An Introduction to hpxMP: A Modern OpenMP Implementation Leveraging HPX, An Asynchronous Many-Task System
An Introduction to hpxMP: A Modern OpenMP Implementation Leveraging HPX, An Asynchronous Many-Task System
复制标题
hpxMP 简介:利用异步多任务系统 HPX 的现代 OpenMP 实现
DOI:
10.1145/3318170.3318191
复制
发表时间:
2019
期刊:
影响因子:
--
通讯作者:
Kaiser, Hartmut
中科院分区:
文献类型:
--
作者:
Zhang, Tianyi;Shirzad, Shahrzad;Diehl, Patrick;Tohid, R.;Wei, Weile;Kaiser, Hartmut
Asynchronous Many-task (AMT) runtime systems have gained increasing acceptance in the HPC community due to the performance improvements offered by fine-grained tasking runtime systems. At the same time, C++ standardization efforts are focused on creating higher-level interfaces able to replace OpenMP or OpenACC in modern C++ codes. These higher level functions have been adopted in standards conforming runtime systems such as HPX, giving users the ability to simply utilize fork-join parallelism in their own codes. Despite innovations in runtime systems and standardization efforts users face enormous challenges porting legacy applications. Not only must users port their own codes, but often users rely on highly optimized libraries such as BLAS and LAPACK which use OpenMP for parallization. Current efforts to create smooth migration paths have struggled with these challenges, especially as the threading systems of AMT libraries often compete with the treading system of OpenMP.To overcome these issues, our team has developed hpxMP, an implementation of the OpenMP standard, which utilizes the underlying AMT system to schedule and manage tasks. This approach leverages the C++ interfaces exposed by HPX and allows users to execute their applications on an AMT system without changing their code.In this work, we compare hpxMP with Clang's OpenMP library with four linear algebra benchmarks of the Blaze C++ library. While hpxMP is often not able to reach the same performance, we demonstrate viability for providing a smooth migration for applications but have to be extended to benefit from a more general task based programming model.
登录
查看更多内容
DOI:
10.1109/ipdpsw.2017.14
发表时间:
2017
期刊:
2017 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)
影响因子:
--
作者:
Zahra Khatami;Hartmut Kaiser;J. Ramanujam
通讯作者:
J. Ramanujam
影响因子:
2.2
作者:
David A. Bader
通讯作者:
David A. Bader
DOI:
10.48550/arxiv.2401.03353
发表时间:
2023
期刊:
ArXiv
影响因子:
--
作者:
T. Heller;Patrick Diehl;Zachary D. Byerly;J. Biddiscombe;Hartmut Kaiser
通讯作者:
Hartmut Kaiser
DOI:
10.1109/ipdpsw.2018.00173
发表时间:
2018
期刊:
2018 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW
影响因子:
--
作者:
Wagle, Bibek;Kellar, Samuel;Serio, Adrian;Kaiser, Hartmut
通讯作者:
Kaiser, Hartmut
DOI:
10.1109/scala.2016.012
发表时间:
2016
期刊:
7th Workshop on Latest Advances in Scalable Algorithms for Large-Scale Systems (ScalA16
影响因子:
--
作者:
Khatami, Zahra;Kaiser, Hartmut;Grubel, Patricia;Serio, Adrian;Ramanujam, J.
通讯作者:
Ramanujam, J.