[hpc-announce] Deadline Extension: MTAGS at SC 2017 - Papers due September 10

[Ke Wang]

We apologize if you receive multiple copies of this notice.

MTAGS17: 10th Workshop on Many-Task Computing on Clouds, Grids, and
Supercomputers

Held in conjunction with SC17:  The International Conference on High
Performance Computing, Networking, Storage and Analysis.

November 17th, 2017, Denver, Colorado, USA

<https://www.cse.unr.edu/mtags17/>


The 10th workshop on Many-Task Computing on Clouds, Grids, and
Supercomputers (MTAGS17) will provide both the scientific and industrial
communities a dedicated forum for presenting new research, development, and
deployment efforts of algorithms, frameworks, and systems for many-task
computing (MTC), machine learning and big data applications on large scale
clusters, clouds, grids, and supercomputers. The theme of the workshop
encompasses loosely-coupled applications driven by big data. The
applications are generally composed of many tasks (e.g., millions to
billions) to achieve some larger application goal. This workshop will cover
challenges that can hamper efficiency and utilization in running
applications on extreme-scale systems, such as local resource manager’s
scalability and granularity, data-aware scheduling, efficient utilization
of intra-node parallelism, parallel file-system contention and scalability,
data locality, I/O management, reliability at scale, and application
scalability. We welcome paper submissions in theoretical, simulations, and
real systems topics with special consideration to papers addressing the
intersection of petascale/exascale challenges with large-scale cloud
computing and machine learning. Papers will be peer-reviewed, and accepted
papers will be published in the workshop proceedings. The workshop will be
held in conjunction with SC17---The International Conference on High
Performance Computing, Networking, Storage and Analysis---in Denver,
Colorado, USA. For more information, please visit: https://www.cse.unr.edu/
mtags17/.


Topics

We invite the submission of original work that is related to the topics
below. The papers should be 8 pages, including all figures and references.
We aim to cover topics related to Many-Task Computing on each of the three
major distributed systems paradigms, Cloud Computing, Grid Computing and
Supercomputing. Topics of interest include, but are not limited to:

Compute resource management
o   Distributed scheduling algorithm
o   Runtime environment
o   Intra-node resource management
o   Performance evaluation of resource managers in use on large scale
systems
o   Dynamic resource allocation
o   Power-aware scheduling
o   Scheduling applications' executions with little performance variation
between different runs
o   Techniques to manage and schedule generic resources including MIC
and/or GPUs
o   Challenges and opportunities in scheduling and running loosely-coupled
data-intensive applications on HPC systems and Cloud Computing
infrastructures

Data storage architectures and implementations
o   Parallel and distributed file systems
o   Multi-tier data stores
o   NoSQL data storage system
o   Distributed meta-data management
o   Data caching frameworks and techniques
o   Data provenance
o   Data management within and across data centers
o   Data-aware and locality-aware scheduling
o   Data-intensive computing applications
o   Eventual-consistency storage usage and management

Programming models and tools
o   MapReduce/Hadoop/Spark and their generalizations and implementations
o   Many-task computing and cloud computing middleware and applications
o   Parallel inter-node programming frameworks
o   Distributed intra-node programming models
o   MPI fault tolerant techniques
o   Scalable and reliable overlay network
o   Ensemble MPI techniques and frameworks
o   Service-oriented science applications

Large-scale workflow systems
o   Workflow system performance and scalability analysis
o   Scalability of workflow systems
o   Parallel programming language
o   Workflow infrastructure and e-Science middleware
o   Application data dependency graph generation tool

Large-scale loosely coupled applications
o   Data-intensive MTC applications
o   High-throughput computing (HTC) applications
o   Machine learning and big data applications
o   Quasi-supercomputing applications, deployments, and experiences

Performance evaluation
o   Real systems
o   Discrete event simulations (DES) and Parallel discrete event
simulations (PDES)
o   Docker container techniques
o   Reliability of large systems
o   Application performance tuning


Important Dates

Full paper due: September 10, 2017
Acceptance notification: September 29th, 2017
Camera Ready Due: October 13th, 2017
Workshop date: November 17th, 2017


Paper Submission

Authors are invited to submit papers with unpublished, original work of not
more than 8 pages of double column text using single spaced 10 point size
on 8.5 x 11 inch pages, as per IEEE 8.5 x 11 manuscript guidelines;
document templates can be found at http://www.ieee.org/conferences_events/
conferences/publishing/templates.html. The final 8-page papers in PDF
format must be submitted online at easychair before deadline:
https://easychair.org/conferences/?conf=mtags17. Papers will be
peer-reviewed for novelty, scientific merit, and scope for the workshop.
Submission implies the willingness of at least one of the authors to
register and present the paper.


Organization

General Chairs

Ke Wang, Intel Corportation, USA
Dongfang Zhao, University of Nevada, USA

Steering Committee

Ioan Raicu, Illinois Institute of Technology, USA
Justin Wozniak, university of Chicago & Argonne National Laboratory, USA
Ian Foster, University of Chicago & Argonne National Laboratory
Yong Zhao, University of Electronic Science and Technology of China

Program Committee

Kyle Chard, University of Chicago, USA
Evangelinos Constantinos, Massachusetts Institute of Technology, USA
Bo Feng, Capital One, USA
Florin Isaila, Universidad Carlos III de Madrid, Spain
Jik-Soo Kim, KISTI, Korea
Anthony Kougkas, Illinois Institute of Technology, USA
Michael Lang, Los Alamos National Laboratory, USA
Christopher Moretti, Princeton University, USA
Bogdan Nicolae, Huawei Research, Germany
David O'Hallaron, Carnegie Mellon University, USA
Ana-Maria Oprescu, University of Amsterdam, Netherlands
Judy Qiu, Indiana University, USA
Matei Ripeanu, University of British Columbia, Canada
Iman Sadooghi, Bank of America, USA
Mike Wilde, University of Chicago & Argonne National Laboratory, USA
Xu Yang, Amazon, USA
Zhao Zhang, Texas Advanced Computing Center, USA
Zhou Zhou, Salesforce, USA

-- 

Ke
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