Accelerating Big Data Processing with Hadoop, Spark and Memcached on Datacenters with Modern Architectures

A Tutorial to be presented at The 22nd ACM International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS-2017)
Dhabaleswar K. (DK) Panda and Xiaoyi Lu (The Ohio State University)

When: April 8, 2017 (1:30pm-5:00pm)
Where: Xi'an, China


Apache Hadoop and Spark are gaining prominence in handling Big Data and analytics. Similarly, Memcached in Web 2.0 environment is becoming important for large-scale query processing. These middleware are traditionally written with sockets and do not deliver best performance on datacenters with modern high performance networks. In this tutorial, we will provide an in-depth overview of the architecture of Hadoop components (HDFS, MapReduce, RPC, HBase, etc.), Spark and Memcached. We will examine the challenges in re-designing the networking and I/O components of these middleware with modern interconnects, protocols (such as InfiniBand, iWARP, RoCE, and RSocket) with RDMA and storage architecture. Using the publicly available software packages in the High-Performance Big Data (HiBD, project, we will provide case studies of the new designs for several Hadoop/Spark/Memcached components and their associated benefits. Through these case studies, we will also examine the interplay between high performance interconnects, storage systems (NVM, SSD, HDD, and Parallel Filesystem), and multi-core platforms to achieve the best solutions for these components.

Targeted Audience and Scope

The tutorial content is planned for half-a-day. This tutorial is targeted for various categories of people working in the areas of Big Data including high-performance Hadoop/Spark/Memcached, high performance communication and I/O architecture, storage, networking, middleware, cloud computing and applications. Specific audience this tutorial is aimed at include: The content level will be as follows: 30% beginner, 40% intermediate, and 30% advanced. There is no fixed pre-requisite. As long as the attendee has a general knowledge in Big Data, Hadoop, Spark, Memcached, high performance computing, networking and storage architecture, and related issues, he/she will be able to understand and appreciate it. The tutorial is designed in such a way that an attendee gets exposed to the topics in a smooth and progressive manner. This tutorial is organized as a coherent talk to cover multiple topics.

Outline of the Tutorial

Brief Biography of Speakers

Dr. Dhabaleswar K. (DK) Panda is a Professor of Computer Science at the Ohio State University. He obtained his Ph.D. in computer engineering from the University of Southern California. His research interests include parallel computer architecture, high performance computing, communication protocols, files systems, network-based computing, and Quality of Service. He has published over 400 papers in major journals and international conferences related to these research areas. Dr. Panda and his research group members have been doing extensive research on modern networking technologies including InfiniBand, HSE and RDMA over Converged Enhanced Ethernet (RoCE). His research group is currently collaborating with National Laboratories and leading InfiniBand and 10GigE/iWARP companies on designing various subsystems of next generation high-end systems. The MVAPICH2 (High Performance MPI over InfiniBand, iWARP and RoCE) open-source software package, developed by his research group, are currently being used by more than 2,700 organizations worldwide (in 83 countries). This software has enabled several InfiniBand clusters (including the 1st one) to get into the latest TOP500 ranking. More than 405,000 downloads of these libraries have taken place from the project's site. These software packages are also available with the stacks for network vendors (InfiniBand and iWARP), server vendors and Linux distributors. The RDMA-enabled Apache Hadoop, Spark and Memcached packages, consisting of acceleration for HDFS, MapReduce, RPC, Spark and Memcached, are publicly available from High-Performance Big Data (HiBD) project site: These packages are currently being used by more than 200 organizations in 27 countries. More than 18,900 downloads have taken place from the project's site. Dr. Panda's research is supported by funding from US National Science Foundation, US Department of Energy, and several industry including Intel, Cisco, SUN, Mellanox, QLogic, NVIDIA and NetApp. He is an IEEE Fellow and a member of ACM. More details about Dr. Panda, including a comprehensive CV and publications are available here.

Dr. Xiaoyi Lu is a Research Scientist of the Department of Computer Science and Engineering at the Ohio State University, USA. His current research interests include high performance interconnects and protocols, Big Data, Hadoop/Spark/Memcached Ecosystem, Parallel Computing Models (MPI/PGAS), Virtualization and Cloud Computing. He has published over 70 papers in international journals and conferences related to these research areas. He has been actively involved in various professional activities (PC Co-Chair, PC Member, Reviewer, Session Chair) in academic journals and conferences. Recently, Dr. Lu is leading the research and development of RDMA-based accelerations for Apache Hadoop, Spark, HBase, and Memcached, and OSU HiBD micro-benchmarks, which are publicly available from ( These libraries are currently being used by more than 200 organizations from 27 countries. More than 18,900 downloads of these libraries have taken place from the project site. He is a core member of the MVAPICH2 (High Performance MPI over InfiniBand, iWARP and RoCE) project and he is leading the research and development of MVAPICH2-Virt (high-performance and scalable MPI for hypervisor and container based HPC cloud). He is a member of IEEE and ACM. More details about Dr. Lu are available at here.
Last Updated: January 12, 2017