The size and scope of cutting-edge scientific simulations are growing much faster than the I/O and storage capabilities of their run-time environments. The growing gap is exacerbated by exploratory, data-intensive analytics, such as querying simulation data with multivariate, spatio-temporal constraints, which induces heterogeneous access patterns that stress the performance of the underlying storage system. Previous work addresses data layout and indexing techniques to improve query performance for a single access pattern, which is not sufficient for complex analytics jobs.
We present PARLO, a parallel run-time layout optimization framework, to achieve multi-level data layout optimization for scientific applications at run-time before data is written to storage. The layout schemes optimize for heterogeneous access patterns with user-specified priorities. PARLO is integrated with ADIOS, a high-performance parallel I/O middleware for large-scale HPC applications, to achieve user-transparent, light-weight layout optimization for scientific datasets. It offers simple XML-based configuration for users to achieve flexible layout optimization without the need to modify or recompile application codes. Experiments show that PARLO improves performance by 2 to 26 times for queries with heterogeneous access patterns compared to state-of-the-art scientific database management systems. Compared to traditional post-processing approaches, its underlying run-time layout optimization achieves a 56% savings in processing time and a reduction in storage overhead of up to 50%. PARLO also exhibits a low run-time resource requirement, while also limiting the performance impact on running applications to a reasonable level.
AuthorsZ. Gong, D. Boyuka, X. Zou, Q. Liu, N. Podhorszki, S. Klasky, N. F. Samatova.
- Z. Gong, D. Boyuka, X. Zou, Q. Liu, N. Podhorszki, S. Klasky, N. F. Samatova,
"PARLO: PArallel Run-time Layout Optimization for Scientific Data Explorations with Heterogeneous Access Patterns",
IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, Delft, The Netherlands, May, 2013. [pdf]
ContactDr.Nagiza Samatova (firstname.lastname@example.org)
PARLO code Alpha release. [parlo-0.1.0.tar.gz]