[CSEE Talk] CHMPR Distinguished Lecture Series: Parallel Real-Time OLAP on Multi-Core Processors, Thurs 12/6

[Anissa Elmerraji]

Center for Hybrid Multicore Productivity Research (CHMPR)
Distinguished Computational Science Lecture Series

Parallel Real-Time OLAP on Multi-Core Processors

Frank Dehne
Chancellor's Professor of Computer Science
Carleton University, Ottawa, Canada
http://www.dehne.net

3:00 p.m. Thursday, 6 December 2012, ITE 456, UMBC


One of the most powerful and prominent technologies for knowledge
discovery in Decision Support systems is On-line Analytical Processing
(OLAP). Most of the traditional OLAP research, and most of the
commercial systems, follow the static data cube approach proposed by
Gray etal. and materialize all or a subset of the cuboids of the data
cube in order to ensure adequate query performance. Practitioners have
called for some time for a real-time OLAP approach where the OLAP
system gets updated instantaneously as new data arrives and always
provides an up-to-date data warehouse for the decision support
process. However, major problems for real-time OLAP are significant
performance issues with large scale data warehouses. The aim of our
research is to address these problems through the use of efficient
parallel computing methods. We present a parallel real-time OLAP
system for multi-core processors. To our knowledge, this is the first
real-time OLAP system that has been parallelized and optimized for
contemporary multi-core processors, providing the opportunity for
real-time OLAP on large scale data warehouses. Our system allows for
multiple insert and multiple query operations (transactions) to be
executed in parallel and in real-time. We evaluated our method for a
multitude of scenarios (different ratios of insert and query
transactions, query transactions with different sizes of results,
different system loads, etc.), using the TPC-DS “Decision Support”
benchmark data set. The tests demonstrate that our parallel system
achieves a significant speedup in transaction response time and a
significant increase in transaction throughput. Since hardware
performance improvements are currently achieved not by faster
processors but by increasing the number of processor cores, our new
parallel real-time OLAP method has the potential to enable OLAP
systems that are real-time and efficient/feasible for large databases.

There will be a tea at 2:30 p.m. preceding the lecture.

The CHMPR Holiday Party will follow the lecture at 4:30 p.m. Please
RSVP to Valerie L Thomas (valeriet at umbc.edu).