Slides for all talks can be found here.
And all accepted papers are now available from the ACM DL, along with full PDFs for downloading.
9:00-9:10 Welcome, Sandra Gesing
9:10-10:00 Manish Parashar, Extreme Scale Data Management for In-Situ Scientific Workflows (invited keynote talk)
Data staging and in-situ/in-transit data processing are emerging as attractive approaches for supporting extreme scale scientific workflows. These approaches can improve end-to-end performance by enabling efficient data sharing between coupled simulations and data analytics components of an in-situ workflow. However, complex and dynamic data access/exchange patterns coupled with architectural trends toward smaller memory per core and deeper memory hierarchies threaten to impact the effectiveness of this approach. In this talk, I will explore a policy-based autonomic data management approach that can adaptively respond at runtime to dynamic data management requirements. Specifically, I will formulate the autonomic data management approach and present the design and implementation of autonomic policies as well as cross layer mechanisms, and will experimentally demonstrate how these autonomic adaptations can tune the application behaviors and resource allocations at runtime while meeting the data management requirements and constraints. This research is part of the DataSpaces project at the Rutgers Discovery Informatics Institute.
Manish Parashar is Distinguished Professor of Computer Science at Rutgers University. He is also the founding Director of the Rutgers Discovery Informatics Institute (RDI2). His research interests are in the broad areas of Parallel and Distributed Computing and Computational and Data-Enabled Science and Engineering. Manish is founding chair of the IEEE Technical Consortium on High Performance Computing (TCHPC), Editor-in-Chief (elect) of the IEEE Transactions on Parallel and Distributed Systems, and serves on the editorial boards and organizing committees of a large number of journals and international conferences and workshops. He has over 350 publications, has deployed several software systems that are widely used, and has received a number of awards for his research and leadership. Manish is Fellow of AAAS, Fellow of IEEE/IEEE Computer Society and ACM Distinguished Scientist. For more information please visit http://parashar.rutgers.edu/.
10:30-10:55 Matthias Janetschek and Radu Prodan: A Compiler Transformation-based Approach to Scientific Workflow Enactment
10:55-11:20 Matthieu Dorier, Justin Wozniak and Robert Ross: Supporting Task-level Fault-Tolerance in HPC Workflows by Launching MPI Jobs inside MPI Jobs
11:20-11:45 Rafael Ferreira Da Silva, Scott Callaghan and Ewa Deelman: On the Use of Burst Buffers for Accelerating Data-Intensive Scientific Workflows
11:45-12:10 Scott Callaghan, Gideon Juve, Karan Vahi, Philip J. Maechling, Thomas H. Jordan and Ewa Deelman, rvGAHP - Push-Based Job Submission Using Reverse SSH Connections
12:10-12:20 Renan Souza, Vítor Silva, Jose J. Camata, Alvaro L.G.A. Coutinho, Patrick Valduriez and Marta Mattoso: Tracking of Online Parameter Fine-tuning in Scientific Workflows
12:20-12:30 Kyle Sweeney and Douglas Thain, Lightweight Container Integration into Workflow Systems: A Case Study with Singularity and Makeflow
14:00-14:25 William Fox, Devarshi Ghoshal, Abel Souza, Gonzalo Rodrigo Alvarez and Lavanya Ramakrishnan: E-HPC: A Library for Elastic Resource Management in HPC Environments
14:25-14:50 Isabel Rosseti, Kary Ocaña and Daniel de Oliveira: Towards Preserving Results Confidentiality in Cloud-based Scientific Workflows
14:50-15:00 Simon Woodman, Hugo Hiden and Paul Watson: e-Science Central Workflows for Stream Processing: Adding Streaming Support to an Existing Workflow System
15:30-15:40 Vahid Arabnejad, Kris Bubendorfer and Bryan Ng: Dynamic Workflow Scheduling: A Deadline and Cost-Aware Approach for Commercial Clouds
15:40-15:50 Yves Caniou, Eddy Caron, Aurélie Kong Win Chnag and Yves Robert: Budget-aware scheduling algorithms for scientific workflows on IaaS Cloud platforms
15:50-16:15 Alok Singh, Arvind Rao, Shweta Purawat and Ilkay Altintas: A Machine Learning Approach for Modular Workflow Performance Prediction
16:15-16:40 Raffaele Montella, Diana Di Luccio, Livia Marcellino, Ardelio Galletti, Sokol Kosta, Alison Brizius and Ian Foster: Processing of Crowd-sourced Data from an Internet of Floating Things
16:40-17:30 Scott Lathrop with Jarek Nabrzyski, Krzysztof Kurowski and Daniel S. Katz: Panel "Promoting Scientific Workflows"