K. Blackburn

The open science grid

The Open Science Grid (OSG) provides a distributed facility where the Consortium members provide guaranteed and opportunistic access to shared computing and storage resources. OSG provides support for and evolution of the infrastructure through activities that cover operations, security, software, troubleshooting, addition of new capabilities, and support for existing and engagement with new communities. The OSG SciDAC-2 project provides specific activities to manage and evolve the distributed infrastructure and support its use. The innovative aspects of the project are the maintenance and performance of a collaborative (shared & common) petascale national facility over tens of autonomous computing sites, for many hundreds of users, transferring terabytes of data a day, executing tens of thousands of jobs a day, and providing robust and usable resources for scientific groups of all types and sizes. More information can be found at the OSG web site: www.opensciencegrid.org.

GriPhyN and LIGO, building a virtual data Grid for gravitational wave scientists

Many Physics experiments today generate large volumes of data. That data is then processed in a variety of ways in order to achieve the understanding of fundamental physical phenomena. The goal of the NSF-funded GriPhyN project (Grid Physics Network) is to enable scientists to seamlessly access data whether it is raw experimental data or a data product which is a result of further processing. GriPhyN provides a new degree of transparency in how data-handling and processing capabilities are integrated to deliver data products to end-users or applications, so that requests for such products are easily mapped into computation and/or data access at multiple locations. GriPhyN refers to the set of all data products available to the user as virtual data. Among the physics applications participating in the project is the Laser Interferometer Gravitational-wave Observatory (LIGO), which is being built to observe the gravitational waves predicted by general relativity. We describe our initial design and prototype of a virtual data Grid for LIGO.

Scheduling Data-IntensiveWorkflows onto Storage-Constrained Distributed Resources

In this paper we examine the issue of optimizing disk usage and of scheduling large-scale scientific workflows onto distributed resources where the workflows are data- intensive, requiring large amounts of data storage, and where the resources have limited storage resources. Our approach is two-fold: we minimize the amount of space a workflow requires during execution by removing data files at runtime when they are no longer required and we schedule the workflows in a way that assures that the amount of data required and generated by the workflow fits onto the individual resources. For a workflow used by gravitational- wave physicists, we were able to improve the amount of storage required by the workflow by up to 57 %. We also designed an algorithm that can not only find feasible solutions for workflow task assignment to resources in disk- space constrained environments, but can also improve the overall workflow performance.

Parameter estimation for compact binaries with ground-based gravitational-wave observations using the LALInference software library

The Advanced LIGO and Advanced Virgo gravitational-wave (GW) detectors will begin operation in the coming years, with compact binary coalescence events a likely source for the first detections. The gravitational waveforms emitted directly encode information about the sources, including the masses and spins of the compact objects. Recovering the physical parameters of the sources from the GW observations is a key analysis task. This work describes the LALInference software library for Bayesian parameter estimation of compact binary signals, which builds on several previous methods to provide a well-tested toolkit which has already been used for several studies. We show that our implementation is able to correctly recover the parameters of compact binary signals from simulated data from the advanced GW detectors. We demonstrate this with a detailed comparison on three compact binary systems: a binary neutron star, a neutron star–black hole binary and a binary black hole, where we show a cross comparison of results obtained using three independent sampling algorithms. These systems were analyzed with nonspinning, aligned spin and generic spin configurations respectively, showing that consistent results can be obtained even with the full 15-dimensional parameter space of the generic spin configurations. We also demonstrate statistically that the Bayesian credible intervals we recover correspond to frequentist confidence intervals under correct prior assumptions by analyzing a set of 100 signals drawn from the prior. We discuss the computational cost of these algorithms, and describe the general and problem-specific sampling techniques we have used to improve the efficiency of sampling the compact binary coalescence parameter space.

The Pegasus portal: web based grid computing

Pegasus is a planning framework for mapping abstract workflows for execution on the Grid. This paper presents the implementation of a web-based portal for submitting workflows to the Grid using Pegasus. The portal also includes components for generating abstract workflows based on a metadata description of the desired data products and application-specific services. We describe our experiences in using this portal for two Grid applications. A major contribution of our work is in introducing several components that can be useful for Grid portals and hence should be included in Grid portal development toolkits.

