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An approach to performance portability through generic programming

Andreas Hadjigeorgiou, Christodoulos Stylianou, Michele Weiland, Dirk Jacob Verschuur, Jacob Finkenrath

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Tags: AMD Radeon Instinct MI100, ATI, Benchmarking, Computer science, CUDA, HIP, HPC, nVidia, OpenMP, Package, Performance, performance portability, Portability, Tesla V100

November 12, 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

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

Dynamic autotuning of SpMV kernel in CUSP library

Miroslav Demek

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Tags: Auto-Tuning, Computer science, nVidia, nVidia GeForce GTX 1070, nVidia GeForce RTX 2080, Performance, Sparse matrix, Thesis

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

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

Fortran performance optimisation and auto-parallelisation by leveraging MLIR-based domain specific abstractions in Flang

Nick Brown, Maurice Jamieson, Anton Lydike, Emilien Bauer, Tobias Grosser

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Tags: Computer science, Fortran, HPC, LLVM, MPI, nVidia, Package, Performance, Tesla V100

October 8, 2023 by hgpu

Automated Buffer Sizing of Dataflow Applications in a High-Level Synthesis Workflow

Alexandre Honorat, Mickaël Dardaillon, Hugo Miomandre, Jean-François Nezan

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Tags: Code generation, Computer science, FPGA, HLS, OpenCL, Package, Performance

October 8, 2023 by hgpu

Improving the Efficiency of OpenCL Kernels through Pipes

Mostafa Eghbali Zarch, Michela Becchi

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Tags: Computer science, FPGA, OpenCL, Performance, performance portability

September 17, 2023 by hgpu

Fortran High-Level Synthesis: Reducing the barriers to accelerating HPC codes on FPGAs

Gabriel Rodriguez-Canal, Nick Brown, Tim Dykes, Jessica R. Jones, Utz-Uwe Haus

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Tags: Computer science, Fortran, FPGA, HLS, HPC, OpenCL, Performance

September 6, 2023 by hgpu

CLUEstering: a high-performance density-based clustering library for scientific computing

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high performance computing on graphics processing units: hgpu.org

An approach to performance portability through generic programming

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

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

Dynamic autotuning of SpMV kernel in CUSP library

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

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

Fortran performance optimisation and auto-parallelisation by leveraging MLIR-based domain specific abstractions in Flang

Automated Buffer Sizing of Dataflow Applications in a High-Level Synthesis Workflow

Improving the Efficiency of OpenCL Kernels through Pipes

Fortran High-Level Synthesis: Reducing the barriers to accelerating HPC codes on FPGAs

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