FG-42
Characterizing Deep Sequencing Analytics using BFAST: Towards a Scalable Distributed Architecture for Next-Generation Sequencing Data
Building Gateways for Life-Science Applications using the Dynamic Application Runtime Environment (DARE) Framework
Abstractions for Loosely-Coupled and Ensemble-Based Simulations on Azure
SAGA BigJob: An Extensible and Interoperable Pilot-Job Abstraction for Distributed Applications and Systems
Understanding application-level interoperability: Scaling-out MapReduce over high-performance grids and clouds
SAGA
Project Details
- Project Lead
- Shantenu Jha
- Project Manager
- Yaakoub El Khamra
- Project Members
- Ole Weidner, Hartmut Kaiser, Pradeep Kumar Mantha, Andre Luckow, Sharath Maddineni, Pradeep Kumar Mantha, Andre Merzky, Melissa Romanus, Ashley Zebrowski, Mark Santcroos, Blaise Bourdin, Frank Löffler, Ole Weidner, Vishal Shah, Anjanibhargavi Ragothaman, Jeffrey Rabinowitz, Dinesh Ganapathi, Sanket Wagle, Matteo Turilli, Christian Straube, Antons Treikalis, Michael Wilde, Antons Treikalis, Nikhil Shenoy, Daniel S. Katz
- Supporting Experts
- Andrew Younge, Xiaoming Gao, Tao Huang
- Institution
- Louisiana State University, Center for Computation & Technology
- Discipline
- Computer Science (401)
Abstract
The Simple API for Grid Applications (SAGA) is an OGF standard (http://www.ogf.org), and defines a high level, application oriented API for developing first principle distributed applications. Our SAGA implementation (in C++ and Python, see http://saga.cct.lsu.edu/) is able to interface to a variety of middleware backends. We also develop application frameworks based on SAGA, such as Master-Worker, MapReduce, AllPairs, BigJobs, etc.\n\n\n\nFor all those components, we intent to use futuregrid and the different software environments available on FG for extensive portability and interoperability testing, but also for scale-up and scale-out experiments. The proposed activities will allow to harden the SAGA components described above.
Intellectual Merit
The Simple API for Grid Applications (SAGA) is an OGF standard (http://www.ogf.org), and defines a high level, application oriented API for developing first principle distributed applications. Our SAGA implementation (in C++ and Python, see http://saga.cct.lsu.edu/) is able to interface to a variety of middleware backends. We also develop application frameworks based on SAGA, such as Master-Worker, MapReduce, AllPairs, BigJobs, etc.\n\n\n\nFor all those components, we intent to use futuregrid and the different software environments available on FG for extensive portability and interoperability testing, but also for scale-up and scale-out experiments. The proposed activities will allow to harden the SAGA components described above.
Broader Impacts
The Simple API for Grid Applications (SAGA) is an OGF standard (http://www.ogf.org), and defines a high level, application oriented API for developing first principle distributed applications. Our SAGA implementation (in C++ and Python, see http://saga.cct.lsu.edu/) is able to interface to a variety of middleware backends. We also develop application frameworks based on SAGA, such as Master-Worker, MapReduce, AllPairs, BigJobs, etc.\n\n\n\nFor all those components, we intent to use futuregrid and the different software environments available on FG for extensive portability and interoperability testing, but also for scale-up and scale-out experiments. The proposed activities will allow to harden the SAGA components described above.
Scale of Use
In general we have very low scale requirements, but would like to be able to test scale up and scale-out now and then, for very short periods of time (hours to a few days).
Results
Abstract:
Advances in many areas of science and scientific computing are predicated on rapid progress in fundamental
The full version of the report is available here.