Analysis Framework for the Prompt Discovery of Compact Binary Mergers in Gravitational-wave Data

We describe a stream-based analysis pipeline to detect gravitational waves from the merger of binary neutron stars, binary black holes, and neutron-star–black-hole binaries within ∼1 min of the arrival of the merger signal at Earth. Such low-latency detection is crucial for the prompt response by electromagnetic facilities in order to observe any fading electromagnetic counterparts that might be produced by mergers involving at least one neutron star. Even for systems expected not to produce counterparts, low-latency analysis of the data is useful for deciding when not to point telescopes, and as feedback to observatory operations. Analysts using this pipeline were the first to identify GW151226, the second gravitational-wave event ever detected. The pipeline also operates in an offline mode, in which it incorporates more refined information about data quality and employs acausal methods that are inapplicable to the online mode. The pipeline’s offline mode was used in the detection of the first two gravitational-wave events, GW150914 and GW151226, as well as the identification of a third candidate, LVT151012.

Optimizing workflow data footprint

In this paper we examine the issue of optimizing disk usage and scheduling large-scale scientific workflows onto distributed resources where the workflows are data-intensive, requiring large amounts of data storage, and the resources have limited storage resources. Our approach is two-fold: we minimize the amount of space a workflow requires during execution by removing data files at runtime when they are no longer needed and we demonstrate that workflows may have to be restructured to reduce the overall data footprint of the workflow. We show the results of our data management and workflow restructuring solutions using a Laser Interferometer Gravitational-Wave Observatory (LIGO) application and an astronomy application, Montage, running on a large-scale production grid-the Open Science Grid. We show that although reducing the data footprint of Montage by 48% can be achieved with dynamic data cleanup techniques, LIGO Scientific Collaboration workflows require additional restructuring to achieve a 56% reduction in data space usage. We also examine the cost of the workflow restructuring in terms of the applications runtime.

A Science Driven Production Cyberinfrastructure--the Open Science Grid

This article describes the Open Science Grid, a large distributed computational infrastructure in the United States which supports many different high-throughput scientific applications, and partners (federates) with other infrastructures nationally and internationally to form multi-domain integrated distributed systems for science. The Open Science Grid consortium not only provides services and software to an increasingly diverse set of scientific communities, but also fosters a collaborative team of practitioners and researchers who use, support and advance the state of the art in large-scale distributed computing. The scale of the infrastructure can be expressed by the daily throughput of around seven hundred thousand jobs, just under a million hours of computing, a million file transfers, and half a petabyte of data movement. In this paper we introduce and reflect on some of the OSG capabilities, usage and activities.

XSIL: Extensible Scientific Interchange Language

We motivate and define the XSIL language as a flexible, hierarchical, extensible transport language for scientific data objects. The entire object may be represented in the file, or there may be metadata in the XSIL file, with a powerful, fault-tolerant linking mechanism to external data. The language is based on XML, and is designed not only for parsing and processing by machines, but also for presentation to humans through web browsers and web-database technology. There is a natural mapping between the elements of the XSIL language and the object model into which they are translated by the parser. As well as common objects (Parameter, Array, Time, Table), we have extended XSIL to include the IGWDFrame, used by gravitational-wave observatories.

Robust parameter estimation for compact binaries with ground-based gravitational-wave observations using the LALInference software library

The Advanced LIGO and Advanced Virgo gravitational-wave (GW) detectors will begin operation in the coming years, with compact binary coalescence events a likely source for the first detections. The gravitational waveforms emitted directly encode information about the sources, including the masses and spins of the compact objects. Recovering the physical parameters of the sources from the GW observations is a key analysis task. This work describes the LALInference software library for Bayesian parameter estimation of compact binary signals, which builds on several previous methods to provide a well-tested toolkit which has already been used for several studies. We show that our implementation is able to correctly recover the parameters of compact binary signals from simulated data from the advanced GW detectors. We demonstrate this with a detailed comparison on three compact binary systems: a binary neutron star, a neutron star–black hole binary and a binary black hole, where we show a cross comparison of results obtained using three independent sampling algorithms. These systems were analyzed with nonspinning, aligned spin and generic spin configurations respectively, showing that consistent results can be obtained even with the full 15-dimensional parameter space of the generic spin configurations. We also demonstrate statistically that the Bayesian credible intervals we recover correspond to frequentist confidence intervals under correct prior assumptions by analyzing a set of 100 signals drawn from the prior. We discuss the computational cost of these algorithms, and describe the general and problem-specific sampling techniques we have used to improve the efficiency of sampling the compact binary coalescence parameter space.