Optimized Execution of Parallel Loops via User-Defined Scheduling Policies

Bak, Seonmyeong; Guo, Yanfei; Balaji, Pavan; Sarkar, Vivek

doi:10.1145/3337821.3337913

Cited by 9 publications

(14 citation statements)

References 38 publications

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“…Another direction of related work includes efforts that propose generic interfaces to allow users to implement their own loop scheduling techniques in different runtime libraries [28] [29] [30]. These efforts reduce the development challenges associated with the direct modification to the RTL source codes, i.e., developers can implement their scheduling technique via simplified, and ideally, well-documented interfaces.…”

Section: Related Workmentioning

confidence: 99%

“…However, these efforts do not exclude the need for extensive scheduling libraries to validate novel scheduling techniques and exploit the increased hardware parallelism of modern HPC systems. Therefore, such efforts [28], [29], [30] can be seen as potential methods that facilitate the development of another version of the LB4OMP scheduling library in the future.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

LB4OMP: A Dynamic Load Balancing Library for Multithreaded Applications

Korndörfer

Eleliemy

Mohammed³

et al. 2022

IEEE Trans. Parallel Distrib. Syst.

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Exascale computing systems will exhibit high degrees of hierarchical parallelism, with thousands of computing nodes and hundreds of cores per node. Efficiently exploiting hierarchical parallelism is challenging due to load imbalance that arises at multiple levels. OpenMP is the most widely-used standard for expressing and exploiting the ever-increasing node-level parallelism. The scheduling options in OpenMP are insufficient to address the load imbalance that arises during the execution of multithreaded applications. The limited scheduling options in OpenMP hinder research on novel scheduling techniques which require comparison with others from the literature. This work introduces LB4OMP, an open-source dynamic load balancing library that implements successful scheduling algorithms from the literature. LB4OMP is a research infrastructure designed to spur and support present and future scheduling research, for the benefit of multithreaded applications performance. Through an extensive performance analysis campaign, we assess the effectiveness and demystify the performance of all loop scheduling techniques in the library. We show that, for numerous applications-systems pairs, the scheduling techniques in LB4OMP outperform the scheduling options in OpenMP. Node-level load balancing using LB4OMP leads to reduced cross-node load imbalance and to improved MPI+OpenMP applications performance, which is critical for Exascale computing.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

LB4OMP: A Dynamic Load Balancing Library for Multithreaded Applications

Korndörfer

Eleliemy

Mohammed³

et al. 2022

IEEE Trans. Parallel Distrib. Syst.

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show abstract

“…Another direction of related work includes efforts that propose generic interfaces to allow users to implement their own loop scheduling techniques in different runtime libraries [25] [26] [27]. These efforts reduce the development challenges associated with the direct modification to the RTL source codes, i.e., developers can implement their scheduling technique via simplified, and ideally, well-documented interfaces.…”

Section: Related Workmentioning

confidence: 99%

LB4OMP: A Dynamic Load Balancing Library for Multithreaded Applications

Korndörfer¹,

Eleliemy²,

M³

et al. 2021

Preprint

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Exascale computing systems will exhibit high degrees of hierarchical parallelism, with thousands of computing nodes and hundreds of cores per node. Efficiently exploiting hierarchical parallelism is challenging due to load imbalance that arises at multiple levels. OpenMP is the most widelyused standard for expressing and exploiting the ever-increasing node-level parallelism. The scheduling options in OpenMP are insufficient to address the load imbalance that arises during the execution of multithreaded applications. The limited scheduling options in OpenMP hinder research on novel scheduling techniques which require comparison with others from the literature. This work introduces LB4OMP, an open-source dynamic load balancing library that implements successful scheduling algorithms from the literature. LB4OMP is a research infrastructure designed to spur and support present and future scheduling research, for the benefit of multithreaded applications performance. Through an extensive performance analysis campaign, we assess the effectiveness and demystify the performance of all loop scheduling techniques in the library. We show that, for numerous applications-systems pairs, the scheduling techniques in LB4OMP outperform the scheduling options in OpenMP. Node-level load balancing using LB4OMP leads to reduced cross-node load imbalance and to improved MPI+OpenMP applications performance, which is critical for Exascale computing.

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“…The training and outreach activity is a cross-cutting effort which is supported by resources from SOLLVE and ECP Broader Engagement, with contributions by external collaborators, notably Lawrence Berkeley National Laboratory. A number of articles have also been published as part of the SOLLVE effort [76,77,78,79,80,81,82,83,84,85,86,87,88,89].…”

Section: Validation and Verification (Vandv)mentioning

confidence: 99%

ECP Software Technology Capability Assessment Report

Heroux

Computing²,

Carter

et al. 2018

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The Exascale Computing Project (ECP) Software Technology (ST) Focus Area is responsible for developing critical software capabilities that will enable successful execution of ECP applications, and for providing key components of a productive and sustainable Exascale computing ecosystem that will position the US Department of Energy (DOE) and the broader high performance (HPC) community with a firm foundation for future extreme-scale computing capabilities. This ECP ST Capability Assessment Report (CAR) provides an overview and assessment of current ECP ST capabilities and activities, giving stakeholders and the broader HPC community information that can be used to assess ECP ST progress and plan their own efforts accordingly. ECP ST leaders commit to updating this document on regular basis (every six to 12 months). Highlights from this version of the report are presented here. What is new in CAR V2.0: CAR V2.0 contains the following updates relative to CAR V1.5. • We introduce the FY20-23 project structure. ECP ST now consists of 6 (up from 5) level-3 (L3) technical areas, introducing the NNSA ST L3 area, which brings into one L3 the ECP open source development efforts at NNSA labs that are of particular importance to the rest of ECP. The number of ECP ST level-4 (L4) subprojects has been reduced from 55 to 33. The strategic aggregation of projects into fewer and larger units enables us to better manage L4 subprojects consistently as a portfolio. See Section 1.2. • We describe new and enhanced project management processes and resources including our iterative planning process, new KPP-3 capability integration process, a product dictionary and dependency management database. These new project features and related dashboards enable more insight and better information to effectively manage efforts across ECP. See Section 2. • The two-page summaries of each ECP L4 projects have been updated to reflect recent progress and next steps. See Section 4. • The Extreme-scale Scientific Software Stack (E4S) is further described. The third release, which is also the first major public release Version 1.0, was November 18, 2019. E4S is the primary integration and delivery vehicle for ECP ST capabilities. See Section 2.1.1. • The ECP ST SDK effort has further refined its groupings. See Section 2.1.2. The Exascale Computing Project Software Technology (ECP ST) focus area represents the key bridge between Exascale systems and the scientists developing applications that will run on those platforms. ECP ST efforts contribute to 70 software products (Section 2.1.3) in six technical areas (Table 1). Since the publishing of CAR V1.5, we have introduced a product dictionary of official product names, which enables more rigorous mapping of ECP ST deliverables to stakeholders (Section 2.1.4). Programming Models & Runtimes: In addition to developing key enhancements to MPI and OpenMP for scalable systems with accelerated node architectures, we are working on performance portability layers (Kokkos and RAJA) and participating in OpenMP and O...

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Optimized Execution of Parallel Loops via User-Defined Scheduling Policies

Cited by 9 publications

References 38 publications

LB4OMP: A Dynamic Load Balancing Library for Multithreaded Applications

LB4OMP: A Dynamic Load Balancing Library for Multithreaded Applications

LB4OMP: A Dynamic Load Balancing Library for Multithreaded Applications

ECP Software Technology Capability Assessment Report

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