Scalable and power efficient data analytics for hybrid exascale systems

Abstract

This project developed a generic and optimized set of core data analytics functions. These functions organically consolidate a broad constellation of high performance analytical pipelines. As the architectures of emerging HPC systems become inherently heterogeneous, there is a need to design algorithms for data analysis kernels accelerated on hybrid multi-node, multi-core HPC architectures comprised of a mix of CPUs, GPUs, and SSDs. Furthermore, the power-aware trend drives the advances in our performance-energy tradeoff analysis framework which enables our data analysis kernels algorithms and software to be parameterized so that users can choose the right power-performance optimizations.

Original document

The different versions of the original document can be found in:

• http://dx.doi.org/10.2172/1173060

• http://pdfs.semanticscholar.org/65b4/3bdd59af7f3239c8678cdfa644255d8a6372.pdf

• https://www.osti.gov/servlets/purl/1173060,

https://academic.microsoft.com/#/detail/2297568525