Intel Ponte Vecchio Max 1100

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Unified schemes for directive-based GPU offloading

Yohei Miki, Toshihiro Hanawa

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Tags: AMD Radeon Instinct MI210, ATI, Computer science, Diffusion equation, Intel, Intel Ponte Vecchio Max 1100, N-body simulation, nVidia, nVidia GH200, nVidia H100, OpenACC, OpenMP, Package

December 8, 2024 by hgpu

Portability of Fortran’s ‘do concurrent’ on GPUs

Ronald M. Caplan, Miko M. Stulajter, Jon A. Linker, Jeff Larkin, Henry A. Gabb, Shiquan Su, Ivan Rodriguez, Zachary Tschirhart, Nicholas Malaya

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Tags: Computer science, Fortran, Intel, Intel Data Center GPU Max 1550, Intel Ponte Vecchio Max 1100, nVidia, nVidia A100, nVidia GH200, nVidia H100, OpenACC, OpenMP, Package

August 18, 2024 by hgpu

Performance Portable Monte Carlo Particle Transport on Intel, NVIDIA, and AMD GPUs

John Tramm, Paul Romano, Patrick Shriwise, Amanda Lund, Johannes Doerfert, Patrick Steinbrecher, Andrew Siegel, Gavin Ridley

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Tags: AMD Radeon Instinct MI250X, ATI, Computer science, CUDA, Intel, Intel Data Center GPU Max 1550, Intel Ponte Vecchio Max 1100, nVidia, nVidia A100, OpenMP, Package, performance portability

March 24, 2024 by hgpu

Distributed OpenMP Offloading of OpenMC on Intel GPU MAX Accelerators

Yehonatan Fridman, Guy Tamir, Uri Steinitz, Gal Oren

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Tags: Benchmarking, Intel, Intel Ponte Vecchio Max 1100, Monte Carlo simulation, OpenMP, Package, Physics

March 10, 2024 by hgpu

SYCL-Bench 2020: Benchmarking SYCL 2020 on AMD, Intel, and NVIDIA GPUs

Luigi Crisci, Lorenzo Carpentieri, Peter Thoman, Aksel Alpay, Vincent Heuveline, Biagio Cosenza

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Tags: AMD Radeon Instinct MI100, ATI, Benchmarking, Computer science, Heterogeneous systems, HPC, Intel, Intel Ponte Vecchio Max 1100, nVidia, oneAPI, Portability, SYCL, Tesla V100

March 10, 2024 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

Portability and Scalability of OpenMP Offloading on State-of-the-art Accelerators

Yehonatan Fridman, Guy Tamir, Gal Oren

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Tags: Benchmarking, Computer science, Intel, Intel Ponte Vecchio Max 1100, nVidia, nVidia A100, oneAPI, OpenMP, Package, performance portability

April 16, 2023 by hgpu

Data-efficient LLM Fine-tuning for Code Generation

DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.

DeepCompile: A Compiler-Driven Approach to Optimizing Distributed Deep Learning Training

Large Language Model Powered C-to-CUDA Code Translation: A Novel Auto-Parallelization Framework

GigaAPI: a user-space API that simplifies multi-GPU programming, bridging the gap between the capabilities of parallel GPU systems and the ability of developers to harness their full potential

GigaAPI for GPU Parallelization

Coccinelle: a C code transformation engine using SmPL for matches, refactorings, and bug fixing

Advances in Semantic Patching for HPC-oriented Refactorings with Coccinelle

DuoReduce: MLIR's benchmark

Hardware-Assisted Software Testing and Debugging for Heterogeneous Computing

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high performance computing on graphics processing units: hgpu.org

Unified schemes for directive-based GPU offloading

Portability of Fortran’s ‘do concurrent’ on GPUs

Performance Portable Monte Carlo Particle Transport on Intel, NVIDIA, and AMD GPUs

Distributed OpenMP Offloading of OpenMC on Intel GPU MAX Accelerators

SYCL-Bench 2020: Benchmarking SYCL 2020 on AMD, Intel, and NVIDIA GPUs

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

Portability and Scalability of OpenMP Offloading on State-of-the-art Accelerators

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Data-efficient LLM Fine-tuning for Code Generation

DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.

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GigaAPI: a user-space API that simplifies multi-GPU programming, bridging the gap between the capabilities of parallel GPU systems and the ability of developers to harness their full potential

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