A Survey of Distributed Asynchronous Many-Task Models and Their Applications

Asynchronous many-task (AMT) runtime systems have become an important paradigm for expressing fine-grained parallelism and managing asynchrony in high-performance computing (HPC). Originating from early dataflow concepts, AMTs have evolved to enable dynamic task generation, explicit dependency management, and asynchronous execution, facilitating the overlap of computation and communication. These capabilities address the limitations of traditional bulk-synchronous models, such as those employed in MPI+X , which can struggle with irregular, adaptive, or data-driven workloads. This survey provides a comprehensive overview of representative distributed AMT systems—including Charm ++ , HPX, Legion, PaRSEC, Uintah, Chapel, and StarPU—focusing on their design principles, execution models, and runtime mechanisms for scheduling, communication, and synchronization. We examine how these systems tackle key challenges such as load imbalance, runtime overheads, programmability, and performance portability. In addition, the paper discusses application domains where AMTs have demonstrated tangible benefits and highlights the conditions under which their use is most advantageous. The goal of this survey is to equip researchers and practitioners with a clear understanding of distributed AMT models and to provide guidance for selecting and applying the most suitable runtime system for specific computational objectives.

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Publication Details

Journal
ACM Computing Surveys
Published
2026-08-14
DOI
https://doi.org/10.1145/3840389
Citations
1
Primary Topic
Parallel Computing and Optimization Techniques
Type
article
Field-Weighted Citation Impact
13.93
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A Survey of Distributed Asynchronous Many-Task Models and Their Applications

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A Survey of Distributed Asynchronous Many-Task Models and Their Applications

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article en
1 citations

Abstract

Asynchronous many-task (AMT) runtime systems have become an important paradigm for expressing fine-grained parallelism and managing asynchrony in high-performance computing (HPC). Originating from early dataflow concepts, AMTs have evolved to enable dynamic task generation, explicit dependency management, and asynchronous execution, facilitating the overlap of computation and communication. These capabilities address the limitations of traditional bulk-synchronous models, such as those employed in MPI+X , which can struggle with irregular, adaptive, or data-driven workloads. This survey provides a comprehensive overview of representative distributed AMT systems—including Charm ++ , HPX, Legion, PaRSEC, Uintah, Chapel, and StarPU—focusing on their design principles, execution models, and runtime mechanisms for scheduling, communication, and synchronization. We examine how these systems tackle key challenges such as load imbalance, runtime overheads, programmability, and performance portability. In addition, the paper discusses application domains where AMTs have demonstrated tangible benefits and highlights the conditions under which their use is most advantageous. The goal of this survey is to equip researchers and practitioners with a clear understanding of distributed AMT models and to provide guidance for selecting and applying the most suitable runtime system for specific computational objectives.

ACM Computing Surveys
University of Stuttgart (DE), Louisiana State University (US), Lawrence Livermore National Laboratory (US), Oak Ridge National Laboratory (US), Advanced Micro Devices (Canada) (CA), Los Alamos National Laboratory (US), University of Illinois Urbana-Champaign (US), University of Utah (US), SLAC National Accelerator Laboratory (US), Digital Science (United States) (US), Hewlett Packard Enterprise (United States) (US), Stanford Medicine (US), Stony Brook University (US)
Openalex Percentile: Top 4%
Parallel Computing and Optimization Techniques
13.93
DOIOpen Access PDFOpenAlex
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