Julian Shun

Julian Shun

Ph.D. student
School of Computer Science
Carnegie Mellon University
Email ID: jshun     Domain: cs.cmu.edu

I am a Ph.D. student in the Computer Science Department at Carnegie Mellon University. I am fortunate to be advised by Guy Blelloch. I am also fortunate to be supported by a Facebook Graduate Fellowship for 2013-2014. I obtained my bachelor's degree in Computer Science from UC Berkeley.

Research Interests

I am interested in all aspects of parallel computing, especially parallel graph processing frameworks, algorithms, data structures and tools for deterministic parallel programming. Below is a description of my recent projects. (For certain papers, the authors are listed alphabetically, following the convention in mathematics and theoretical computer science, and others are listed by contribution.)

Large-scale Graph Processing

I am very interested in developing algorithms for large-scale graph processing. Graph algorithms have many applications, ranging from analyzing social networks to finding patterns in biological networks. I have developed Ligra, a lightweight graph processing framework for shared memory. The project was motivated by the fact that the largest publicly available real-world graphs all fit in shared memory. When graphs fit in shared-memory, processing them using Ligra can give performance improvements of up to orders of magnitude compared to distributed-memory graph processing systems. As an extension, I am currently working on supporting graph compression techniques in Ligra, so that it can support large graphs with less memory. I have also developed algorithms with strong theoretical guarantees for many fundamental graph algorithms, such as connected components, minimum spanning forest, maximal independent set and maximal matching. I have implemented these algorithms for the shared memory multicore setting and also for external memory.

Parallel Text Algorithms and Data Structures

I have developed algorithms for several important algorithms and data structures in text processing. These have important applications in bioinformatics, data compression, information retrieval among many others. The algorithms are the state-of-the-art for shared memory multicore machines.

Problem Based Benchmark Suite (PBBS)

I have developed a benchmark suite of fundamental problems along with parallel algorithms for solving them. The benchmarks are solely defined by the problem specification and input/output formats, without any reference to the algorithm, programming language or machine used. Please contribute your own implementations to the benchmark suite by sending them to me! The following papers contain descriptions of the benchmarks and experiments using them:

Deterministic Parallel Programming

I am interested in developing tools that many it easier for others to do parallel programming. In particular, I have developed algorithms, data structures and tools for deterministic parallel programming. Determinism is very important in parallel programming as it allows for ease of debugging, reasoning about correctness and performance.