Scott Callaghan

Pegasus, a workflow management system for science automation

Modern science often requires the execution of large-scale, multi-stage simulation and data analysis pipelines to enable the study of complex systems. The amount of computation and data involved in these pipelines requires scalable workflow management systems that are able to reliably and efficiently coordinate and automate data movement and task execution on distributed computational resources: campus clusters, national cyberinfrastructures, and commercial and academic clouds. This paper describes the design, development and evolution of the Pegasus Workflow Management System, which maps abstract workflow descriptions onto distributed computing infrastructures. Pegasus has been used for more than twelve years by scientists in a wide variety of domains, including astronomy, seismology, bioinformatics, physics and others. This paper provides an integrated view of the Pegasus system, showing its capabilities that have been developed over time in response to application needs and to the evolution of the scientific computing platforms. The paper describes how Pegasus achieves reliable, scalable workflow execution across a wide variety of computing infrastructures. Comprehensive description of the Pegasus Workflow Management System.Detailed explanation of Pegasus workflow transformations.Data management in Pegasus.Earthquake science application example.

Managing Large-Scale Workflow Execution from Resource Provisioning to Provenance Tracking: The CyberShake Example

This paper discusses the process of building an environment where large-scale, complex, scientific analysis can be scheduled onto a heterogeneous collection of computational and storage resources. The example application is the Southern California Earthquake Center (SCEC) CyberShake project, an analysis designed to compute probabilistic seismic hazard curves for sites in the Los Angeles area. We explain which software tools were used to build to the system, describe their functionality and interactions. We show the results of running the CyberShake analysis that included over 250,000 jobs using resources available through SCEC and the TeraGrid.

SCEC CyberShake Workflows—Automating Probabilistic Seismic Hazard Analysis Calculations

The Southern California Earthquake Center (SCEC) is a community of more than 400 scientists from over 54 research organizations that conducts geophysical research in order to develop a physics-based understanding of earthquake processes and to reduce the hazard from earthquakes in the Southern California region [377].

Physics-based seismic hazard analysis on petascale heterogeneous supercomputers

We have developed a highly scalable and efficient GPU-based finite-difference code (AWP) for earthquake simulation that implements high throughput, memory locality, communication reduction and communication / computation overlap and achieves linear scalability on Cray XK7 Titan at ORNL and NCSAs Blue Waters system. We simulate realistic 0-10 Hz earthquake ground motions relevant to building engineering design using high-performance AWP. Moreover, we show that AWP provides a speedup by a factor of 110 in key strain tensor calculations critical to probabilistic seismic hazard analysis (PSHA). These performance improvements to critical scientific application software, coupled with improved co-scheduling capabilities of our workflow-managed systems, make a statewide hazard model a goal reachable with existing supercomputers. The performance improvements of GPU-based AWP are expected to save millions of core-hours over the next few years as physics-based seismic hazard analysis is developed using heterogeneous petascale supercomputers.

Metrics for heterogeneous scientific workflows: A case study of an earthquake science application

Scientific workflows are a common computational model for performing scientific simulations. They may include many jobs, many scientific codes, and many file dependencies. Since scientific workflow applications may include both high-performance computing (HPC) and high-throughput computing (HTC) jobs, meaningful performance metrics are difficult to define, as neither traditional HPC metrics nor HTC metrics fully capture the extent of the application. We describe and propose the use of alternative metrics to accurately capture the scale of scientific workflows and quantify their efficiency. In this paper, we present several specific practical scientific workflow performance metrics and discuss these metrics in the context of a large-scale scientific workflow application, the Southern California Earthquake Center CyberShake 1.0 Map calculation. Our metrics reflect both computational performance, such as floating-point operations and file access, and workflow performance, such as job and task scheduling and execution. We break down performance into three levels of granularity: the task, the workflow, and the application levels, presenting a complete view of application performance. We show how our proposed metrics can be used to compare multiple invocations of the same application, as well as executions of heterogeneous applications, quantifying the amount of work performed and the efficiency of the work. Finally, we analyze CyberShake using our proposed metrics to determine potential application optimizations.

