HPC4BD, Minneapolis, MN



Processing large datasets for extracting information and knowledge has always been a fundamental problem. Today this problem is further exacerbated, as the data a researcher or a company needs to cope with can be immense in terms of volume, distributed in terms of location, and unstructured in terms of format. Recent advances in computer hardware and storage technologies have allowed us to gather, store, and analyze such large-scale data. However, without scalable and cost effective algorithms that utilize the resources in an efficient way, neither the resources nor the data itself can serve to science and society at its full potential.

Analyzing Big Data requires a vast amount of storage and computing resources. We need to untangle the big, puzzling information we have and while doing this, we need to be fast and robust: the information we need may be crucial for a life-or-death situation. We need to be accurate: a single misleading information extracted from the data can cause an avalanche effect. Each problem has its own characteristic and priorities.  Hence, the best algorithm and architecture combination is different for different applications.

This workshop aims to bring people who work on data-intensive and high performance computing in industry, research labs, and academia together to share their problems posed by the Big Data in various application domains and knowledge required to solve them.

All novel data-intensive computing techniques, data storage and integration schemes, and algorithms for cutting-edge high performance computing architectures which targets the utilization of Big Data are of interest to the workshop. Examples of topics include but not limited to

  1. parallel algorithms for data-intensive applications,

  2. scalable data and text mining andd information retrieval,

  3. using Hadoop and MapReduce to analyze Big Data,

  4. energy-efficient data-intensive computing, 

  5. querying and visualization of large network datasets,

  6. processing large-scale datasets on clusters of multicore and manycore processors, and accelerators,

  7. heterogeneous computing for Big Data architectures,

  8. Big Data in the Cloud,

  9. processing and analyzing high-resolution images using high-performance computing,

  10. using hybrid infrastructures for Big Data analysis.

Submission information: Papers should be formatted according to the CPS standard, double column format with a font size 10 pt or larger. Templates can be found here. Each paper is strictly limited to 10 pages in length. Submissions should represent original, substantive research results.

Update: Please follow the instructions here for camera ready submissions.



Workshop on




for Big Data

to be held in conjunction with the 43rd International Conference on Parallel Processing (ICPP), Sept. 10 2014.


Kamer Kaya, The Ohio State University

Buğra Gedik, Bilkent University

Ümit V. Çatalyürek, The Ohio State University

Program Committee

Berkant Barla Cambazoğlu

Yahoo Research

Mahantesh Halappanavar

Pacific Northwest National Laboratory

Nilesh Jain

Intel Labs

Heng Ji

Rensselaer Polytechnic Institute

Vana Kalogeraki

Athens Uni. of Economics and Business

Tevfik Koşar

University of Buffalo

Tahsin Kurç

Stony Brook University

Kamesh Madduri

Pennsylvania State University

Ioan Raicu

Illinois Institute of Technology

Siva Rajamanickam

Sandia National Laboratories

Sanjay Ranka

University of Florida

Erik Saule

University of North Carolina Charlotte

Scott Schneider

IBM Research

Bora Uçar

CNRS and LIP, ENS Lyon

Peter R. Pietzuch

Imperial College London

Important Dates

Submission deadline: May 8, 2014

               Extended:  May 20, 2014

Notification deadline: June 8, 2014

Camera ready deadline: July 4, 2014 (No extension. Please follow the instructions here)

Workshop date: September 10, 2014  (Please see the schedule here)