Abstract: Parallelism is the key to achieving high performance in computing. However, writing efficient and scalable parallel programs is notoriously difficult, and often requires significant expertise. To address this challenge, it is crucial to provide programmers with high-level tools to enable them to develop solutions more easily, and at the same time emphasize the theoretical and practical aspects of algorithm design to allow the solutions developed to run efficiently under many possible settings. My research addresses this challenge using a three-pronged approach consisting of the design of shared-memory programming techniques, frameworks, and algorithms for important problems in computing. In this talk, I will present tools for deterministic parallel programming, large-scale shared-memory algorithms that are efficient both in theory and in practice, and Ligra, a framework for simplifying the programming of shared-memory graph algorithms.
Bio: Julian Shun is currently a Miller Research Fellow (post-doc) at UC Berkeley. He obtained his Ph.D. in Computer Science from Carnegie Mellon University, and his undergraduate degree in Computer Science from UC Berkeley. He is interested in developing large-scale parallel algorithms for graph processing, and parallel text algorithms and data structures. He is also interested in designing methods for writing deterministic parallel programs and benchmarking parallel programs. He has received the ACM Doctoral Dissertation Award, CMU School of Computer Science Doctoral Dissertation Award, Miller Research Fellowship, Facebook Graduate Fellowship, and a best student paper award at the Data Compression Conference.
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