Can far memory improve job throughput?

Emmanuel Amaro; Christopher Branner-Augmon; Zhihong Luo; Amy Ousterhout; Marcos K. Aguilera; Aurojit Panda; Sylvia Ratnasamy; Scott Shenker

doi:10.1145/3342195.3387522

Can far memory improve job throughput?

Emmanuel Amaro, Christopher Branner-Augmon, Zhihong Luo, Amy Ousterhout, Marcos K. Aguilera, Aurojit Panda, Sylvia Ratnasamy, Scott Shenker

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

As memory requirements grow, and advances in memory technology slow, the availability of sufficient main memory is increasingly the bottleneck in large compute clusters. One solution to this is memory disaggregation, where jobs can remotely access memory on other servers, or far memory. This paper first presents faster swapping mechanisms and a far memory-Aware cluster scheduler that make it possible to support far memory at rack scale. Then, it examines the conditions under which this use of far memory can increase job throughput. We find that while far memory is not a panacea, for memory-intensive workloads it can provide performance improvements on the order of 10% or more even without changing the total amount of memory available.

Original language	English (US)
Title of host publication	Proceedings of the 15th European Conference on Computer Systems, EuroSys 2020
Publisher	Association for Computing Machinery, Inc
ISBN (Electronic)	9781450368827
DOIs	https://doi.org/10.1145/3342195.3387522
State	Published - Apr 15 2020
Event	15th European Conference on Computer Systems, EuroSys 2020 - Heraklion, Greece Duration: Apr 27 2020 → Apr 30 2020

Publication series

Name	Proceedings of the 15th European Conference on Computer Systems, EuroSys 2020

Conference

Conference	15th European Conference on Computer Systems, EuroSys 2020
Country/Territory	Greece
City	Heraklion
Period	4/27/20 → 4/30/20

ASJC Scopus subject areas

Information Systems
Computer Networks and Communications
Hardware and Architecture
Software
Control and Systems Engineering

Access to Document

10.1145/3342195.3387522

Cite this

Amaro, E., Branner-Augmon, C., Luo, Z., Ousterhout, A., Aguilera, M. K., Panda, A., Ratnasamy, S., & Shenker, S. (2020). Can far memory improve job throughput? In Proceedings of the 15th European Conference on Computer Systems, EuroSys 2020 (Proceedings of the 15th European Conference on Computer Systems, EuroSys 2020). Association for Computing Machinery, Inc. https://doi.org/10.1145/3342195.3387522

Can far memory improve job throughput? / Amaro, Emmanuel; Branner-Augmon, Christopher; Luo, Zhihong et al.
Proceedings of the 15th European Conference on Computer Systems, EuroSys 2020. Association for Computing Machinery, Inc, 2020. (Proceedings of the 15th European Conference on Computer Systems, EuroSys 2020).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Amaro, E, Branner-Augmon, C, Luo, Z, Ousterhout, A, Aguilera, MK, Panda, A, Ratnasamy, S & Shenker, S 2020, Can far memory improve job throughput? in Proceedings of the 15th European Conference on Computer Systems, EuroSys 2020. Proceedings of the 15th European Conference on Computer Systems, EuroSys 2020, Association for Computing Machinery, Inc, 15th European Conference on Computer Systems, EuroSys 2020, Heraklion, Greece, 4/27/20. https://doi.org/10.1145/3342195.3387522

@inproceedings{5e5ac27c46684d4e9986399e1a8172ad,

title = "Can far memory improve job throughput?",

abstract = "As memory requirements grow, and advances in memory technology slow, the availability of sufficient main memory is increasingly the bottleneck in large compute clusters. One solution to this is memory disaggregation, where jobs can remotely access memory on other servers, or far memory. This paper first presents faster swapping mechanisms and a far memory-Aware cluster scheduler that make it possible to support far memory at rack scale. Then, it examines the conditions under which this use of far memory can increase job throughput. We find that while far memory is not a panacea, for memory-intensive workloads it can provide performance improvements on the order of 10% or more even without changing the total amount of memory available.",

author = "Emmanuel Amaro and Christopher Branner-Augmon and Zhihong Luo and Amy Ousterhout and Aguilera, {Marcos K.} and Aurojit Panda and Sylvia Ratnasamy and Scott Shenker",

note = "Publisher Copyright: {\textcopyright} 2020 ACM.; 15th European Conference on Computer Systems, EuroSys 2020 ; Conference date: 27-04-2020 Through 30-04-2020",

year = "2020",

month = apr,

day = "15",

doi = "10.1145/3342195.3387522",

language = "English (US)",

series = "Proceedings of the 15th European Conference on Computer Systems, EuroSys 2020",

publisher = "Association for Computing Machinery, Inc",

booktitle = "Proceedings of the 15th European Conference on Computer Systems, EuroSys 2020",