Abstract – WRENCH enables novel avenues for scientific workflow use, research, development, and education. WRENCH capitalizes on recent and critical advances in the state of the art of distributed platform/application simulation. WRENCH builds on top of the open-source SimGrid simulation framework. SimGrid enables the simulation of large-scale distributed applications in a way that is accurate (via validated simulation models), scalable (low ratio of simulation time to simulated time, ability to run large simulations on a single computer with low compute, memory, and energy footprints), and expressive (ability to simulate arbitrary platform, application, and execution scenarios). WRENCH provides directly usable high-level simulation abstractions using SimGrid as a foundation. More information on https://wrench-project.org.
In a nutshell, WRENCH makes it possible to:
- Prototype implementations of Workflow Management System (WMS) components and underlying algorithms;
- Quickly, scalably, and accurately simulate arbitrary workflow and platform scenarios for a simulated WMS implementation; and
- Run extensive experimental campaigns to conclusively compare workflow executions, platform architectures, and WMS algorithms and designs.
Held in conjunction with SC18: The International Conference for High Performance Computing, Networking, Storage and Analysis
Data-intensive workflows (a.k.a. scientific workflows) are routinely used in most scientific disciplines today, especially in the context of high-performance, parallel and distributed computing. They provide a systematic way of describing a complex scientific process and rely on sophisticated workflow management systems to execute on a variety of parallel and distributed resources. With the dramatic increase of raw data volume in every domain, they play an even more critical role to assist scientists in organizing and processing their data and to leverage HPC or HTC resources, being at the interface between end-users and computing infrastructures.
This workshop focuses on the many facets of data-intensive workflow management systems, ranging from actual execution to service management and the coordination and optimization of data, service and job dependencies. The workshop covers a broad range of issues in the scientific workflow lifecycle that include: data-intensive workflows representation and enactment; designing workflow composition interfaces; workflow mapping techniques to optimize the execution of the workflow for different infrastructures; workflow enactment engines that need to deal with failures in the application and execution environment; and a number of computer science problems related to scientific workflows such as semantic technologies, compiler methods, scheduling and fault detection and tolerance.
Papers due: July 30, 2018
Paper Acceptance Notification: September 9, 2018
Presentation held at the 18th SIAM Conference on Parallel Processing for Scientific Computing, 2018
Resource Management, Scheduling, Workflows: Critical Middleware for HPC and Clouds
Abstract – This talk will examine issues of workflow execution, in particular using the Pegasus Workflow Management System, on distributed resources and how these resources can be provisioned ahead of the workflow execution. Pegasus was designed, implemented and supported to provide abstractions that enable scientists to focus on structuring their computations without worrying about the details of the target cyberinfrastructure. To support these workflow abstractions Pegasus provides automation capabilities that seamlessly map workflows onto target resources, sparing scientists the overhead of managing the data flow, job scheduling, fault recovery and adaptation of their applications. In some cases, it is beneficial to provision the resources ahead of the workflow execution, enabling the re-use of resources across workflow tasks. The talk will examine the benefits of resource provisioning for workflow execution.