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Daniel S. Katz

Publications and source records attributed to Daniel S. Katz.

At least 73 records · Page 4Linked to original sources
arXiv2019

Supporting High-Performance and High-Throughput Computing for Experimental Science

The advent of experimental science facilities-instruments and observatories, such as the Large Hadron Collider, the Laser Interferometer Gravitational Wave Observatory, and the upcoming Large Synoptic Survey Telescope-has brought about challenging, large-scale computational and data processing requirements. Traditionally, the computing infrastructure to support these facility's requirements were organized into separate infrastructure that supported their high-throughput needs and those that supported their high-performance computing needs. We argue that to enable and accelerate scientific discovery at the scale and sophistication that is now needed, this separation between high-performance computing and high-throughput computing must be bridged and an integrated, unified infrastructure provided. In this paper, we discuss several case studies where such infrastructure has been implemented. These case studies span different science domains, software systems, and application requirements as well as levels of sustainability. A further aim of this paper is to provide a basis to determine the common characteristics and requirements of such infrastructure, as well as to begin a discussion of how best to support the computing requirements of existing and future experimental science facilities.

cs.DC↗
arXiv2019

Deep Learning for Multi-Messenger Astrophysics: A Gateway for Discovery in the Big Data Era

This report provides an overview of recent work that harnesses the Big Data Revolution and Large Scale Computing to address grand computational challenges in Multi-Messenger Astrophysics, with a particular emphasis on real-time discovery campaigns. Acknowledging the transdisciplinary nature of Multi-Messenger Astrophysics, this document has been prepared by members of the physics, astronomy, computer science, data science, software and cyberinfrastructure communities who attended the NSF-, DOE- and NVIDIA-funded "Deep Learning for Multi-Messenger Astrophysics: Real-time Discovery at Scale" workshop, hosted at the National Center for Supercomputing Applications, October 17-19, 2018. Highlights of this report include unanimous agreement that it is critical to accelerate the development and deployment of novel, signal-processing algorithms that use the synergy between artificial intelligence (AI) and high performance computing to maximize the potential for scientific discovery with Multi-Messenger Astrophysics. We discuss key aspects to realize this endeavor, namely (i) the design and exploitation of scalable and computationally efficient AI algorithms for Multi-Messenger Astrophysics; (ii) cyberinfrastructure requirements to numerically simulate astrophysical sources, and to process and interpret Multi-Messenger Astrophysics data; (iii) management of gravitational wave detections and triggers to enable electromagnetic and astro-particle follow-ups; (iv) a vision to harness future developments of machine and deep learning and cyberinfrastructure resources to cope with the scale of discovery in the Big Data Era; (v) and the need to build a community that brings domain experts together with data scientists on equal footing to maximize and accelerate discovery in the nascent field of Multi-Messenger Astrophysics.

astro-ph.IM↗
arXiv2019

HEP Software Foundation Community White Paper Working Group - Training, Staffing and Careers

The rapid evolution of technology and the parallel increasing complexity of algorithmic analysis in HEP requires developers to acquire a much larger portfolio of programming skills. Young researchers graduating from universities worldwide currently do not receive adequate preparation in the very diverse fields of modern computing to respond to growing needs of the most advanced experimental challenges. There is a growing consensus in the HEP community on the need for training programmes to bring researchers up to date with new software technologies, in particular in the domains of concurrent programming and artificial intelligence. We review some of the initiatives under way for introducing new training programmes and highlight some of the issues that need to be taken into account for these to be successful.

physics.ed-ph↗
arXiv2018

Community Organizations: Changing the Culture in Which Research Software Is Developed and Sustained

Software is the key crosscutting technology that enables advances in mathematics, computer science, and domain-specific science and engineering to achieve robust simulations and analysis for science, engineering, and other research fields. However, software itself has not traditionally received focused attention from research communities; rather, software has evolved organically and inconsistently, with its development largely as by-products of other initiatives. Moreover, challenges in scientific software are expanding due to disruptive changes in computer hardware, increasing scale and complexity of data, and demands for more complex simulations involving multiphysics, multiscale modeling and outer-loop analysis. In recent years, community members have established a range of grass-roots organizations and projects to address these growing technical and social challenges in software productivity, quality, reproducibility, and sustainability. This article provides an overview of such groups and discusses opportunities to leverage their synergistic activities while nurturing work toward emerging software ecosystems.

cs.CY↗
arXiv2018

Enforcing public data archiving policies in academic publishing: A study of ecology journals

