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Performance Optimization of Deep Learning Sparse Matrix Kernels on Intel Max Series GPU

Mohammad Zubair, Christoph Bauinger

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Tags: Computer science, CUDA, Deep learning, Intel, Intel Data Center GPU Max 1550, Linear Algebra, Machine learning, Mathematical Software, Matrix multiplication, nVidia, nVidia V100, Sparse matrix, SYCL

November 5, 2023 by hgpu

Applying the Midas Touch of Reproducibility to High-Performance Computing

A. C. Minor, W. Feng

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Tags: Computer science, CUDA, Heterogeneous systems, HPC, nVidia, OpenCL, SYCL

November 5, 2023 by hgpu

A Comparison of the Performance of the Molecular Dynamics Simulation Package GROMACS Implemented in the SYCL and CUDA Programming Models

Leonard Apanasevich, Yogesh Kale, Himanshu Sharma, Ana Marija Sokovic

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Tags: Benchmarking, Bioinformatics, Biology, Chemistry, Computer science, CUDA, Heterogeneous systems, Molecular dynamics, nVidia, nVidia A100, nVidia P100, nVidia V100, OpenCL, OpenMP, Performance, SYCL

November 5, 2023 by hgpu

OpenRAND: A Performance Portable, Reproducible Random Number Generation Library for Parallel Computations

Shihab Shahriar Khan, Bryce Palmer, Christopher Edelmaierd, Hasan Metin Aktulga

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Tags: Computer science, CUDA, HPC, nVidia, nVidia A100, Package, Pseudo-random number generators, Tesla V100

November 5, 2023 by hgpu

Performance Tuning for GPU-Embedded Systems: Machine-Learning-based and Analytical Model-driven Tuning Methodologies

Adrian Perez Dieguez, Margarita Amor Lopez

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Tags: Computer science, CUDA, FFT, Machine learning, nVidia, nVidia Jetson TX1, Performance, performance portability

October 29, 2023 by hgpu

Performance portability evaluation of blocked stencil computations on GPUs

Oscar Antepara, Hans Johansen, Samuel Williams, Tuowen Zhao, Samantha Hirsch, Priya Goyal, Mary Hall

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Tags: AMD Radeon Instinct MI250X, ATI, Code generation, Computer science, CUDA, HIP, nVidia, nVidia A100, Package, performance portability, Stencil computation, SYCL

October 29, 2023 by hgpu

A Performance-Portable SYCL Implementation of CRK-HACC for Exascale

Esteban M. Rangel, S. John Pennycook, Adrian Pope, Nicholas Frontiere, Zhiqiang Ma, Varsha Madananth

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Tags: AMD Radeon Instinct MI250X, ATI, Computer science, Cosmology, CUDA, HIP, HPC, nVidia, nVidia A100, Performance, performance portability, Physics, SYCL

October 29, 2023 by hgpu

GEVO-ML: Optimizing Machine Learning Code with Evolutionary Computation

Jhe-Yu Liou, Stephanie Forrest, Carole-Jean Wu

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Tags: Computer science, CUDA, Deep learning, Evolutionary Computations, Machine learning, Neural and Evolutionary Computing, nVidia, nVidia P100

October 29, 2023 by hgpu

Performance/power assessment of CNN packages on embedded automotive platforms

Paolo Burgio, Gianluca Brilli

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Tags: CNN, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia Jetson AGX Xavier, nVidia Jetson Nano, nVidia Jetson TX2, Performance

October 22, 2023 by hgpu

Performance portability analysis of SYCL with a classical CG on CPU, GPU, and FPGA

Julian Franquinet

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Tags: Benchmarking, Computer science, CUDA, FPGA, nVidia, nVidia Quadro GP100, Optimization, Performance, performance portability, SYCL

October 22, 2023 by hgpu

Predicting the Execution Time of a kernel on a specific GPU using PTX code

Monika Dagar, Jorge Roldan

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Tags: Computer science, CUDA, Deep learning, Machine learning, nVidia, nVidia GeForce GTX 1650, nVidia GeForce GTX Titan XP, Performance, PTX, Tesla K20, Tesla K80, Tesla M60, Tesla P100, Tesla T4, Tesla V100

October 22, 2023 by hgpu

SYCL in the Edge: Performance Evaluation for Heterogeneous Acceleration

Youssef Faqir-Rhazoui, Carlos García

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Tags: Computer science, CUDA, FPGA, Heterogeneous systems, nVidia, nVidia Jetson Nano, Optical flow, Package, SYCL

October 22, 2023 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

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high performance computing on graphics processing units: hgpu.org

Performance Optimization of Deep Learning Sparse Matrix Kernels on Intel Max Series GPU

Applying the Midas Touch of Reproducibility to High-Performance Computing

A Comparison of the Performance of the Molecular Dynamics Simulation Package GROMACS Implemented in the SYCL and CUDA Programming Models

OpenRAND: A Performance Portable, Reproducible Random Number Generation Library for Parallel Computations

Performance Tuning for GPU-Embedded Systems: Machine-Learning-based and Analytical Model-driven Tuning Methodologies

Performance portability evaluation of blocked stencil computations on GPUs

A Performance-Portable SYCL Implementation of CRK-HACC for Exascale

GEVO-ML: Optimizing Machine Learning Code with Evolutionary Computation

Performance/power assessment of CNN packages on embedded automotive platforms

Performance portability analysis of SYCL with a classical CG on CPU, GPU, and FPGA

Predicting the Execution Time of a kernel on a specific GPU using PTX code

SYCL in the Edge: Performance Evaluation for Heterogeneous Acceleration

Recent source codes

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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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