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iGniter: Interference-Aware GPU Resource Provisioning for Predictable DNN Inference in the Cloud

Fei Xu, Jianian Xu, Jiabin Chen, Li Chen, Ruitao Shang, Zhi Zhou, Fangming Liu

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Tags: Cloud, Computer science, CUDA, Deep learning, Neural networks, nVidia, Package, Tesla V100

November 13, 2022 by hgpu

Enabling Data Movement and Computation Pipelining in Deep Learning Compiler

Guyue Huang, Yang Bai, Liu Liu, Yuke Wang, Bei Yu, Yufei Ding, Yuan Xie

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Tags: Computer science, CUDA, Deep learning, nVidia, nVidia A100

November 6, 2022 by hgpu

Using scheduling entropy amplification in CUDA/OpenMP code to exhibit non-reproducibility issues

David Defour

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Tags: Computer science, CUDA, Floating point error, nVidia, nVidia GeForce RTX 2060, OpenMP

November 6, 2022 by hgpu

Apple Silicon Performance in Scientific Computing

Connor Kenyon, Collin Capano

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Tags: Benchmarking, Computer science, Heterogeneous systems, nVidia, nVidia A100, nVidia V100, OpenCL, Performance

November 6, 2022 by hgpu

A systematic performance study of the parallel programming framework SkePU 3 using HPC-benchmarks

Erik Tedhamre

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Tags: Benchmarking, Computer science, CUDA, HPC, nVidia, OpenCL, OpenMP, Package, Performance

October 30, 2022 by hgpu

gSuite: A Flexible and Framework Independent Benchmark Suite for Graph Neural Network Inference on GPUs

Taha Tekdoğan, Serkan Göktaş, Ayse Yilmazer-Metin

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Tags: Benchmarking, Computer science, CUDA, Machine learning, Neural networks, nVidia, Performance, Tesla V100

October 30, 2022 by hgpu

Providing performance portable numerics for Intel GPUs

Yu-Hsiang M. Tsai, Terry Cojean, Hartwig Anzt

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Tags: AMD Radeon Instinct MI100, ATI, Computer science, CUDA, Linear Algebra, nVidia, nVidia A100, OpenCL, Package, performance portability, Sparse, SYCL

October 30, 2022 by hgpu

Benchmarking GPU and TPU Performance with Graph Neural Networks

Xiangyang Ju, Yunsong Wang, Daniel Murnane, Nicholas Choma, Steven Farrell, Paolo Calafiura

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Tags: Artificial intelligence, Benchmarking, Computer science, CUDA, Deep learning, Machine learning, Neural networks, nVidia, nVidia A100, Pattern recognition, Tesla V100, TPU

October 30, 2022 by hgpu

torchode: A Parallel ODE Solver for PyTorch

Marten Lienen, Stephan Günnemann

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Tags: Computer science, Deep learning, Numerical Analysis, nVidia, nVidia GeForce GTX 1080 Ti, ODEs, Ordinary differential equations, Package, PyTorch

October 30, 2022 by hgpu

A Ray Tracing Implementation Performance Comparison between the CPU and the GPU

Robin Nordmark, Tim Olsén

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Tags: 3D Graphics and Realism, CUDA, nVidia, nVidia GeForce GTX 1070, Package, Raytracing, Rendering

October 23, 2022 by hgpu

Tausch: A halo exchange library for large heterogeneous computing systems using MPI, OpenCL, and CUDA

Lukas Spies, Amanda Bienz, David Moulton, Luke Olson, Andrew Reisner

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Tags: Computer science, CUDA, Heterogeneous systems, MPI, nVidia, OpenCL, Package, Tesla V100

October 23, 2022 by hgpu

Thwarting Piracy: Anti-debugging Using GPU-assisted Self-healing Codes

Adhokshaj Mishra, Manjesh Kumar Hanawal

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 960 M, Security

October 23, 2022 by hgpu

Faial: finds bugs in CUDA kernels

Sound and Partially-Complete Static Analysis of Data-Races in GPU Programs

Effects of OpenCL-Based Parallelization Methods on Explicit Numerical Methods to Solve the Heat Equation

UVaFTLE: Lagrangian finite time Lyapunov exponent extraction for fluid dynamic applications

Challenging Portability Paradigms: FPGA Acceleration Using SYCL and OpenCL

HPCToolkit

Refining HPCToolkit for application performance analysis at exascale

Open Catalog of Best Practices for Modernization and Optimization

Optimizing the Weather Research and Forecasting Model with OpenMP Offload and Codee

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high performance computing on graphics processing units: hgpu.org

Enabling Data Movement and Computation Pipelining in Deep Learning Compiler

Using scheduling entropy amplification in CUDA/OpenMP code to exhibit non-reproducibility issues

Apple Silicon Performance in Scientific Computing

A systematic performance study of the parallel programming framework SkePU 3 using HPC-benchmarks

gSuite: A Flexible and Framework Independent Benchmark Suite for Graph Neural Network Inference on GPUs

Providing performance portable numerics for Intel GPUs

Benchmarking GPU and TPU Performance with Graph Neural Networks

torchode: A Parallel ODE Solver for PyTorch

A Ray Tracing Implementation Performance Comparison between the CPU and the GPU

Tausch: A halo exchange library for large heterogeneous computing systems using MPI, OpenCL, and CUDA

Thwarting Piracy: Anti-debugging Using GPU-assisted Self-healing Codes

Recent source codes

Faial: finds bugs in CUDA kernels

Effects of OpenCL-Based Parallelization Methods on Explicit Numerical Methods to Solve the Heat Equation

Intel® SHMEM: Device initiated shared memory based communication library

miniLB: Lattice Botlzmann miniapp w/SYCL

AFOCL

2domination

MFC: Exascale simulation of multiphase/physics fluid dynamics

UVaFTLE: Lagrangian finite time Lyapunov exponent extraction for fluid dynamic applications

HPCToolkit

Open Catalog of Best Practices for Modernization and Optimization

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