Enabling large-scale scientific workflows on petascale resources using MPI master/worker

Computational scientists often need to execute large, loosely-coupled parallel applications such as workflows and bags of tasks in order to do their research. These applications are typically composed of many, short-running, serial tasks, which frequently demand large amounts of computation and storage. In order to produce results in a reasonable amount of time, scientists would like to execute these applications using petascale resources. In the past this has been a challenge because petascale systems are not designed to execute such workloads efficiently. In this paper we describe a new approach to executing large, fine-grained workflows on distributed petascale systems. Our solution involves partitioning the workflow into independent subgraphs, and then submitting each subgraph as a self-contained MPI job to the available resources (often remote). We describe how the partitioning and job management has been implemented in the Pegasus Workflow Management System. We also explain how this approach provides an end-to-end solution for challenges related to system architecture, queue policies and priorities, and application reuse and development. Finally, we describe how the system is being used to enable the execution of a very large seismic hazard analysis application on XSEDE resources.

Accelerating CyberShake Calculations on the XE6/XK7 Platform of Blue Waters

CyberShake is a computational platform developed by the Southern California Earthquake Center (SCEC) that explicitly incorporates earthquake rupture time histories and deterministic wave propagation effects into seismic hazard calculations through the use of 3D waveform simulations. Using CyberShake, SCEC has created the first physics-based probabilistic seismic hazard analysis (PSHA) models of the Los Angeles region from suites of simulations comprising ~108 seismograms. The current models are, however, limited to low seismic frequencies (≤ 0.5 Hz). To increase the maximum simulated frequency to above 1 Hz and produce a California state-wide model, we have transformed SCEC Anelastic Wave Propagation code (AWP-ODC) to include strain Greens tensor (SGT) calculations to accelerate CyberShake calculations. This tensor-valued wave field code has both CPU and GPU components in place for flexibility on different architectures. We have demonstrated the performance and scalability of this solver optimized for the heterogeneous Blue Waters system at NCSA. The high performance of the wave propagation computation, coupled with CPU/GPU co-scheduling capabilities of our workflow-managed systems, make a statewide hazard model a goal reachable with existing supercomputers.

Experiences Using GlideinWMS and the Corral Frontend across Cyberinfrastructures

Even with Grid technologies, the main mode of access for the current High Performance Computing and High Throughput Computing infrastructures today is logging in via ssh. This mode of access locks scientists to particular machines as it is difficult to move the codes and environments between hosts. In this paper we show how switching the resource access mode to a Condor glide in-based overlay can bring together computational resources from multiple cyber infrastructures. This approach provides scientists with a computational infrastructure anchored around the familiar environment of the desktop computer. Additionally, the approach enhances the reliability of applications and workflows by automatically rerouting jobs to functioning infrastructures. Two different science applications were used to demonstrate applicability, one from the field of astronomy and the other one from earth sciences. We demonstrate that a desktop computer is viable as a submit host and central manager for these kind of glide in overlays. However, issues of ease of use and security need to be considered.

The SCEC Unified Community Velocity Model Software Framework

ABSTRACT Crustal seismic‐velocity models and datasets play a key role in regional 3D numerical earthquake ground‐motion simulation, full waveform tomography, and modern physics‐based probabilistic earthquake‐hazard analysis, as well as in other related fields, including geophysics and earthquake engineering. Most of these models and datasets, often collectively identified as Community Velocity Models (CVMs), synthesize information from multiple sources and are delivered to users in variable formats, including computer applications that allow for interactive querying of material properties, namely P ‐ and S ‐wave velocities and density ρ . Computational users often require massive and repetitive access to velocity models and datasets, and such access is often unpractical and difficult due to a lack of standardized methods and procedures. To overcome these issues and to facilitate access by the community to these models, the Southern California Earthquake Center developed the Unified CVM (UCVM) software framework, an open‐source collection of tools that enables users to access one or more seismic‐velocity models, while providing a standard query interface. Here, we describe the research challenges that motivated the development of UCVM, its software design, development approach, and basic capabilities, as well as a few examples of seismic‐modeling applications that use UCVM.

rvGAHP: push-based job submission using reverse SSH connections

Computational science researchers running large-scale scientific workflow applications often want to run their workflows on the largest available compute systems to improve time to solution. Workflow tools used in distributed, heterogeneous, high performance computing environments typically rely on either a push-based or a pull-based approach for resource provisioning from these compute systems. However, many large clusters have moved to two-factor authentication for job submission, making traditional automated push-based job submission impossible. On the other hand, pull-based approaches such as pilot jobs may lead to increased complexity and a reduction in node-hour efficiency. In this paper, we describe a new, efficient approach based on HTCondor-G called reverse GAHP (rvGAHP) that allows us to push jobs using reverse SSH submissions with better efficiency than pull-based methods. We successfully used this approach to perform a large probabilistic seismic hazard analysis study using SCECs CyberShake workflow in March 2017 on the Titan Cray XK7 hybrid system at Oak Ridge National Laboratory.