Toward a Standard Interface for User-Defined Scheduling in OpenMP

Kale, Vivek; Iwainsky, Christian; Klemm, Michael; Korndörfer, Jonas H. Müller; Ciorba, Florina M.

doi:10.1007/978-3-030-28596-8_13

Cited by 10 publications

(8 citation statements)

References 25 publications

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“…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%

LB4OMP: A Dynamic Load Balancing Library for Multithreaded Applications

Korndörfer

Eleliemy

Mohammed³

et al. 2022

IEEE Trans. Parallel Distrib. Syst.

Self Cite

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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%

LB4OMP: A Dynamic Load Balancing Library for Multithreaded Applications

Korndörfer

Eleliemy

Mohammed³

et al. 2022

IEEE Trans. Parallel Distrib. Syst.

Self Cite

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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 [87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,100,105,71].…”

Section: Validation and Verification (Vandv)mentioning

confidence: 99%

ECP Software Technology Capability Assessment Report V3.0

Heroux

McInnes

et al. 2022

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The Exascale Computing Project (ECP) Software Technology (ST) focus area is responsible for (1) developing critical software capabilities that will enable the successful execution of ECP applications and (2) 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 computing (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 6-12 months). Highlights from this version of the report are presented here.This version of the CAR contains the following updates relative to the previous revision.• This report highlights the progress with the Extreme-scale Scientific Software Stack (E4S) efforts.In particular, this report discusses how E4S continues to gain traction as a first-class entity in the HPC ecosystem, enabling new conversations with users, facilities, vendors, other US agencies, and international partners.• The several-page summaries of each ECP Level 4 project were updated to reflect recent progress and next steps (Section 4). Of particular note are the experiences of our teams on early-access systems for Frontier.• The E4S is described further. E4S is now updated via quarterly releases. E4S is the primary integration and delivery vehicle for ECP ST capabilities (Section 2.1.1).• The ECP ST software development kit (SDK) effort further refined its groupings (Section 2.1.2).The 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 approximately 70 software products (Section 2.1.3) in six technical areas (Table 1). Since publishing the previous revision of the CAR, the team has continued to evolve the 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, the team is working on performance portability layers (Kokkos and RAJA) and participating in OpenMP and OpenACC software design and development that will enable applications to write much of their source code without needing to provide vendor-specific implementations for each exascale system. One legacy of ECP ST efforts is anticipated to be a software stack that supports Intel and AMD accelerators in addition to NVIDIA's accelerators (Section 4.1).Development Tools: The team is enhancing existing widely used compilers (e.g., LLVM) and performance tools for next-generation platforms. Compilers are critical for heterogeneous archi...

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“…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 [25][26][27] 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%

LB4OMP: A Dynamic Load Balancing Library for Multithreaded Applications

Korndörfer¹,

Eleliemy²,

M³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Toward a Standard Interface for User-Defined Scheduling in OpenMP

Cited by 10 publications

References 25 publications

LB4OMP: A Dynamic Load Balancing Library for Multithreaded Applications

LB4OMP: A Dynamic Load Balancing Library for Multithreaded Applications

ECP Software Technology Capability Assessment Report V3.0

LB4OMP: A Dynamic Load Balancing Library for Multithreaded Applications

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