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Performance Assessment of OpenMP Compilers Targeting NVIDIA V100 GPUs

Joshua Hoke Davis, Christopher Daley, Swaroop Pophale, Thomas Huber, Sunita Chandrasekaran, Nicholas J. Wright

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, OpenACC, OpenMP, Performance, Tesla V100

October 25, 2020 by hgpu

On the performance of a highly-scalable Computational Fluid Dynamics code on AMD, ARM and Intel processors

Pablo Ouro, Unai Lopez-Novoa, Martyn Guest

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Tags: ARM, cfd, Fluid dynamics, HPC, Performance

October 18, 2020 by hgpu

It’s all about data movement: Optimising FPGA data access to boost performance

Nick Brown, David Dolman

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Tags: Computer science, FPGA, HLS, Performance

October 11, 2020 by hgpu

Flexible Performant GEMM Kernels on GPUs

Thomas Faingnaert, Tim Besard, Bjorn De Sutter

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Tags: Computer science, CUBLAS, CUDA, Julia, Machine learning, Mathematical Software, Matrix multiplication, Mixed precision, nVidia, nVidia GeForce RTX 2080 Ti, Package, Performance

October 4, 2020 by hgpu

Accelerating Sparse Matrix-Matrix Multiplication with GPU Tensor Cores

Orestis Zachariadis, Nitin Satpute, Juan Gómez-Luna, Joaquín Olivares

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Tags: Algorithms, Computer science, CUDA, Matrix multiplication, Mixed precision, nVidia, nVidia GeForce RTX 2070, nVidia Titan RTX, Package, Performance, Sparse matrix

October 4, 2020 by hgpu

Tools for GPU Computing–Debugging and Performance Analysis of Heterogenous HPC Applications

Michael Knobloch, Bernd Mohr

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Tags: Computer science, CUDA, nVidia, OpenCL, Performance, survey

September 13, 2020 by hgpu

Accelerating High-Order Stencils on GPUs

Ryuichi Sai, John Mellor-Crummey, Xiaozhu Meng, Mauricio Araya-Polo, Jie Meng

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Tags: Computer science, HPC, nVidia, nVidia Quadro NVS 510, Package, Performance, Stencil computation, Tesla P100, Tesla V100

September 13, 2020 by hgpu

HPX – The C++ Standard Library for Parallelism and Concurrency

Hartmut Kaiser, Patrick Diehl, Adrian S. Lemoine, Bryce Adelstein Lelbach, Parsa Amini, Agustín Berge, John Biddiscombe, Steven R. Brandt, Nikunj Gupta, Thomas Heller, Kevin Huck, Zahra Khatami, Alireza Kheirkhahan, Auriane Reverdell, Shahrzad Shirzad, Mikael Simberg, Bibek Wagle, Weile Wei, Tianyi Zhang

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Tags: Computer science, CUDA, nVidia, OpenCL, Package, Performance

September 13, 2020 by hgpu

GPA: A GPU Performance Advisor Based on Instruction Sampling

Keren Zhou, Xiaozhu Meng, Ryuichi Sai, John Mellor-Crummey

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Tags: Benchmarking, Computer science, nVidia, Performance, Tesla V100

September 13, 2020 by hgpu

Hierarchical Roofline Analysis: How to Collect Data using Performance Tools on Intel CPUs and NVIDIA GPUs

Charlene Yang

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Tags: Computer science, CUDA, nVidia, Package, Performance, Tesla V100

September 13, 2020 by hgpu

Performance portability through machine learning guided kernel selection in SYCL libraries

John Lawson

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Tags: AMD R9 Nano, ATI, Auto-Tuning, Benchmarking, Clustering, Computer science, Machine learning, Package, Performance, performance portability, SYCL

September 6, 2020 by hgpu

8 Steps to 3.7 TFLOP/s on NVIDIA V100 GPU: Roofline Analysis and Other Tricks

Charlene Yang

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Tags: Computer science, HPC, nVidia, OpenACC, Optimization, Package, Performance, Tesla V100

August 30, 2020 by hgpu

Code examples for paper on SYCL backend of Kokkos - IWOCL 2024

Experiences with implementing Kokkos’ SYCL backend

ROCm's implementation of Gromacs

GROMACS on AMD GPU-Based HPC Platforms: Using SYCL for Performance and Portability

SimSYCL: Synchronous, single-threaded, library-only SYCL implementation for debugging and verification

SimSYCL: A SYCL Implementation Targeting Development, Debugging, Simulation and Conformance

GPU plugin for PySCF

Python-Based Quantum Chemistry Calculations with GPU Acceleration

QArray

QArray: a GPU-accelerated constant capacitance model simulator for large quantum dot arrays

Celerity: High-level C++ for Accelerator Clusters

Balancing Tracking Granularity and Parallelism in Many-Task Systems: The Horizons Approach

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

gpu_tracker: Python package for tracking and profiling GPU utilization in both desktop and high-performance computing environments

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

94% on CIFAR-10 in 3.29 Seconds on a Single GPU

LOOPer: a polyhedral compiler for expressing fast and portable data parallel algorithms

LOOPer: A Learned Automatic Code Optimizer For Polyhedral Compilers

OpenMC Monte Carlo Code

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

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