Clemson University TigerPrints All Dissertations Dissertations December 2016 Accelerating Big Data Analytics on Traditional High-Performance Computing Systems Using Two-Level Storage Pengfei Xuan Clemson University,
[email protected] Follow this and additional works at: https://tigerprints.clemson.edu/all_dissertations Recommended Citation Xuan, Pengfei, "Accelerating Big Data Analytics on Traditional High-Performance Computing Systems Using Two-Level Storage" (2016). All Dissertations. 2318. https://tigerprints.clemson.edu/all_dissertations/2318 This Dissertation is brought to you for free and open access by the Dissertations at TigerPrints. It has been accepted for inclusion in All Dissertations by an authorized administrator of TigerPrints. For more information, please contact
[email protected]. ACCELERATING BIG DATA ANALYTICS ON TRADITIONAL HIGH-PERFORMANCE COMPUTING SYSTEMS USING TWO-LEVEL STORAGE A Dissertation Presented to the Graduate School of Clemson University In Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy Computer Science by Pengfei Xuan December 2016 Accepted by: Dr. Feng Luo, Committee Chair Dr. Pradip Srimani Dr. Rong Ge Dr. Jim Martin Abstract High-performance Computing (HPC) clusters, which consist of a large number of compute nodes, have traditionally been widely employed in industry and academia to run diverse compute-intensive applications. In recent years, the revolution in data-driven science results in large volumes of data, often size in terabytes or petabytes, and makes data-intensive applications getting exponential growth. The data-intensive computing presents new challenges to HPC clusters due to the different workload characteristics and optimization objectives. One of those challenges is how to efficiently integrate software frameworks developed for big data analytics, such as Hadoop and Spark, with traditional HPC systems to support both data-intensive and compute-intensive workloads.