Kernels for scalable data analysis in science: Towards an architecture-portable future
| Autor: | Sreenivas R. Sukumar, Ramakrishnan Kannan, Seung-Hwan Lim, Michael A. Matheson |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
0301 basic medicine
Theoretical computer science business.industry Computer science Distributed computing Big data 02 engineering and technology Modular design 021001 nanoscience & nanotechnology Supercomputer Matrix decomposition 03 medical and health sciences 030104 developmental biology Workflow Scalability Algorithm design 0210 nano-technology business Agile software development |
| Zdroj: | IEEE BigData |
| DOI: | 10.1109/bigdata.2016.7840703 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|