AMCilk: A Framework for Multiprogrammed Parallel Workloads

Zhe Wang, Chen Xu, Kunal Agrawal, Jing Li

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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. 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.

Original languageEnglish (US)
Title of host publicationProceedings - 2020 IEEE 27th International Conference on High Performance Computing, Data, and Analytics, HiPC 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages212-222
Number of pages11
ISBN (Electronic)9780738110356
DOIs
StatePublished - Dec 2020
Event27th IEEE International Conference on High Performance Computing, Data, and Analytics, HiPC 2020 - Virtual, Pune, India
Duration: Dec 16 2020Dec 18 2020

Publication series

NameProceedings - 2020 IEEE 27th International Conference on High Performance Computing, Data, and Analytics, HiPC 2020

Conference

Conference27th IEEE International Conference on High Performance Computing, Data, and Analytics, HiPC 2020
CountryIndia
CityVirtual, Pune
Period12/16/2012/18/20

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Information Systems and Management
  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications

Keywords

  • Cilk
  • multiprogrammed
  • parallel computing

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