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CHARM-SYCL: New Unified Programming Environment for Multiple Accelerator Types

Norihisa Fujita, Beau Johnston, Ryohei Kobayashi, Keita Teranishi, Seyong Lee, Taisuke Boku, Jeffrey S. Vetter

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Tags: AMD Radeon Instinct MI100, ATI, Computer science, CUDA, Heterogeneous systems, nVidia, Package, performance portability, SYCL, Tesla V100

November 19, 2023 by hgpu

ExaNBody: a HPC framework for N-Body applications

Thierry Carrard, Raphaël Prat, Guillaume Latu, Killian Babilotte, Paul Lafourcade, Lhassan Amarsid, Laurent Soulard

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Tags: Astrophysics, Computer science, CUDA, MPI, N-body simulation, nVidia, nVidia A100, OpenMP, performance portability

November 19, 2023 by hgpu

On the Three P’s of Parallel Programming for Heterogeneous Computing: Performance, Productivity, and Portability

Atharva Gondhalekar, Wu-chun Feng

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Tags: Computer science, FPGA, Heterogeneous systems, HLS, nVidia, nVidia GeForce RTX 3090, oneAPI, OpenCL, Performance, performance portability, SYCL

November 12, 2023 by hgpu

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

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

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

Open SYCL on heterogeneous GPU systems: A case of study

Rocío Carratalá-Sáez, Francisco J. andújar, Yuri Torres, Arturo Gonzalez-Escribano, Diego R. Llanos

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Tags: Computer science, CUDA, Heterogeneous systems, HIP, nVidia, Package, performance portability, SYCL

October 15, 2023 by hgpu

Experience Migrating OpenCL to SYCL: A Case Study on Searches for Potential Off-Target Sites of Cas9 RNA-Guided Endonucleases on AMD GPUs

Zheming Jin, Jeffrey S. Vetter

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Tags: AMD Radeon Instinct MI100, AMD Radeon Instinct MI60, AMD Radeon VII, ATI, ATI Radeon HD 7870, Bioinformatics, Biology, OpenCL, performance portability, RNA, SYCL

October 1, 2023 by hgpu

OpenMP Kernel Language Extensions for Performance Portable GPU Codes

Shilei Tian, Tom Scogland, Barbara Chapman, Johannes Doerfert

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Tags: AMD Radeon Instinct MI250, ATI, Benchmarking, Compilers, Computer science, CUDA, HIP, nVidia, nVidia A100, OpenMP, performance portability

October 1, 2023 by hgpu

Evaluating the performance portability of SYCL across CPUs and GPUs on bandwidth-bound applications

Istvan Z Reguly

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Tags: AMD Radeon Instinct MI250X, ATI, Benchmarking, cfd, Computer science, CUDA, Fluid dynamics, HIP, Intel, Intel Ponte Vecchio Max 1100, nVidia, nVidia A100, OpenCL, performance portability, SYCL

September 24, 2023 by hgpu

high performance computing on graphics processing units: hgpu.org

CHARM-SYCL: New Unified Programming Environment for Multiple Accelerator Types

ExaNBody: a HPC framework for N-Body applications

On the Three P’s of Parallel Programming for Heterogeneous Computing: Performance, Productivity, and Portability

An approach to performance portability through generic programming

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

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

Open SYCL on heterogeneous GPU systems: A case of study

Experience Migrating OpenCL to SYCL: A Case Study on Searches for Potential Off-Target Sites of Cas9 RNA-Guided Endonucleases on AMD GPUs

OpenMP Kernel Language Extensions for Performance Portable GPU Codes

Evaluating the performance portability of SYCL across CPUs and GPUs on bandwidth-bound applications

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