Fast graph analytics on heterogenous and deep-memory architectures

Speaker: Ümit V. Çatalyürek , Georgia Institute of Technology

Date: Monday, June 29, 2020

Time: 2:00 PM to 3:00 PM Note: all times are in the Eastern Time Zone

Public: Yes

Location: https://mit.zoom.us/meeting/register/tJUrdOqopj8uHdO4gUyVMnfglOFEqIye_Je0 (Registration required)

Event Type: Seminar

Room Description: https://mit.zoom.us/meeting/register/tJUrdOqopj8uHdO4gUyVMnfglOFEqIye_Je0 (Registration required)

Host: Julian Shun, MIT CSAIL

Contact: Julian Shun, jshun@mit.edu, lindalynch@csail.mit.edu

Relevant URL: http://fast-code.csail.mit.edu/

Speaker URL: https://www.cc.gatech.edu/~umit/

Speaker Photo:
None

Reminders to: seminars@csail.mit.edu, fast-code-seminar@lists.csail.mit.edu, pl@csail.mit.edu, commit@csail.mit.edu

Reminder Subject: TALK: Fast graph analytics on heterogenous and deep-memory architectures

Abstract:
In today's data-driven world and heterogeneous computing environments processing large-scale graphs, in an architecture agnostic manner has become more important than ever before. Therefore, there has been a significant interest in high-performance graph processing. On one side, many researchers developed optimized algorithms for specific graph analytics kernel for a specific architecture. On the other side, software systems have been designed to leverage modern high-performance computing platforms. Some of them provide a very productive programming environment for graph analysis, however, they cannot get even close to the single-threaded performance. The main thesis of this talk is that block-based graph algorithms offer a sweet spot between efficient parallelism and architecture agnostic algorithm design for a variety of graph problems. We demonstrate that with two graph analytics kernels: graph merging, and triangle count.

Bio:
Ümit V. Çatalyürek is currently a Professor and the Associate Chair of the School of Computational Science and Engineering in the College of Computing at the Georgia Institute of Technology. He received his Ph.D., M.S. and B.S. in Computer Engineering and Information Science from Bilkent University, Turkey, in 2000, 1994 and 1992, respectively. Dr. Çatalyürek is a Fellow of IEEE and SIAM. He was the elected Chair for IEEE TCPP for 2016-2019, and currently serves as Vice-Chair for ACM SIGBio for 2015-2021 terms. He also serves as the member of Board of Trustees of Bilkent University. Dr. Çatalyürek currently serves as the Editor-in-Chief for Parallel Computing. In the past, he also served on the editorial boards of the IEEE Transactions on Parallel and Distributed Computing, the SIAM Journal of Scientific Computing, Journal of Parallel and Distributed Computing, and Network Modeling and Analysis in Health Informatics and Bioinformatics. He also serves on the program committees and organizing committees of numerous international conferences. Dr. Çatalyürek is a recipient of an NSF CAREER award and is the primary investigator of several awards from the Department of Energy, the National Institute of Health, and the National Science Foundation. He has co-authored more than 200 peer-reviewed articles, invited book chapters and papers. His main research areas are in parallel computing, combinatorial scientific computing and biomedical informatics. More information about Dr. Çatalyürek and his research group can be found at http://cc.gatech.edu/~umit and http://tda.gatech.edu.

Research Areas:
Algorithms & Theory, Computer Architecture, Programming Languages & Software

Impact Areas:
Big Data

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Created by Julian J. Shun at Friday, June 26, 2020 at 11:15 AM.