MTAGS 2013 - 6th Workshop on Many-Task Computing on Clouds, Grids, and Supercomputers

The 6th workshop on Many-Task Computing on Grids and Supercomputers (MTAGS) will provide the scientific community a dedicated forum for presenting new research, development, and deployment efforts of large-scale many-task computing (MTC) applications on large scale clusters, Grids, Supercomputers, and Cloud Computing infrastructure. MTC, the theme of the workshop encompasses loosely coupled applications, which are generally composed of many tasks (both independent and dependent tasks) to achieve some larger application goal. This workshop will cover challenges that can hamper efficiency and utilization in running applications on large-scale systems, such as local resource manager scalability and granularity, efficient utilization of raw hardware, parallel file system contention and scalability, data management, I/O management, reliability at scale, and application scalability. We welcome paper submissions on all theoretical, simulations, and systems topics related to MTC, but we give special consideration to papers addressing petascale to exascale challenges. Papers will be peer-reviewed, and accepted papers will be published in the workshop proceedings as part of the ACM digital library (pending approval). The workshop will be co-located with the IEEE/ACM Supercomputing 2013 Conference in Denver Colorado on November 18th, 2013.
For more information, please see http://datasys.cs.iit.edu/events/MTAGS13/. For more information on past workshops, please see MTAGS12, MTAGS11, MTAGS10, MTAGS09, and MTAGS08. We also ran a Special Issue on Many-Task Computing in the IEEE Transactions on Parallel and Distributed Systems (TPDS) which has appeared in June 2011; the proceedings can be found online at http://www.computer.org/portal/web/csdl/abs/trans/.... We, the workshop organizers, also published a highly relevant paper that defines Many-Task Computing which was published in MTAGS08, titled “Many-Task Computing for Grids and Supercomputers”; we encourage potential authors to read this paper, and to clearly articulate in your paper submissions how your papers are related to Many-Task Computing.
Topics
We invite the submission of original work that is related to the topics below. The papers should be 6 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:
Compute Resource Management
Scheduling
Job execution frameworks
Local resource manager extensions
Performance evaluation of resource managers in use on large scale systems
Dynamic resource provisioning
Techniques to manage many-core resources and/or GPUs
Challenges and opportunities in running many-task workloads on HPC systems
Challenges and opportunities in running many-task workloads on Cloud Computing infrastructure
Storage architectures and implementations
Distributed file systems
Parallel file systems
Distributed meta-data management
Content distribution systems for large data
Data caching frameworks and techniques
Data management within and across data centers
Data-aware scheduling
Data-intensive computing applications
Eventual-consistency storage usage and management
Programming models and tools
Map-reduce and its generalizations
Many-task computing middleware and applications
Parallel programming frameworks
Ensemble MPI techniques and frameworks
Service-oriented science applications
Large-Scale Workflow Systems
Workflow system performance and scalability analysis
Scalability of workflow systems
Workflow infrastructure and e-Science middleware
Programming Paradigms and Models
Large-Scale Many-Task Applications
High-throughput computing (HTC) applications
Data-intensive applications
Quasi-supercomputing applications, deployments, and experiences
Performance Evaluation
Performance evaluation
Real systems
Simulations
Reliability of large systems
How MTC Addresses Challenges of Petascale and Exascale Computing
Concurency & Programmability
I/O & Memory
Energy
Resilience
Heterogeneity