Gaurang Mehta

Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data.

EDBT 2026 Demo / reviewers in the wild / expert

dblp:m/GaurangMehta · DBLP profile ↗

← Back

22ranked-venue papers

0as first author

0since 2021 · last 2013

—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Systems, architecture and hardware · 11Applied, interdisciplinary, general and emerging computing · 9Software engineering, systems software and programming languages · 8Artificial intelligence and machine learning · 1Graphics, computer vision, multimedia, augmented reality and games · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer architecture, parallel and distributed computing, and storage systems 6 papers		High-performance computing · 40% Distributed systems · 26% Storage systems · 18%
Interdisciplinary, comprehensive, and emerging computing 2 papers		Bioinformatics and computational biology · 82% Computational science and engineering · 18%
Artificial intelligence 1 paper		Knowledge representation and reasoning · 100%

Topics — the 15 heaviest of 17, each with the papers that count most for it

Topic	Weight	Papers	Last	Evidence papers
High-performance computing scientific workflow	0.2	2	2010	Data Sharing Options for Scientific Workflows on Amazon EC2 · SC 2010 An integrated framework for performance-based optimization of scientific workflows · HPDC 2009
Bioinformatics and computational biology › transcriptomics RNA-seq analysis	0.1	1	2011	RseqFlow: workflows for RNA-Seq data analysis · Bioinform. 2011
High-performance computing › scientific workflow scientific workflow execution	0.1	1	2011	RseqFlow: workflows for RNA-Seq data analysis · Bioinform. 2011
Distributed systems workflow management	0.1	1	2011	RseqFlow: workflows for RNA-Seq data analysis · Bioinform. 2011
Cloud and datacenter computing cloud storage	0.1	1	2010	Data Sharing Options for Scientific Workflows on Amazon EC2 · SC 2010
Storage systems data management	0.1	1	2010	Data Sharing Options for Scientific Workflows on Amazon EC2 · SC 2010
Distributed systems › resource sharing data sharing	0.1	1	2010	Data Sharing Options for Scientific Workflows on Amazon EC2 · SC 2010
Storage systems › file systems distributed file system	0.1	1	2010	Data Sharing Options for Scientific Workflows on Amazon EC2 · SC 2010
Performance modeling and evaluation parameter space exploration	0.1	1	2009	An integrated framework for performance-based optimization of scientific workflows · HPDC 2009
High-performance computing › scientific workflow workflow optimization	0.1	1	2009	An integrated framework for performance-based optimization of scientific workflows · HPDC 2009
Distributed systems grid computing	0.0	1	2004	The Grid2003 Production Grid: Principles and Practice · HPDC 2004
Distributed systems distributed data processing	0.0	1	2002	GriPhyN and LIGO, Building a Virtual Data Grid for Gravitational Wave Scientists · HPDC 2002
High-performance computing scientific computing systems	0.0	2	2007	Wings for Pegasus: Creating Large-Scale Scientific Applications Using Semantic Representations of Computational Workflows · AAAI 2007 GriPhyN and LIGO, Building a Virtual Data Grid for Gravitational Wave Scientists · HPDC 2002
Computational science and engineering spatial data analysis	0.0	1	2009	An integrated framework for performance-based optimization of scientific workflows · HPDC 2009
High-performance computing distributed computing infrastructure	0.0	1	2004	The Grid2003 Production Grid: Principles and Practice · HPDC 2004

Methods — techniques the papers use, named apart from their topics

performance-based optimization · 0.2workflow generation · 0.1shared storage · 0.1file transfer · 0.1grid middleware · 0.0

