CS Faculty Candidate Talk: Dr. Ke Fan

Title: Optimizing Irregular Data Movement at Scale

Time: February 18, 2025 at 10:00 AM Eastern Time (US and Canada)

Location: M03-721. Refreshments will be served.

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Abstract: Rapid advancements in computing technologies, especially the broad adoption of heterogeneous computing and the imminent arrival of Exascale machines, are pushing the frontiers of computational sciences in terms of both the scale and the complexity of problems that can be studied. However, these growing possibilities also pose the critical challenge of optimizing computational resources to efficiently manage data movement, particularly sparse and irregular patterns associated with unbalanced workloads. Many modern high-performance computing (HPC) applications, such as parallel machine learning (ML) and graph mining, exhibit some degree of sparsity and irregularity characterized by unpredictable memory access patterns, complex network communication behaviors, and imbalanced workloads or I/O per process. These characteristics present significant scalability challenges for large-scale systems. To mitigate these challenges and maximize resource utilization and performance, I focus on two primary areas: (1) collective communication, focusing on optimizing data exchange among processes with non-uniform, sparse data distributions over networks, and (2) parallel file I/O, addressing unbalanced data exchange effectively between compute nodes and parallel file systems. In addition, while dealing with high degrees of parallelism, performance analysis of irregular HPC applications becomes increasingly crucial. The scalable performance analysis frameworks for characterizing the behavior of unstructured large-scale applications can offer valuable insights into data movement patterns that inform further optimizations.

Bio: Ke Fan is currently a Ph.D. candidate in Computer Science at the University of Illinois Chicago under the mentorship of Dr. Sidharth Kumar. Her research lies in the area of high-performance computing (HPC), with a particular emphasis on three key areas: optimizing the performance of MPI collectives, enhancing the performance of irregular parallel I/O operations, and improving the scalability of performance introspection frameworks. Throughout her doctoral journey, she has made significant contributions to the field of HPC, which is reflected in her publications at top-tier HPC conferences such as HPDC, HiPC, and ISC. Her poster presentation was recognized as the Best Poster Finalist at Supercomputing (SC) 2023. Further cementing her impact in the field, she was awarded the esteemed 2024 ACM/IEEE-CS George Michael Memorial High-Performance Computing Fellowship, one of the most prestigious honors in H