Emmanuel Amaro

EECS Department, University of California, Berkeley

Technical Report No. UCB/EECS-2021-268

December 23, 2021

Datacenter efficiency has become increasingly relevant, as the end of Moore's Law and Dennard scaling have caused CPU and memory performance to begin plateauing. Resource disaggregation is a recent datacenter design point, where server nodes share remote resources through a fast (usually RDMA-based) network, enabling greater execution flexibility and performance in datacenters. Remote or far memory--an instance of resource disaggregation--increases flexibility because nodes can access more memory than locally available. And performance in distributed applications can improve as RDMA provides high-performance access to shared state. This dissertation describes two networked systems that allow server nodes in a data center to leverage far memory.

First, WICkit is a framework and runtime for Where-Independent Code. WICs are a location-independent abstraction representing complex remote memory accesses, e.g., accessing a value in a hashmap. Without code changes, the WICkit runtime can execute WICs at the client, server, and SmartNIC CPU locations. As different locations provide different performance and resource trade-offs, WICkit allows users to flexibly choose the location when execution begins while obtaining comparable performance to location-specific systems.

Second, Cluster Far Memory is a system that transparently allows existing jobs to access far memory. CFM includes a fast swapping mechanism and a far memory-aware job scheduler that enable far memory support at rack scale. Using CFM for memory-intensive workloads, a rack can improve its throughput on the order of 10% or more without increasing the total amount of memory in it.

Advisors: Scott Shenker

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BibTeX citation:

@phdthesis{Amaro:EECS-2021-268,
    Author= {Amaro, Emmanuel},
    Title= {Systems for Using Far Memory in Datacenters},
    School= {EECS Department, University of California, Berkeley},
    Year= {2021},
    Month= {Dec},
    Url= {http://www2.eecs.berkeley.edu/Pubs/TechRpts/2021/EECS-2021-268.html},
    Number= {UCB/EECS-2021-268},
    Abstract= {Datacenter efficiency has become increasingly relevant, as the end of Moore's Law and Dennard scaling have caused CPU and memory performance to begin plateauing. Resource disaggregation is a recent datacenter design point, where server nodes share remote resources through a fast (usually RDMA-based) network, enabling greater execution flexibility and performance in datacenters. Remote or far memory--an instance of resource disaggregation--increases flexibility because nodes can access more memory than locally available. And performance in distributed applications can improve as RDMA provides high-performance access to shared state. This dissertation describes two networked systems that allow server nodes in a data center to leverage far memory. 

First, WICkit is a framework and runtime for Where-Independent Code. WICs are a location-independent abstraction representing complex remote memory accesses, e.g., accessing a value in a hashmap. Without code changes, the WICkit runtime can execute WICs at the client, server, and SmartNIC CPU locations. As different locations provide different performance and resource trade-offs, WICkit allows users to flexibly choose the location when execution begins while obtaining comparable performance to location-specific systems.

Second, Cluster Far Memory is a system that transparently allows existing jobs to access far memory. CFM includes a fast swapping mechanism and a far memory-aware job scheduler that enable far memory support at rack scale. Using CFM for memory-intensive workloads, a rack can improve its throughput on the order of 10% or more without increasing the total amount of memory in it.},
}

EndNote citation:

%0 Thesis
%A Amaro, Emmanuel 
%T Systems for Using Far Memory in Datacenters
%I EECS Department, University of California, Berkeley
%D 2021
%8 December 23
%@ UCB/EECS-2021-268
%U http://www2.eecs.berkeley.edu/Pubs/TechRpts/2021/EECS-2021-268.html
%F Amaro:EECS-2021-268