Year	Publication	Venue	Position
2013	Characterizing and profiling scientific workflows Gideon Juve, Ann L. Chervenak, Ewa Deelman, Shishir Bharathi, Gaurang Mehta, Karan Vahi	Future Gener. Comput. Syst.	5
2013	A Case Study into Using Common Real-Time Workflow Monitoring Infrastructure for Scientific Workflows Karan Vahi, Ian Harvey, Taghrid Samak, Dan Gunter, Kieran Evans, David Rogers, Ian J. Taylor, Monte Goode, Fabio Silva, Eddie Al-Shakarchi, Gaurang Mehta, Ewa Deelman, Andrew C. Jones	J. Grid Comput.	11
2011	Online workflow management and performance analysis with Stampede Dan Gunter, Ewa Deelman, Taghrid Samak, Christopher X. Brooks, Monte Goode, Gideon Juve, Gaurang Mehta, Priscilla Moraes, Fabio Silva, D. Martin Swany, Karan Vahi	CNSM	7
2011	A Cloud-based Dynamic Workflow for Mass Spectrometry Data Analysis abstract There is a growing interest in the use of cloud computing for scientific applications, including scientific workflows. Key attractions of the cloud include the pay-as-you-go model and elasticity. While the elasticity offered by clouds can be beneficial for many applications and use-scenarios, it also imposes significant challenges in the development of applications or services. For example, no general framework exists that can enable a scientific workflow to execute in a dynamic fashion, i.e. exploiting elasticity of clouds and automatically allocating and deal locating resources to meet time and/or cost constraints. This paper presents a case-study in creating a dynamic cloud workflow implementation of a scientific application. We work with Mass Matrix, an application which searches proteins and peptides from tandem mass spectrometry data. In order to use cloud resources, we first parallelize the search method used in this algorithm. Next, we create a flexible workflow using the Pegasus Workflow Management System. Finally, we add a new dynamic resource allocation module, which can use fewer or a larger number of resources based on a time constraint specified by the user. We evaluate our implementation using several different datasets, and show that the application scales quite well, and that our dynamic framework is effective in meeting time constraints. Ashish Nagavaram, Gagan Agrawal, Michael A. Freitas, Kelly H. Telu, Gaurang Mehta, Rajiv Mayani, Ewa Deelman	eScience	5
2011	Experiences Using GlideinWMS and the Corral Frontend across Cyberinfrastructures abstract 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. Mats Rynge, Gideon Juve, Gaurang Mehta, Ewa Deelman, Krista Larson, Burt Holzman, Igor Sfiligoi, Frank Würthwein, G. Bruce Berriman, Scott Callaghan	eScience	3
2011	Online Fault and Anomaly Detection for Large-Scale Scientific Workflows abstract Scientific workflows are an enabler of complex scientific analyses. Large-scale scientific workflows are executed on complex parallel and distributed resources, where many things can fail. Application scientists need to track the status of their workflows in real time, detect execution anomalies automatically, and perform troubleshooting -- without logging into remote nodes or searching through thousands of log files. As part of the NSF-funded Synthesized Tools for Archiving Monitoring Performance and Enhanced DEbugging (STAMPEDE) project, we have developed an infrastructure to answer these needs by integrating detailed workflow and resource monitoring. On top of this infrastructure, we have developed analysis techniques for online detection of a wide variety of "hard" and "soft" types of failures. We use these detected failures to derive higher-level statistics about the status of the resources and the workflow as a whole. In this paper, we describe our techniques and evaluate their effectiveness in the context of real application logs. Taghrid Samak, Dan Gunter, Monte Goode, Ewa Deelman, Gideon Juve, Gaurang Mehta, Fabio Silva, Karan Vahi	HPCC	6
2011	RseqFlow: workflows for RNA-Seq data analysis abstract SUMMARY: We have developed an RNA-Seq analysis workflow for single-ended Illumina reads, termed RseqFlow. This workflow includes a set of analytic functions, such as quality control for sequencing data, signal tracks of mapped reads, calculation of expression levels, identification of differentially expressed genes and coding SNPs calling. This workflow is formalized and managed by the Pegasus Workflow Management System, which maps the analysis modules onto available computational resources, automatically executes the steps in the appropriate order and supervises the whole running process. RseqFlow is available as a Virtual Machine with all the necessary software, which eliminates any complex configuration and installation steps. AVAILABILITY AND IMPLEMENTATION: http://genomics.isi.edu/rnaseq CONTACT: [email protected]; [email protected]; [email protected]; [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Gaurang Mehta, Rajiv Mayani, Jingxi Lu, Tade Souaiaia, Yangho Chen, Andrew P. Clark, Hee Jae Yoon, Oleg V. Evgrafov, James A. Knowles, Ewa Deelman	Bioinform.	2
2010	Data Sharing Options for Scientific Workflows on Amazon EC2 abstract Efficient data management is a key component in achieving good performance for scientific workflows in distributed environments. Workflow applications typically communicate data between tasks using files. When tasks are distributed, these files are either transferred from one computational node to another, or accessed through a shared storage system. In grids and clusters, workflow data is often stored on network and parallel file systems. In this paper we investigate some of the ways in which data can be managed for workflows in the cloud. We ran experiments using three typical workflow applications on Amazon's EC2. We discuss the various storage and file systems we used, describe the issues and problems we encountered deploying them on EC2, and analyze the resulting performance and cost of the workflows. Gideon Juve, Ewa Deelman, Karan Vahi, Gaurang Mehta, G. Bruce Berriman, Benjamin P. Berman, Philip Maechling	SC	4
2009	An integrated framework for performance-based optimization of scientific workflows abstract Data analysis processes in scientific applications can be expressed as coarse-grain workflows of complex data processing operations with data flow dependencies between them. Performance optimization of these workflows can be viewed as a search for a set of optimal values in a multi-dimensional parameter space. While some performance parameters such as grouping of workflow components and their mapping to machines do not a ect the accuracy of the output, others may dictate trading the output quality of individual components (and of the whole workflow) for performance. This paper describes an integrated framework which is capable of supporting performance optimizations along multiple dimensions of the parameter space. Using two real-world applications in the spatial data analysis domain, we present an experimental evaluation of the proposed framework. Vijay S. Kumar, P. Sadayappan, Gaurang Mehta, Karan Vahi, Ewa Deelman, Varun Ratnakar, Jihie Kim, Yolanda Gil, Mary W. Hall, Tahsin M. Kurç, Joel H. Saltz	HPDC	3
2009	Adaptive workflow processing and execution in Pegasus abstract Abstract Workflows are widely used in applications that require coordinated use of computational resources. Workflow definition languages typically abstract over some aspects of the way in which a workflow is to be executed, such as the level of parallelism to be used or the physical resources to be deployed. As a result, a workflow management system has the responsibility of establishing how best to execute a workflow given the available resources. The Pegasus workflow management system compiles abstract workflows into concrete execution plans, and has been widely used in large‐scale e‐Science applications. This paper describes an extension to Pegasus whereby resource allocation decisions are revised during workflow evaluation, in the light of feedback on the performance of jobs at runtime. The contributions of this paper include: (i) a description of how adaptive processing has been retrofitted to an existing workflow management system; (ii) a scheduling algorithm that allocates resources based on runtime performance; and (iii) an experimental evaluation of the resulting infrastructure using grid middleware over clusters. Copyright © 2009 John Wiley & Sons, Ltd. Kevin Lee 0006, Norman W. Paton, Rizos Sakellariou, Ewa Deelman, Alvaro A. A. Fernandes, Gaurang Mehta	Concurr. Comput. Pract. Exp.	6
2008	Estimating Resource Needs for Time-Constrained Workflows abstract Workflow technologies have become a major vehicle for the easy and efficient development of science applications. At the same time new computing environments such as the Cloud are now available. A challenge is to determine the right amount of resources to provision for an application. This paper introduces an algorithm named balanced time scheduling (BTS), which estimates the minimum number of virtual processors required to execute a workflow within a user-specified finish time. The resource estimate of BTS is abstract, so it can be easily integrated with any resource description language or any resource provisioning system. The experimental results with a number of synthetic workflows demonstrate that BTS can estimate the computing capacity close to the optimal. The algorithm is scalable so that its turnaround time is only tens of seconds even with workflows having thousands of tasks and edges. Eun-Kyu Byun, Yang-Suk Kee, Ewa Deelman, Karan Vahi, Gaurang Mehta, Jin-Soo Kim 0001	eScience	5
