[hpc-announce] Extended Deadline Sep 11: PDSW-DISCS at SC 2016, The 1st Joint International Workshop on Parallel Data Storage and Data Intensive Scalable Computing Systems

[Jialin Liu]

**** CALL FOR PAPERS ****

The 1st Joint International Workshop on Parallel Data Storage and Data
Intensive Scalable Computing Systems (PDSW-DISCS’16)
Held in conjunction with SC16: The International Conference for High
Performance Computing, Networking, Storage and Analysis, Salt Lake
City, UT, and in cooperation with SIGHPC.
Monday, November 14, 2016

http://www.pdsw-discs.org

We are pleased to announce that the first Joint International Workshop
on Parallel Data Storage and Data Intensive Scalable Computing Systems
(PDSW-DISCS’16) will be hosted at SC16: The International Conference
for High Performance Computing, Networking, Storage and Analysis.  The
objective of this one day joint workshop is to combine two overlapping
communities and to better promote and stimulate researchers’
interactions to address some of the most critical challenges for
scientific data storage, management, devices, and processing
infrastructure for both traditional compute intensive simulations and
data-intensive high performance computing solutions.  Special
attention will be given to issues in which community collaboration can
be crucial for problem identification, workload capture, solution
interoperability, standards with community buy­-in, and shared tools.

Many scientific problem domains continue to be extremely data
intensive. Traditional high performance computing (HPC) systems and
the programming models for using them such as MPI were designed from a
compute-centric perspective with an emphasis on achieving high
floating point computation rates. But processing, memory, and storage
technologies have not kept pace and there is a widening performance
gap between computation and the data management infrastructure. Hence
data management has become the performance bottleneck for a
significant number of applications targeting HPC systems.
Concurrently, there are increasing challenges in meeting the growing
demand for analyzing experimental and observational data.  In many
cases, this is leading new communities to look towards HPC platforms.
In addition, the broader computing space has seen a revolution in new
tools and frameworks to support Big Data analysis and machine
learning.

There is a growing need for convergence between these two worlds.
Consequently, the U.S. Congressional Office of Management and Budget
has informed the U.S. Department of Energy that new machines beyond
the first exascale machines must address both the traditional
simulation workloads as well as data intensive applications. This
coming convergence prompts integrating these two workshops into a
single entity to address the common challenges.


The scope of the proposed joint PDSW-DISCS workshop is summarized as:
* Scalable storage architectures, archival storage, storage
virtualization, emerging storage devices and techniques
* Performance benchmarking, resource management, and workload studies
from production systems including both traditional HPC and
data-intensive workloads.
* Programmability, APIs, and fault tolerance of storage systems
* Parallel file systems, metadata management, and complex data
management, object and key-value storage, and other emerging data
storage/retrieval techniques
* Programming models and frameworks for data intensive computing
including extensions to traditional and nontraditional programming
models, asynchronous multi-task programming models, or to data
intensive programming models
* Techniques for data integrity, availability and reliability especially
* Productivity tools for data intensive computing, data mining and
knowledge discovery
* Application or optimization of emerging “big data” frameworks
towards scientific computing and analysis
* Techniques and architectures to enable cloud and container-based
models for scientific computing and analysis
* Techniques for integrating compute into a complex memory and storage
hierarchy facilitating in situ and in transit data processing
* Data filtering/compressing/reduction techniques that maintain
sufficient scientific validity for large scale compute-intensive
workloads
* Tools and techniques for managing data movement among compute and
data intensive components both solely within the computational
infrastructure as well as incorporating the memory/storage hierarchy


Paper Submissions: http://www.pdsw-discs.org/
Paper (in pdf format) due Wednesday, Sept. 11, 2016, 11:59PM AoE (Hard deadline)
Notification: Friday, Sept. 30, 2016
Camera ready due: Friday, Oct. 7, 2016
Slides due before workshop: Sunday, Nov. 13, 2016, 5:00 pm PDT

Paper Submission Details:
The PDSW-DISCS Workshop holds a peer reviewed competitive process for
selecting short papers. Submit a not previously published short paper
of up to 5 pages, not less than 10 point font and not including
references, in a PDF file as instructed on the workshop web site.
Submitted papers will be reviewed under the supervision of the
workshop program committee. Submissions should indicate authors and
affiliations. Papers must not be longer than 5 pages (excluding
references). Selected papers and associated talk slides will be made
available on the workshop web site; the papers will also be published
in the digital libraries of the IEEE and ACM.

Work-in-progress (WIP) Submissions: http://www.pdsw-discs.org/
There will also be a WIP session at the workshop, where presenters
give 5-minute brief talks on their on-going work, with fresh
problems/solutions, but may not be mature or complete yet for paper
submission. A 1-page abstract is required as instructed on the
workshop web site.

WIP Submission Deadline: Tuesday, Nov. 1, 2016
WIP Notification: Monday, Nov. 7, 2016

General Co-Chairs:
Garth Gibson (Carnegie Mellon University)
Yong Chen (Texas Tech University)

Program Co-Chairs:
Shane Canon (Lawrence Berkeley National Laboratory)
Dean Hildebrand (IBM Research)

Publications Chair:
Jialin Liu (Lawrence Berkeley National Laboratory)