computer science, Deep learning, Distributed computing, Package, TensorFlow

November 3, 2019 by hgpu

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Octo-Tiger: Astrophysics program simulating the evolution of star systems based on the fast multipole method on adaptive Octrees

Stellar Mergers with HPX-Kokkos and SYCL: Methods of using an Asynchronous Many-Task Runtime System with SYCL

BAT: A standardized benchmark suite for auto-tuners

Towards a Benchmarking Suite for Kernel Tuners

BenchDirect: A Directed Language Model for Compiler Benchmarks

A Deep Learning Model for Loop Interchange

Bridging Control-Centric and Data-Centric Optimization

Advanced evolutionary computation library built directly on top of PyTorch, created at NNAISENSE

EvoTorch: Scalable Evolutionary Computation in Python

See all packages

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high performance computing on graphics processing units: hgpu.org

ARK: GPU-driven Code Execution for Distributed Deep Learning

AutoDDL: Automatic Distributed Deep Learning with Asymptotically Optimal Communication

Distributed Calculations with Algorithmic Skeletons for Heterogeneous Computing Environments

Integrating SkePU’s algorithmic skeletons with GPI on a cluster

Lightning: Scaling the GPU Programming Model Beyond a Single GPU

HeterPS: Distributed Deep Learning With Reinforcement Learning Based Scheduling in Heterogeneous Environments

Performance Evaluation of Python ParallelProgramming Models: Charm4Py and mpi4py

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

Efficient MPI-based Communication for GPU-Accelerated Dask Applications

StencilFlow: Mapping Large Stencil Programs to Distributed Spatial Computing Systems

JSDoop and TensorFlow.js: Volunteer Distributed Web Browser-Based Neural Network Training

Recent source codes

E2C-Sim: a simulator and workload generator for Edge-to-Cloud environments

Kernel Tuner

Clava: C/C++ Source-to-Source Tool based on Clang

gpuLik

Octo-Tiger: Astrophysics program simulating the evolution of star systems based on the fast multipole method on adaptive Octrees

BAT: A standardized benchmark suite for auto-tuners

BenchDirect: A Directed Language Model for Compiler Benchmarks

A Deep Learning Model for Loop Interchange

Bridging Control-Centric and Data-Centric Optimization

Advanced evolutionary computation library built directly on top of PyTorch, created at NNAISENSE

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