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On a Simplified Approach to Achieve Parallel Performance and Portability Across CPU and GPU Architectures

Nathaniel Morgan, Caleb Yenusah, Adrian Diaz, Daniel Dunning,Jacob Moore,Erin Heilman, Calvin Roth, Evan Lieberman, Steven Walton, Sarah Brown, Daniel Holladay, Marko Knezevic, Gavin Whetstone, Zachary Baker, Robert Robey

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Tags: Computer science, CUDA, Fortran, HIP, nVidia, Package, performance portability, Pthreads

November 10, 2024 by hgpu

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

Chayanon (Namo)Wichitrnithed, Woo-Sun-Yang, Yun (Helen)He, Brad Richardson, Koichi Sakaguchi, Manuel Arenaz, William I. Gustafson Jr., Jacob Shpund, Ulises Costi Blanco, Alvaro Goldar Dieste

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Tags: Computer science, CUDA, Fortran, MPI, nVidia, nVidia A100, OpenMP, Optimization, Package, Weather prediction

September 15, 2024 by hgpu

CI/CD Efforts for Validation, Verification and Benchmarking OpenMP Implementations

Aaron Jarmusch, Felipe Cabarcas, Swaroop Pophale, Andrew Kallai, Johannes Doerfert, Luke Peyralans, Seyong Lee, Joel Denny, Sunita Chandrasekaran

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Tags: AMD Radeon Instinct MI210, AMD Radeon Instinct MI250X, ATI, Compilers, Computer science, Fortran, Heterogeneous systems, HPC, nVidia, nVidia H100, OpenMP

August 25, 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

Creating a Dataset for High-Performance Computing Code Translation using LLMs: A Bridge Between OpenMP Fortran and C+

Bin Lei, Caiwen Ding, Le Chen, Pei-Hung Lin, Chunhua Liao

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Tags: Benchmarking, Computer science, CUDA, Fortran, Machine learning, nVidia, nVidia RTX A6000, OpenMP, Package

November 19, 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

Comparing Llama-2 and GPT-3 LLMs for HPC kernels generation

Pedro Valero-Lara, Alexis Huante, Mustafa Al Lail, William F. Godoy, Keita Teranishi, Prasanna Balaprakash, Jeffrey S. Vetter

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Tags: Code generation, Computer science, CUDA, Deep learning, Fortran, HIP, HPC, nVidia, OpenACC, OpenMP, Package, Python

September 17, 2023 by hgpu

Many Cores, Many Models: GPU Programming Model vs. Vendor Compatibility Overview

Andreas Herten

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Tags: Computer science, CUDA, Fortran, HIP, HPC, nVidia, Overview, programming model, SYCL

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

Quantifying OpenMP: Statistical Insights into Usage and Adoption

Tal Kadosh, Niranjan Hasabnis, Timothy Mattson, Yuval Pinter, Gal Oren

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Tags: Computer science, CUDA, Databases, Fortran, HPC, MPI, nVidia, OpenACC, OpenCL, OpenMP, Package, SYCL

August 20, 2023 by hgpu

Creating a Dataset Supporting Translation Between OpenMP Fortran and C++ Code

Bin Lei, Caiwen Ding, Le Chen, Pei-Hung Lin, Chunhua Liao

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Tags: Computer science, CUDA, Fortran, Machine learning, nVidia, nVidia RTX 6000 Ada, OpenMP, Package

July 24, 2023 by hgpu

Evaluation of OpenAI Codex for HPC Parallel Programming Models Kernel Generation

William F. Godoy, Pedro Valero-Lara, Keita Teranishi, Prasanna Balaprakash, Jeffrey S. Vetter

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Tags: AI, Artificial intelligence, Benchmarking, Code generation, Computer science, CUDA, Fortran, HPC, Julia, nVidia, OpenACC, OpenMP, Package, Python

July 2, 2023 by hgpu

Kokkidio: Kokkos+Eigen - write fast, readable multi-platform code

Kokkidio: Fast, expressive, portable code, based on Kokkos and Eigen

LLaMA-Inference-Bench

LLM-Inference-Bench: Inference Benchmarking of Large Language Models on AI Accelerators

MATAR

On a Simplified Approach to Achieve Parallel Performance and Portability Across CPU and GPU Architectures

Over-synchronization in GPU Programs

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

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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