A Fine-grained Asynchronous Bulk Synchronous parallelism model for PGAS applications

Journal Article · Sat Apr 01 00:00:00 EDT 2023 · Journal of Computational Science

DOI:https://doi.org/10.1016/j.jocs.2023.102014· OSTI ID:2422859

Paul, Sri Raj ^[1]; Hayashi, Akihiro ^[2]; Chen, Kun ^[3]; Elmougy, Youssef ^[2]; Sarkar, Vivek ^[2]

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1. Intel Corporation, Austin, TX (United States)
2. Georgia Institute of Technology, Atlanta, GA (United States)
3. Meta, Menlo Park, CA (United States)

The Partitioned Global Address Space (PGAS) model is well suited for executing irregular applications on cluster-based systems, due to its efficient support for short, one-sided messages. Separately, the actor model has been gaining popularity as a productive asynchronous message-passing approach for distributed objects in enterprise and cloud computing platforms, typically implemented in languages such as Erlang, Scala or Rust. To the best of our knowledge, there has been no past work on using the actor model to deliver both productivity and scalability to irregular PGAS applications with large number of small messages. In this paper, we introduce a new programming system for PGAS applications, in which point-to-point remote operations can be expressed as fine-grained asynchronous actor messages. In our approach, the programmer does not need to worry about programming complexities related to message aggregation and termination detection. Our approach can be viewed as extending the classical Bulk Synchronous Parallelism model with fine-grained asynchronous communications within a phase or superstep. Here, we believe that our approach offers a desirable point in the productivity-performance space for PGAS applications, with more scalable performance and higher productivity relative to past approaches. Specifically, for seven irregular mini-applications from the Bale Kernels and three graph kernels executed using 2048 cores in the NERSC Cori system, our approach shows geometric mean performance improvements of ≥ 20X relative to standard PGAS versions (UPC and OpenSHMEM) while maintaining comparable productivity to those versions.

• Research Organization: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States). National Energy Research Scientific Computing Center (NERSC)
• Sponsoring Organization: USDOE; USDOE Office of Science (SC)
• Grant/Contract Number: AC02-05CH11231
• OSTI ID: 2422859
• Alternate ID(s): OSTI ID: 1970079
• Journal Information: Journal of Computational Science, Journal Name: Journal of Computational Science Journal Issue: C Vol. 69; ISSN 1877-7503
• Publisher: Elsevier
• Country of Publication: United States
• Language: English

Related Subjects

97 MATHEMATICS AND COMPUTING
Actors
Bale
Communication aggregation
Conveyors
Irregular applications
Large scale graph analytics
OpenSHMEM
PGAS
Selectors