Kernels for scalable data analysis in science: Towards an architecture-portable future

Journal Article · Thu Dec 01 04:00:00 EST 2016

DOI:https://doi.org/10.1109/BigData.2016.7840703· OSTI ID:1567560

Sukumar, Sreenivas R.; Kannan, Ramakrishnan; Lim, Seung-Hwan; Matheson, Michael A.

In this paper, we pose and address some of the unique challenges in the analysis of scientific Big Data on supercomputing platforms. Our approach identifies, implements and scales numerical kernels that are critical to the instantiation of theory-inspired analytic workflows on modern computing architectures. We present the benefits of scalable kernels towards constructing algorithms such as principal component analysis and non-negative matrix factorization on an image-analysis use case at the Oak Ridge Leadership Computing Facility (OLCF). Based on experience with the use-case, we conclude that piecing scalable analytic kernels into user-defined analytic workflows are a flexible, modular and agile way to enable architecture-portable productivity for the data-intensive sciences.

• Research Organization: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). Oak Ridge Leadership Computing Facility (OLCF)
• Sponsoring Organization: USDOE Office of Science; USDOE
• OSTI ID: 1567560
• Country of Publication: United States
• Language: English