

Julian Shun
Email: jshun at cs.cmu.edu
C.V.


I will start as a
Miller Research Fellow at UC Berkeley in Fall 2015.
I obtained my Ph.D. from
the
Computer
Science Department at Carnegie Mellon University, where I was fortunate
to be advised by
Guy Blelloch. I was also fortunate to be supported
by
a
Facebook Graduate Fellowship for 20132014. My Ph.D. thesis is available
here. 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.)
Largescale Graph Processing
I am very interested in
developing algorithms for largescale 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 realworld graphs all fit
in shared memory. When graphs fit in sharedmemory, processing them
using Ligra can give performance improvements of up to orders of
magnitude compared to distributedmemory graph processing systems. I
have also developed Ligra+, an extension of Ligra that uses graph
compression techniques to process large graphs with less
memory. Recently, I have used Ligra/Ligra+ to implement and evaluate
various parallel algorithms for graph eccentricity estimation.
I have also developed practical algorithms with strong theoretical
guarantees for many fundamental graph algorithms, such as connected
components, minimum spanning forest, triangle computations, maximal
independent set and maximal matching. I have implemented these
algorithms for the shared memory multicore setting and also for
external memory.
 (alphabetical order)
Niklas Baumstark, Guy Blelloch and Julian Shun
Efficient Implementation of a Synchronous Parallel PushRelabel Algorithm
To appear in the Proceedings of the European Symposium on Algorithms (ESA), 2015.
 Julian Shun and Kanat Tangwongsan
Multicore Triangle Computations Without Tuning
Proceedings of the IEEE International Conference on Data Engineering (ICDE), pp. 149160, 2015.
Source code
 Julian Shun, Laxman Dhulipala and Guy Blelloch
A Simple
and Practical LinearWork Parallel Algorithm for Connectivity
Proceedings of the ACM Symposium on Parallelism in Algorithms and
Architectures (SPAA), pp. 143153, 2014.
Source code
 Aapo Kyrola, Julian Shun and Guy Blelloch
Beyond
Synchronous: New Techniques for External Memory Graph
Algorithms
Proceedings of the Symposium on Experimental Algorithms (SEA),
pp. 123137, 2014.
Parallel String/Text Algorithms and Data Structures
I have
developed practical parallel algorithms with theoretical guarantees
for several important algorithms and data structures in string/text
processing. These have important applications in bioinformatics, data
compression, information retrieval among many others.
 Julian Shun
Parallel Wavelet Tree Construction
Proceedings of the IEEE Data Compression Conference (DCC), pp. 6372, 2015.
Awarded the Capocelli Prize for Best StudentAuthored Paper
Source code
 Julian Shun
Fast Parallel
Computation of Longest Common Prefixes
Proceedings of the ACM/IEEE International Conference for High
Performance Computing, Networking, Storage and Analysis (SC),
pp. 387398, 2014.
 Julian Shun and Guy Blelloch
A Simple Parallel
Cartesian Tree Algorithm and its Application to Parallel Suffix Tree
Construction
ACM Transactions on Parallel Computing (TOPC), Vol. 1 Issue 1,
Article No. 8, 2014. (Earlier version appears in ALENEX 2011.)
Source code
 Julian Shun and Fuyao Zhao (joint first author)
Practical Parallel
LempelZiv Factorization
Proceedings of the IEEE Data Compression Conference (DCC),
pp. 123132, 2013.
Source code
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.
 Julian Shun, Yan Gu, Guy Blelloch, Jeremy Fineman and Phillip Gibbons
Sequential Random
Permutation, List Contraction and Tree Contraction are Highly
Parallel
Proceedings of the ACMSIAM Symposium on Discrete
Algorithms (SODA), pp. 431448, 2015.
 Julian Shun and Guy Blelloch
Phaseconcurrent Hash
Tables for Determinism
Proceedings of the ACM Symposium on Parallelism in Algorithms and
Architectures (SPAA), pp. 96107, 2014.
 Julian Shun, Guy Blelloch, Jeremy Fineman and
Phillip Gibbons
Reducing Contention
Through Priority Updates
Proceedings of the ACM Symposium on Parallelism in Algorithms and
Architectures (SPAA), pp. 152163, 2013.
 (alphabetical order) Guy Blelloch, Jeremy Fineman and Julian
Shun
Greedy Sequential Maximal
Independent Set and Matching are Parallel on Average
Proceedings of the ACM Symposium on Parallelism in Algorithms and
Architectures (SPAA), pp. 308317, 2012.
 (alphabetical order) Guy Blelloch, Jeremy Fineman,
Phillip Gibbons and Julian Shun
Internally
Deterministic Parallel Algorithms Can Be Fast
Proceedings of the ACM SIGPLAN Symposium on Principles and
Practice of Parallel Programming (PPoPP), pp. 181192, 2012.
Website
Sorting and Related Problems
I am interested in designing
efficient algorithms for sorting and semisorting (where equalvalued
keys are contiguous but different keys are not necessarily in sorted
order), which are fundamental problems in computing. I have designed
efficient sorting algorithms for memories with asymmetric costs of
reading and writing, as well as a practical and
theoreticallyefficient parallel algorithm for semisorting.
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! The following paper
contains descriptions of the benchmarks and experiments using them: