[an error occurred while processing this directive] [an error occurred while processing this directive] [an error occurred while processing this directive] [an error occurred while processing this directive] PhD Candidate [an error occurred while processing this directive] Georgia Institute of Technology [an error occurred while processing this directive] [an error occurred while processing this directive] [an error occurred while processing this directive] [an error occurred while processing this directive]

Artificial Intelligence

Computer Architecture and Engineering

Operating Systems and Networking

[an error occurred while processing this directive] Machine Intelligent and Timely Data Management for Hybrid Memory Systems [an error occurred while processing this directive] Big data analytics in datacenter platforms and data intensive simulations in exascale computing environments create the need for massive main memory capacities, on the order of terabytes, to boost application performance. To satisfy these requirements, memory hierarchies become more complex, incorporating emerging types of technologies or disaggregation techniques to offset the skyrocketing cost that DRAM-only systems would impose. As we shift away from traditional memory hierarchies, the effectiveness of existing data management solutions decreases, as these have not provisioned against the even bigger disparity in the access speeds of the heterogeneous components that are now part of the memory subsystem. Additionally, system-level configuration knobs need to be re-tuned to adjust to the speeds of the newly introduced memory hardware. In the face of this complexity, conventional approaches to designing data management solutions with empirically-derived configuration parameters become impractical. This makes the case for leveraging machine intelligence in building a new generation of data management solutions for hybrid memory systems. This thesis identifies the machine intelligent methods that can be effective for and practically integrated with system-level memory management, and demonstrates their importance through the design of new components of the memory management stack; from system-level support for configuring stack parameters to memory scheduling. [an error occurred while processing this directive] Thaleia Dimitra Doudali is a final year PhD student in Computer Science at Georgia Tech advised by Ada Gavrilovska. Her dissertation research contributes novel machine intelligent and timely data management for systems with hybrid memory components. Her collaboration with AMD Research led to a best paper award finalist at HPDC '19. Prior to Georgia Tech, she received an undergraduate diploma in Electrical and Computer Engineering at the National Technical University of Athens in Greece. [an error occurred while processing this directive] Personal home page [an error occurred while processing this directive] [an error occurred while processing this directive]