Memory Locality Optimisations for Graph Processing

Speaker: Hans Vandierendonck , Queen's University Belfast

Date: Wednesday, June 01, 2022

Time: 4:00 PM to 5:00 PM Note: all times are in the Eastern Time Zone

Public: Yes

Location: https://mit.zoom.us/meeting/register/tJMtdOqgrTwsGNV_k0Nk6JjLMk-G6GFzTRyk

Event Type: Seminar

Room Description: https://mit.zoom.us/meeting/register/tJMtdOqgrTwsGNV_k0Nk6JjLMk-G6GFzTRyk

Host: Julian Shun, MIT CSAIL

Contact: Julian Shun, jshun@mit.edu

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

Speaker URL: https://pure.qub.ac.uk/en/persons/hans-vandierendonck

Speaker Photo:
None

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

Reminder Subject: TALK: Memory Locality Optimisations for Graph Processing

******************IMPORTANT NOTE ABOUT REGISTRATION******************
- The registration link for Spring/Summer 2022 is the same as the link from Fall 2021.
- Please save the Zoom link that you receive after you register. This link will stay the same for all subsequent Fast Code seminars.
- Zoom does not recognize a second registration, and will not send out the link a second time. The organizers will not be notified of any second registration.
- If you have any problems with registration, please contact lindalynch@csail.mit.edu by May 31, so that we can try to resolve it before the seminar begins.
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Abstract:
Graph analytics present a challenging workload for modern computer architectures due to the size of data sets, unpredictable and randomly-looking access patterns and limited opportunities for data reuse in on-chip caches. This talk discusses three application-specific methods for improving the memory locality of graph processing applications. While the three techniques are fundamentally different, they all share the common goal of aiming to reduce the range of data accessed through random access patterns. Hub temporal locality aims to control random access patterns associated to hub vertices, enabling the majority of random access stores to hit in the on-chip CPU caches. Narrow vertex properties enable the cache to store more vertex properties, thereby making the cache appear larger. Finally, LOTUS improves locality by partitioning the graph adjacency matrix and adjusting the storage format for distinct components in an application-aware manner. These results demonstrate the performance potential, and need for, bespoke memory locality optimisations.

Bio:
Hans Vandierendonck is Professor of High-Performance and Data-Intensive Computing in the school of Electronics, Electrical Engineering and Computer Science, at Queen's University Belfast. He is Director of the Centre for Data Science and Scalable Computing (DSSC) in the Institute for Electronics, Communications and Information Technology. His research interests are in compilers, runtime systems and architectures for parallel systems with special attention to the programmability of such systems. Hans also has a vested interested in computer architecture, and particularly in cache architecture, prediction and performance evaluation. He has co-authored over a 100 papers and has supervised PhD dissertations of 4 students, one of whom, Dr Jiawen Sun, is finalist in the EPSRC Connected Nation Pioneers competition. Hans received the IBM Belgium Prize for Computer Science in 2000 for his graduation thesis and in 2004 for his PhD dissertation. He is a Senior Member of IEEE and ACM.

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

Impact Areas:
Big Data

This event is not part of a series.

Created by Julian J. Shun at Tuesday, May 24, 2022 at 10:58 PM.