high performance computing on graphics processing units: hgpu.org

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Exploring GPU-to-GPU Communication: Insights into Supercomputer Interconnects

Daniele De Sensi, Lorenzo Pichetti, Flavio Vella, Tiziano De Matteis, Zebin Ren, Luigi Fusco, Matteo Turisini, Daniele Cesarini, Kurt Lust, Animesh Trivedi, Duncan Roweth, Filippo Spiga, Salvatore Di Girolamo, Torsten Hoefler

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Tags: AMD Radeon Instinct MI250X, ATI, Benchmarking, Computer science, CUDA, HPC, MPI, nVidia, nVidia A100, nVidia H100, Performance

September 1, 2024 by hgpu

Characterizing CUDA and OpenMP Synchronization Primitives

Brandon Alexander Burtchell, Martin Burtscher

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Tags: Computer science, CUDA, nVidia, nVidia A100, nVidia GeForce RTX 2070, nVidia GeForce RTX 4090, OpenMP, Package, Performance

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

HiCCL: A Hierarchical Collective Communication Library

Mert Hidayetoglu, Simon Garcia de Gonzalo, Elliott Slaughter, Pinku Surana, Wen-mei Hwu, William Gropp, Alex Aiken

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Tags: AMD Radeon Instinct MI250X, ATI, Computer science, CUDA, HIP, nVidia, nVidia A100, Package, Performance, SYCL

August 18, 2024 by hgpu

In-Situ Techniques on GPU-Accelerated Data-Intensive Applications

Yi Ju, Mingshuai Li, Adalberto Perez, Laura Bellentani, Niclas Jansson, Stefano Markidis, Philipp Schlatter, Erwin Laure

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Tags: Computer science, CUDA, Fluid dynamics, HPC, Molecular dynamics, nVidia, nVidia A100, PC cluster, Performance

August 14, 2024 by hgpu

Data-driven Forecasting of Deep Learning Performance on GPUs

Seonho Lee, Amar Phanishayee, Divya Mahajan

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Tags: Computer science, CUDA, Deep learning, nVidia, nVidia A100, nVidia H100, nVidia P100, nVidia V100, Performance, PyTorch, Tesla T4

August 4, 2024 by hgpu

A Comparison of OpenCL, CUDA, and HIP as Compilation Targets for a Functional Array Language

Troels Henriksen

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Tags: AMD Radeon Instinct MI100, ATI, Code generation, Computer science, CUDA, HIP, nVidia, nVidia A100, OpenCL, Package, Programming Languages

July 28, 2024 by hgpu

Bringing Auto-tuning to HIP: Analysis of Tuning Impact and Difficulty on AMD and Nvidia GPUs

Milo Lurati, Stijn Heldens, Alessio Sclocco, Ben van Werkhoven

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Tags: AMD Radeon Instinct MI250X, AMD Radeon Pro W6600, ATI, Computer science, CUDA, HIP, nVidia, nVidia A100, nVidia RTX A4000, Package, Performance, Python

July 28, 2024 by hgpu

PSCToolkit: solving sparse linear systems with a large number of GPUs

Pasqua D'Ambra, Fabio Durastante, Salvatore Filippone

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Tags: CUDA, Linear Algebra, Mathematical Software, Mathematics, Numerical Analysis, nVidia, nVidia A100, Package, Sparse

July 7, 2024 by hgpu

A Survey of General-purpose Polyhedral Compilers

Arun Thangamani, Vincent Loechner, Stéphane Genaud

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Tags: Compilers, Computer science, CUDA, nVidia, nVidia A100, OpenCL, Package, survey

June 30, 2024 by hgpu

Breaking the Memory Wall: A Study of I/O Patterns and GPU Memory Utilization for Hybrid CPU-GPU Offloaded Optimizers

Avinash Maurya, Jie Ye, M. Mustafa Rafique, Franck Cappello, Bogdan Nicolae

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Tags: Computer science, CUDA, LLM, Memory, nVidia, nVidia A100, nVidia H100, Performance

June 23, 2024 by hgpu

Optimal Kernel Orchestration for Tensor Programs with Korch

Muyan Hu, Ashwin Venkatram, Shreyashri Biswas, Balamurugan Marimuthu, Bohan Hou, Gabriele Oliaro, Haojie Wang, Liyan Zheng, Xupeng Miao, Jidong Zhai

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Tags: Compilers, Computer science, CUDA, Neural networks, nVidia, nVidia A100, nVidia V100, Package, PyTorch

June 23, 2024 by hgpu

HPCTransCompile: An AI Compiler Generated Dataset for High-Performance CUDA Transpilation and LLM Preliminary Exploration

chemtrain: Training Molecular Dynamics Potentials in JAX

chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations

microSYCL: SYCL micro-benchmarks repository

Exploring SYCL as a Portability Layer for High-Performance Computing on CPUs

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high performance computing on graphics processing units: hgpu.org

Exploring GPU-to-GPU Communication: Insights into Supercomputer Interconnects

Characterizing CUDA and OpenMP Synchronization Primitives

Portability of Fortran’s ‘do concurrent’ on GPUs

HiCCL: A Hierarchical Collective Communication Library

In-Situ Techniques on GPU-Accelerated Data-Intensive Applications

Data-driven Forecasting of Deep Learning Performance on GPUs

A Comparison of OpenCL, CUDA, and HIP as Compilation Targets for a Functional Array Language

Bringing Auto-tuning to HIP: Analysis of Tuning Impact and Difficulty on AMD and Nvidia GPUs

PSCToolkit: solving sparse linear systems with a large number of GPUs

A Survey of General-purpose Polyhedral Compilers

Breaking the Memory Wall: A Study of I/O Patterns and GPU Memory Utilization for Hybrid CPU-GPU Offloaded Optimizers

Optimal Kernel Orchestration for Tensor Programs with Korch

Recent source codes

Efficient GPU Implementation of Multi-Precision Integer Division

ParEval: A Parallel Code Evaluation Benchmark

FlashSparse: Minimizing Computation Redundancy for Fast Sparse Matrix Multiplications on Tensor Cores

exa-AMD: Exascale Accelerated Materials Discovery

WiLLM: An Open Wireless LLM Communication System

Vcc: the Vulkan Clang Compiler

hpcbench: A set of benchmarking utilities for biomolecular simulation tools

HPCTransCompile: An AI Compiler Generated Dataset for High-Performance CUDA Transpilation and LLM Preliminary Exploration

chemtrain: Training Molecular Dynamics Potentials in JAX

microSYCL: SYCL micro-benchmarks repository

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