To improve the quality and efficiency of research, groups within the scientific community seek to exploit the value of data sharing. Funders, institutions, and specialist organizations are developing and implementing strategies to encourage or mandate data sharing within and across disciplines, with varying degrees of success. Academic journals in ecology and evolution have adopted several types of public data archiving policies requiring authors to make data underlying scholarly manuscripts freely available. Yet anecdotes from the community and studies evaluating data availability suggest that these policies have not obtained the desired effects, both in terms of quantity and quality of available datasets. We conducted a qualitative, interview-based study with journal editorial staff and other stakeholders in the academic publishing process to examine how journals enforce data archiving policies. We specifically sought to establish who editors and other stakeholders perceive as responsible for ensuring data completeness and quality in the peer review process. Our analysis revealed little consensus with regard to how data archiving policies should be enforced and who should hold authors accountable for dataset submissions. Themes in interviewee responses included hopefulness that reviewers would take the initiative to review datasets and trust in authors to ensure the completeness and quality of their datasets. We highlight problematic aspects of these thematic responses and offer potential starting points for improvement of the public data archiving process.

cs.CY↗
arXiv2018

Mapping the research software sustainability space

A growing number of largely uncoordinated initiatives focus on research software sustainability. A comprehensive mapping of the research software sustainability space can help identify gaps in their efforts, track results, and avoid duplication of work. To this end, this paper suggests enhancing an existing schematic of activities in research software sustainability, and formalizing it in a directed graph model. Such a model can be further used to define a classification schema which, applied to research results in the field, can drive the identification of past activities and the planning of future efforts.

cs.SE↗
arXiv2018

Software Citation in Theory and Practice

In most fields, computational models and data analysis have become a significant part of how research is performed, in addition to the more traditional theory and experiment. Mathematics is no exception to this trend. While the system of publication and credit for theory and experiment (journals and books, often monographs) has developed and has become an expected part of the culture, how research is shared and how candidates for hiring, promotion are evaluated, software (and data) do not have the same history. A group working as part of the FORCE11 community developed a set of principles for software citation that fit software into the journal citation system, allow software to be published and then cited, and there are now over 50,000 DOIs that have been issued for software. However, some challenges remain, including: promoting the idea of software citation to developers and users; collaborating with publishers to ensure that systems collect and retain required metadata; ensuring that the rest of the scholarly infrastructure, particularly indexing sites, include software; working with communities so that software efforts "count" and understanding how best to cite software that has not been published.

cs.CY↗
arXiv2018

The State of Sustainable Research Software: Results from the Workshop on Sustainable Software for Science: Practice and Experiences (WSSSPE5.1)

This article summarizes motivations, organization, and activities of the Workshop on Sustainable Software for Science: Practice and Experiences (WSSSPE5.1) held in Manchester, UK in September 2017. The WSSSPE series promotes sustainable research software by positively impacting principles and best practices, careers, learning, and credit. This article discusses the Code of Conduct, idea papers, position papers, experience papers, demos, and lightning talks presented during the workshop. The main part of the article discusses the speed-blogging groups that formed during the meeting, along with the outputs of those sessions.

cs.SE↗
arXiv2018

HEP Software Foundation Community White Paper Working Group - Software Development, Deployment and Validation

The High Energy Phyiscs community has developed and needs to maintain many tens of millions of lines of code and to integrate effectively the work of thousands of developers across large collaborations. Software needs to be built, validated, and deployed across hundreds of sites. Software also has a lifetime of many years, frequently beyond that of the original developer, it must be developed with sustainability in mind. Adequate recognition of software development as a critical task in the HEP community needs to be fostered and an appropriate publication and citation strategy needs to be developed. As part of the HEP Softare Foundation's Community White Paper process a working group on Software Development, Deployment and Validation was formed to examine all of these issues, identify best practice and to formulare recommendations for the next decade. Its report is presented here.

physics.comp-ph↗
arXiv2018

HEP Software Foundation Community White Paper Working Group - Data Analysis and Interpretation

At the heart of experimental high energy physics (HEP) is the development of facilities and instrumentation that provide sensitivity to new phenomena. Our understanding of nature at its most fundamental level is advanced through the analysis and interpretation of data from sophisticated detectors in HEP experiments. The goal of data analysis systems is to realize the maximum possible scientific potential of the data within the constraints of computing and human resources in the least time. To achieve this goal, future analysis systems should empower physicists to access the data with a high level of interactivity, reproducibility and throughput capability. As part of the HEP Software Foundation Community White Paper process, a working group on Data Analysis and Interpretation was formed to assess the challenges and opportunities in HEP data analysis and develop a roadmap for activities in this area over the next decade. In this report, the key findings and recommendations of the Data Analysis and Interpretation Working Group are presented.

physics.comp-ph↗
arXiv2018

BioWorkbench: A High-Performance Framework for Managing and Analyzing Bioinformatics Experiments

Advances in sequencing techniques have led to exponential growth in biological data, demanding the development of large-scale bioinformatics experiments. Because these experiments are computation- and data-intensive, they require high-performance computing (HPC) techniques and can benefit from specialized technologies such as Scientific Workflow Management Systems (SWfMS) and databases. In this work, we present BioWorkbench, a framework for managing and analyzing bioinformatics experiments. This framework automatically collects provenance data, including both performance data from workflow execution and data from the scientific domain of the workflow application. Provenance data can be analyzed through a web application that abstracts a set of queries to the provenance database, simplifying access to provenance information. We evaluate BioWorkbench using three case studies: SwiftPhylo, a phylogenetic tree assembly workflow; SwiftGECKO, a comparative genomics workflow; and RASflow, a RASopathy analysis workflow. We analyze each workflow from both computational and scientific domain perspectives, by using queries to a provenance and annotation database. Some of these queries are available as a pre-built feature of the BioWorkbench web application. Through the provenance data, we show that the framework is scalable and achieves high-performance, reducing up to 98% of the case studies execution time. We also show how the application of machine learning techniques can enrich the analysis process.

