Adaptive scheduling of multiprogrammed dynamic-multithreading applications

Document Type

Article

Publication Date

4-1-2022

Abstract

Modern parallel platforms, such as clouds or servers, are often shared among many different jobs. However, existing parallel programming runtime systems are designed and optimized for running a single parallel job, so it is generally hard to directly use them to schedule multiple parallel jobs without incurring high overhead and inefficiency. In this work, we develop AMCilk (Adaptive Multiprogrammed Cilk), a novel runtime system framework, designed to support multiprogrammed parallel workloads. AMCilk has client-server architecture where users can dynamically submit parallel jobs to the system. AMCilk has a single runtime system that runs these jobs while dynamically reallocating cores, last-level cache, and memory bandwidth among these jobs according to the scheduling policy. AMCilk exposes the interface to the system designer, which allows the designer to easily build different scheduling policies meeting the requirements of various application scenarios and performance metrics, while AMCilk transparently (to designers) enforces the scheduling policy. AMCilk also enables its use in cloud environment where other processes may be sharing the system with AMCilk. In this scenario, an external scheduler can change the resource availability for AMCilk and AMCilk seamlessly adapts to these changes. The primary feature of AMCilk is the low-overhead and responsive preemption mechanism that allows fast reallocation of cores between jobs. Our empirical evaluation indicates that AMCilk incurs small overheads and provides significant benefits on application-specific criteria for a set of 4 practical applications due to its fast and low-overhead core reallocation mechanism.

Identifier

85123794643 (Scopus)

Publication Title

Journal of Parallel and Distributed Computing

External Full Text Location

https://doi.org/10.1016/j.jpdc.2022.01.009

ISSN

07437315

First Page

76

Last Page

88

Volume

162

Grant

CCF-1733873

Fund Ref

National Science Foundation

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