Preparing Ginkgo for AMD GPUs – A Testimonial on Porting CUDA Code to HIP

06/25/2020
by   Yuhsiang M. Tsai, et al.
0

With AMD reinforcing their ambition in the scientific high performance computing ecosystem, we extend the hardware scope of the Ginkgo linear algebra package to feature a HIP backend for AMD GPUs. In this paper, we report and discuss the porting effort from CUDA, the extension of the HIP framework to add missing features such as cooperative groups, the performance price of compiling HIP code for AMD architectures, and the design of a library providing native backends for NVIDIA and AMD GPUs while minimizing code duplication by using a shared code base.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
03/18/2021

Porting a sparse linear algebra math library to Intel GPUs

With the announcement that the Aurora Supercomputer will be composed of ...
research
04/27/2018

Tiramisu: A Code Optimization Framework for High Performance Systems

This paper introduces Tiramisu, an optimization framework designed to ge...
research
08/06/2023

Bandicoot: C++ Library for GPU Linear Algebra and Scientific Computing

This report provides an introduction to the Bandicoot C++ library for li...
research
02/11/2020

AnySeq: A High Performance Sequence Alignment Library based on Partial Evaluation

Sequence alignments are fundamental to bioinformatics which has resulted...
research
10/31/2016

Hybrid CPU-GPU generation of the Hamiltonian and Overlap matrices in FLAPW methods

In this paper we focus on the integration of high-performance numerical ...
research
09/19/2023

Julia as a unifying end-to-end workflow language on the Frontier exascale system

We evaluate using Julia as a single language and ecosystem paradigm powe...
research
09/18/2018

SCOPE: C3SR Systems Characterization and Benchmarking Framework

This report presents the design of the Scope infrastructure for extensib...

Please sign up or login with your details

Forgot password? Click here to reset