Thaleia Doudali

Title: Adding Machine Learning to the Management of Heterogeneous Resources

Abstract: Computing platforms increasingly incorporate heterogeneous hardware technologies, as a way
to scale application performance, resource capacities and achieve cost effectiveness. However,
this heterogeneity, along with the greater irregularity in the behavior of emerging workloads,
render existing resource management approaches ineffective. This results in a significant gap
between the realized vs. achievable performance and efficiency. My research develops a
practical approach for using machine learning to bridge this gap, specifically targeting systems
with heterogeneous memory technologies. In this talk, I will answer the key challenges into
realizing this approach. These include which machine learning (ML) method to use, which part
of the memory management stack to target, and how to configure its deployment. I will present
new techniques for integrating machine learning (ML) in existing system-level management of
hybrid memory hardware, which, on average, bridge 80% of the performance gap left by
existing solutions.`

Bio: Thaleia Dimitra Doudali is a Ph.D. candidate in the School of Computer Science at Georgia
Tech, advised by Ada Gavrilovska. Thaleia’s research interests are broadly in the area of
systems, with a focus at the intersection of machine learning and systems. Her dissertation
research adds machine learning to the resource management of heterogeneous memory
hardware and part of it has been recognized as a best paper award finalist at HPDC ‘19. Thaleia
was selected to participate in the prestigious 2020 Rising Stars in EECS workshop.