Automating heterogeneous memory management

Hardware heterogeneity is becoming an increasingly common feature in high-performance computing systems. Unfortunately, while these systems may offer new technologies in memory and computation, the general trend of memory performance is falling behind. With the added complexities of heterogeneous systems, achieving good memory performance is now becoming more difficult. Since different memory technologies exhibit various performance characteristics, careful memory management is required to consider tradeoffs in latency, bandwidth, capacity, and power. Application behavior, including data access patterns, data sizes, and operation types can indicate which characteristics limit the performance of an operation. However, understanding this information and using it to perform optimizations can be a difficult task. We expect this problem to become increasingly prevelent as the memory stack continues to change and expand. In this dissertation we present a solution to managing memory for these heterogeneous systems in an automated manner. We demonstrate its use on several applications and machine types, showcasing the benefit, flexibility, and expandability of the framework.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Computer Science

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2020

Author dc:creator

• Brooks, Alex

Contributors dc:contributor

• Snir, Marc
• Olson, Luke N
• Garzaran, Maria
• Scogland, Thomas R. W.

• heterogeneous
• memory
• system
• automated
• analysis
• profiling
• framework
• access
• patterns
• latency
• bandwidth

Statement dc:rights

• Copyright 2019 Alex Brooks

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/106362

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/106362