Adaptive work placement for query processing on heterogeneous computing resources

The hardware landscape is currently changing from homogeneous multi-core systems towards heterogeneous systems with many di↵erent computing units, each with their own characteristics. This trend is a great opportunity for database systems to increase the overall performance if the heterogeneous resources can be utilized eciently. To achieve this, the main challenge is to place the right work on the right computing unit. Current approaches tackling this placement for query processing assume that data cardinalities of intermediate results can be correctly estimated. However, this assumption does not hold for complex queries. To overcome this problem, we propose an adaptive placement approach being independent of cardinality estimation of intermediate results. Our approach is incorporated in a novel adaptive placement sequence. Additionally, we implement our approach as an extensible virtualization layer, to demonstrate the broad applicability with multiple database systems. In our evaluation, we clearly show that our approach significantly improves OLAP query processing on heterogeneous hardware, while being adaptive enough to react to changing cardinalities of intermediate query results

Scalable frequent itemset mining on many-core processors

Frequent-itemset mining is an essential part of the association rule mining process, which has many application areas. It is a computation and memory intensive task with many opportunities for optimization. Many efficient sequential and parallel algorithms were proposed in the recent years. Most of the parallel algorithms, however, cannot cope with the huge number of threads that are provided by large multiprocessor or many-core systems. In this paper, we provide a highly parallel version of the well-known Eclat algorithm. It runs on both, multiprocessor systems and many-core coprocessors, and scales well up to a very large number of threads---244 in our experiments. To evaluate mcEclat's performance, we conducted many experiments on realistic datasets. mcEclat achieves high speedups of up to 11.5x and 100x on a 12-core multiprocessor system and a 61-core Xeon Phi many-core coprocessor, respectively. Furthermore, mcEclat is competitive with highly optimized existing frequent-itemset mining implementations taken from the FIMI repository

Improving in-memory database index performance with Intel® Transactional Synchronization Extensions

The increasing number of cores every generation poses challenges for high-performance in-memory database systems. While these systems use sophisticated high-level algorithms to partition a query or run multiple queries in parallel, they also utilize low-level synchronization mechanisms to synchronize access to internal database data structures. Developers often spend significant development and verification effort to improve concurrency in the presence of such synchronization. The Intel ® Transactional Synchronization Extensions (Intel ® TSX) in the 4th Generation Core™ Processors enable hardware to dynamically determine whether threads actually need to synchronize even in the presence of conservatively used synchronization. This paper evaluates the effectiveness of such hardware support in a commercial database. We focus on two index implementations: a B+Tree Index and the Delta Storage Index used in the SAP HANA ® database system. We demonstrate that such support can improve performance of database data structures such as index trees and presents a compelling opportunity for the development of simpler, scalable, and easy-to-verify algorithms