The 6th IEEE International Workshop on High-Performance Big Data and Cloud Computing (HPBDC)
In conjunction with the 34th IEEE International Parallel and Distributed Processing Symposium (IPDPS 2020)
New Orleans, Louisiana, USA
Monday, May 18th, 2020
Managing and processing large volumes of data, or Big Data, and gaining meaningful insights is a significant challenge facing the parallel and distributed computing community. This has significant impact in a wide range of domains including health care, bio-medical research, Internet search, finance and business informatics, and scientific computing. As data-gathering technologies and data sources witness an explosion in the amount of input data, it is expected that in the future massive quantities of data in the order of hundreds or thousands of petabytes will need to be processed. Thus, it is critical that data-intensive computing middleware (such as Hadoop, Spark, Flink, etc.) to process such data are diligently designed, with high performance and scalability, in order to meet the growing demands of such Big Data applications.
The explosive growth of Big Data has caused many industrial firms to adopt High Performance Computing (HPC) technologies to meet the requirements of huge amount of data to be processed and stored. The convergence of HPC, Big Data, and Deep Learning is becoming the next game-changing business opportunity. Apache Hadoop, Spark, gRPC/TensorFlow, and Memcached are becoming standard building blocks in handling Big Data oriented processing and mining.
Modern HPC bare-metal systems and Cloud Computing platforms have been fueled with the advances in multi-/many-core architectures, RDMA-enabled networking, NVRAMs, and NVMe-SSDs during the last decade. However, Big Data and Deep Learning middleware (such as Hadoop, Spark, Flink, and gRPC) have not embraced such technologies fully. These disparities are taking HPC, Big Data, and Deep Learning into divergent trajectories.
International Workshop on High-Performance Big Data, Deep Learning, and Cloud Computing (HPBDC), aims to bring HPC, Big Data processing, Deep Learning, and Cloud Computing into a convergent trajectory. The workshop provides a forum for scientists and engineers in academia and industry to present their latest research findings in major and emerging topics for ‘HPC + Big Data + Deep Learning over HPC Clusters and Clouds’.
HPBDC 2020 will be held in conjunction with the 34th IEEE International Parallel and Distributed Processing Symposium (IPDPS 2020).
HPBDC 2020 welcomes original submissions in a range of areas, including but not limited to:
* High-Performance Big Data analytics, Deep Learning, and Cloud Computing frameworks, programming models, and tools
* Performance optimizations for Big Data, Deep Learning, and Cloud Computing systems and applications with HPC technologies
* High-Performance in-memory computing technologies and abstractions
* Performance modeling and evaluation for emerging Big Data processing, Deep Learning, and Coud Computing technologies
* Big Data processing and Deep Learning on HPC, Cloud, and Grid computing infrastructures
* Fault tolerance, reliability, and availability for high-performance Big Data computing, Deep Learning, and Cloud Computing
* Green Big Data computing, Deep Learning, and HPC Clouds
* Scheduling and provisioning data analytics on HPC and Cloud infrastructures
* Scientific computing with Big Data and Deep Learning on HPC Clusters and/or Clouds
* Case studies of Big Data and Deep Learning applications on HPC systems and Clouds
* High-Performance streaming data processing architectures and technologies
* High-Performance graph processing with Big Data
* High-Performance SQL and NoSQL data management technologies
Papers should present original research. As the fields of Big Data, Deep Learning, and Cloud Computing span many disciplines, papers should provide sufficient background material to make them accessible to the broader community. One outstanding paper will be selected for the Best Paper Award.
All submissions should follow the IEEE standard 8.5×11 two-column format. The workshop will accept traditional research papers (8-10 pages) for in-depth topics and short papers (4 pages) for works in progress on hot topics.
– Long papers: 8-10 pages, with a full problem description, background and related work, design, and evaluation.
– Short papers: 4 pages, for works in progress on hot topics.
All the papers should be submitted through
All papers will be carefully reviewed by at least three reviewers. Papers should not be submitted in parallel to any other conference or journal.
The proceedings of this workshop will be published together with the proceedings of other IPDPS 2020 workshops by the IEEE Computer Society Press. Proceedings of the workshops are distributed at the conference and are submitted for inclusion in the IEEE Xplore Digital Library after the conference. At least one of the authors of each accepted paper must register as a participant of the workshop and present the paper at the workshop, in order to have the paper published in the proceedings.
– Abstract submission deadline (optional): January 16th, 2020 (Anywhere on Earth)
– Paper submission deadline (extended): February 1st, 2020 (Anywhere on Earth)
– Acceptance notification: March 1st, 2020
– Camera-ready deadline: March 15th, 2020
– Workshop: May 18th, 2020
Xiaoyi Lu, The Ohio State University
Jianfeng Zhan, Institute of Computing Technology, Chinese Academy of Sciences, China
Haiyang Shi, The Ohio State University
PROGRAM COMMITTEE (Confirmed So far)
Luiz F. Bittencourt, University of Campinas, Brazil
Jong Youl Choi, Oak Ridge National Laboratory
Shadi Ibrahim, Inria, France
Hyun-Wook Jin, Konkuk University, Korea
Jithin Jose, Microsoft
Zengxiang Li, Institute Of High Performance Computing, Singapore
Mingzhe Li, Facebook
Suzanne McIntosh, New York University
Manoj Nambiar, Tata Consultancy Services Ltd., India
Juan Touriño, University of A Coruña, Spain
Yunquan Zhang, Institute of Computing Technology, Chinese Academy of Sciences, China