5200 N Lake Rd, Merced, CA 95343

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Electrical Engineering and Computer Science (EECS)

Ph.D. Dissertation Defense


"Memory Management for Big Memory Systems"

Jie Ren

Electrical Engineering and Computer Science

University of California, Merced


Abstract: The memory system has been evolving at a fast pace recently, driven by the emergence of largescale applications and the advance of hardware technology. This trend calls for the birth of memory systems with extreme heterogeneity, which combines multiple memory technologies with different latency, bandwidth, and capacity to construct main memory. The heterogeneity of memory systems brings a substantial disparity in the performance and efficiency, making the decision of which technology to use at what times intricate. In this dissertation, I will introduce our work on memory management for big memory systems. First, I will show how to train multibillion parameter models with limited GPU recourses by exploring the usage of heterogeneous memory. Then, I will show how to enable large-scale plasma simulations on a single machine at unprecedented scales with memory heterogeneity. Lastly, I will share my view on memory management for future generation heterogeneous computing and how they handle emerging workloads more efficiently.


Biography: Jie Ren is a Ph.D. candidate working with Professor Dong Li at the Electrical Engineering and Computer Science, University of California, Merced. She received her B.Sc. from Beijing Institute of Technology. Jie’s research focuses on developing practical techniques to solve memory management issues in parallel computing systems, particularly in developing runtime support on heterogeneous memory systems.


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