Scalable and Power Efficient Data Analytics for Hybrid Exascale Systems

[摘要] 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.

[发布日期] 2015-03-19

[学科分类] 数学（综合）

[关键词] Exascale;Scalability;Power efficiency;Data analytics;Heterogeneous architectures