[Storage-research-list] PDSW-DISCS'18: Call for Works-In-Progress (WIP) Abstracts

[Glenn Lockwood]

CALL FOR WORKS-IN-PROGRESS (WIP) ABSTRACTS - PDSW-DISCS'18

The 3rd Joint International Workshop on Parallel Data Storage and Data
Intensive Scalable Computing Systems (PDSW-DISCS'18)

Monday, November 12, 2018 9:00am - 5:30pm

SC'18 Workshop - Dallas, TX

http://www.pdsw-discs.org


### IMPORTANT DATES ###

- Work In Progress (WIP) Abstract Submissions due: Nov. 1, 2018, 11:59 PM
AoE
- WIP Notification: Nov. 7, 2018

### WORKSHOP ABSTRACT ###

We are pleased to announce that the 3rd Joint International Workshop on
Parallel Data Storage and Data Intensive Scalable Computing Systems
(PDSW-DISCS’18) will be hosted at SC18: The International Conference for
High Performance Computing, Networking, Storage and Analysis. This one day
joint workshop combines two overlapping communities 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 traditional simulation workloads as
well as data intensive applications. This coming convergence prompted the
integration of the PDSW and DISCS workshops into a single entity to address
the common challenges.

### TOPICS OF INTEREST ###

** 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

### WORK-IN-PROGRESS (WIP) SUBMISSION GUIDELINES ###

There will be a WIP session where presenters provide brief 5-minute talks
on their on-going work, with fresh problems/solutions. WIP content is
typically material that may not be mature or complete enough for a full
paper submission. A one-page abstract is required and should be emailed to
mcintosh at cs.nyu.edu by the Nov. 1, 2018 deadline.

### WORKSHOP ORGANIZERS ###

General Chair:
** Kathryn Mohror, Lawrence Livermore National Laboratory

Program Co-Chairs:
** Suzanne McIntosh, New York University
** Raghunath Raja Chandrasekar, Amazon Web Services

Reproducibility Co-Chairs:
** Carlos Maltzahn, University of California, Santa Cruz
** Ivo Jimenez, University of California, Santa Cruz

Publicity Chair:
** Glenn K. Lockwood, Lawrence Berkeley National Laboratory

Web and Proceedings Chair:
** Joan Digney, Carnegie Mellon University
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