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.