Julian Shun: Shared-Memory Parallelism Can Be Simple, Fast, and Scalable

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 efficiently, and at the same time emphasize the theoretical and practical aspects of algorithm design to allow the solutions developed to run efficiently under all 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 a post-doc at UC Berkeley, supported by a Miller Research Fellowship. He obtained a Ph.D. in Computer Science from Carnegie Mellon University. He is interested in developing tools for large-scale shared-memory graph processing, as well as designing parallel algorithms that are efficient in theory and practice. He is also interested in developing methods for writing deterministic shared-memory programs, benchmarking parallel programs, and designing external-memory and cache-efficient algorithms. Julian's research spans both theory and practice. His work has been supported by a Facebook Graduate Fellowship. Julian obtained his undergraduate degree in Computer Science from UC Berkeley.