cs.DC↗
arXiv2017

BOSS-LDG: A Novel Computational Framework that Brings Together Blue Waters, Open Science Grid, Shifter and the LIGO Data Grid to Accelerate Gravitational Wave Discovery

We present a novel computational framework that connects Blue Waters, the NSF-supported, leadership-class supercomputer operated by NCSA, to the Laser Interferometer Gravitational-Wave Observatory (LIGO) Data Grid via Open Science Grid technology. To enable this computational infrastructure, we configured, for the first time, a LIGO Data Grid Tier-1 Center that can submit heterogeneous LIGO workflows using Open Science Grid facilities. In order to enable a seamless connection between the LIGO Data Grid and Blue Waters via Open Science Grid, we utilize Shifter to containerize LIGO's workflow software. This work represents the first time Open Science Grid, Shifter, and Blue Waters are unified to tackle a scientific problem and, in particular, it is the first time a framework of this nature is used in the context of large scale gravitational wave data analysis. This new framework has been used in the last several weeks of LIGO's second discovery campaign to run the most computationally demanding gravitational wave search workflows on Blue Waters, and accelerate discovery in the emergent field of gravitational wave astrophysics. We discuss the implications of this novel framework for a wider ecosystem of Higher Performance Computing users.

cs.DC↗
arXiv2017

Understanding Software in Research: Initial Results from Examining Nature and a Call for Collaboration

This lightning talk paper discusses an initial data set that has been gathered to understand the use of software in research, and is intended to spark wider interest in gathering more data. The initial data analyzes three months of articles in the journal Nature for software mentions. The wider activity that we seek is a community effort to analyze a wider set of articles, including both a longer timespan of Nature articles as well as articles in other journals. Such a collection of data could be used to understand how the role of software has changed over time and how it varies across fields.

cs.SE↗
arXiv2017

Report on the Fourth Workshop on Sustainable Software for Science: Practice and Experiences (WSSSPE4)

This report records and discusses the Fourth Workshop on Sustainable Software for Science: Practice and Experiences (WSSSPE4). The report includes a description of the keynote presentation of the workshop, the mission and vision statements that were drafted at the workshop and finalized shortly after it, a set of idea papers, position papers, experience papers, demos, and lightning talks, and a panel discussion. The main part of the report covers the set of working groups that formed during the meeting, and for each, discusses the participants, the objective and goal, and how the objective can be reached, along with contact information for readers who may want to join the group. Finally, we present results from a survey of the workshop attendees.

cs.SE↗
arXiv2017

Streaming supercomputing needs workflow-enabled programming-in-the-large

This is a position paper, submitted to the Future Online Analysis Platform Workshop (https://press3.mcs.anl.gov/futureplatform/), which argues that simple data analysis applications are common today, but future online supercomputing workloads will need to couple multiple advanced technologies (streams, caches, analysis, and simulations) to rapidly deliver scientific results. Each of these technologies are active research areas when integrated with high-performance computing. These components will interact in complex ways, therefore coupling them needs to be programmed. Programming in the large, on top of existing applications, enables us to build much more capable applications and to productively manage this complexity.

cs.DC↗
arXiv2016

Introducing Distributed Dynamic Data-intensive (D3) Science: Understanding Applications and Infrastructure

A common feature across many science and engineering applications is the amount and diversity of data and computation that must be integrated to yield insights. Data sets are growing larger and becoming distributed; and their location, availability and properties are often time-dependent. Collectively, these characteristics give rise to dynamic distributed data-intensive applications. While "static" data applications have received significant attention, the characteristics, requirements, and software systems for the analysis of large volumes of dynamic, distributed data, and data-intensive applications have received relatively less attention. This paper surveys several representative dynamic distributed data-intensive application scenarios, provides a common conceptual framework to understand them, and examines the infrastructure used in support of applications.

cs.DC↗
arXiv2016

Integrating Abstractions to Enhance the Execution of Distributed Applications

One of the factors that limits the scale, performance, and sophistication of distributed applications is the difficulty of concurrently executing them on multiple distributed computing resources. In part, this is due to a poor understanding of the general properties and performance of the coupling between applications and dynamic resources. This paper addresses this issue by integrating abstractions representing distributed applications, resources, and execution processes into a pilot-based middleware. The middleware provides a platform that can specify distributed applications, execute them on multiple resource and for different configurations, and is instrumented to support investigative analysis. We analyzed the execution of distributed applications using experiments that measure the benefits of using multiple resources, the late-binding of scheduling decisions, and the use of backfill scheduling.

cs.DC↗