2008	Reducing Time-to-Solution Using Distributed High-Throughput Mega-Workflows - Experiences from SCEC CyberShake abstract Researchers at the Southern California Earthquake Center (SCEC) use large-scale grid-based scientific workflows to perform seismic hazard research as a part of SCEC's program of earthquake system science research. The scientific goal of the SCEC CyberShake project is to calculate probabilistic seismic hazard curves for sites in Southern California. For each site of interest, the CyberShake platform includes two large-scale MPI calculations and approximately 840,000 embarrassingly parallel post-processing jobs. In this paper, we describe the computational requirements of CyberShake and detail how we meet these requirements using grid-based, high-throughput, scientific workflow tools. We describe the specific challenges we encountered and we discuss workflow throughput optimizations we developed that reduced our time to solution by a factor of three and we present runtime statistics and propose further optimizations. Scott Callaghan, Philip Maechling, Ewa Deelman, Karan Vahi, Gaurang Mehta, Gideon Juve, Kevin Milner 0001, Robert Graves, Edward Field, David Okaya, Dan Gunter, Keith Beattie, Thomas H. Jordan	eScience	5
2008	On the Use of Cloud Computing for Scientific Workflows abstract This paper explores the use of cloud computing for scientific workflows, focusing on a widely used astronomy application-Montage. The approach is to evaluate from the point of view of a scientific workflow the tradeoffs between running in a local environment, if such is available, and running in a virtual environment via remote, wide-area network resource access. Our results show that for Montage, a workflow with short job runtimes, the virtual environment can provide good compute time performance but it can suffer from resource scheduling delays and widearea communications. Christina Hoffa, Gaurang Mehta, Timothy Freeman 0001, Ewa Deelman, Kate Keahey, G. Bruce Berriman, John Good	eScience	2
2008	Provenance trails in the Wings/Pegasus system abstract Abstract Our research focuses on creating and executing large‐scale scientific workflows that often involve thousands of computations over distributed, shared resources. We describe an approach to workflow creation and refinement that uses semantic representations to (1) describe complex scientific applications in a data‐independent manner, (2) automatically generate workflows of computations for given data sets, and (3) map the workflows to available computing resources for efficient execution. Our approach is implemented in the Wings/Pegasus workflow system and has been demonstrated in a variety of scientific application domains. This paper illustrates the application‐level provenance information generated Wings during workflow creation and the refinement provenance by the Pegasus mapping system for execution over grid computing environments. We show how this information is used in answering the queries of the First Provenance Challenge. Copyright © 2007 John Wiley & Sons, Ltd. Jihie Kim, Ewa Deelman, Yolanda Gil, Gaurang Mehta, Varun Ratnakar	Concurr. Comput. Pract. Exp.	4
2008	Special Issue: The First Provenance Challenge abstract Abstract The first Provenance Challenge was set up in order to provide a forum for the community to understand the capabilities of different provenance systems and the expressiveness of their provenance representations. To this end, a functional magnetic resonance imaging workflow was defined, which participants had to either simulate or run in order to produce some provenance representation, from which a set of identified queries had to be implemented and executed. Sixteen teams responded to the challenge, and submitted their inputs. In this paper, we present the challenge workflow and queries, and summarize the participants' contributions. Copyright © 2007 John Wiley & Sons, Ltd. Luc Moreau 0001, Bertram Ludäscher, Ilkay Altintas, Roger S. Barga, Shawn Bowers, Steven P. Callahan, George Chin, Ben Clifford, Shirley Cohen, Sarah Cohen Boulakia, Susan B. Davidson, Ewa Deelman, Luciano A. Digiampietri, Ian T. Foster, Juliana Freire, James Frew, Joe Futrelle, Tara Gibson, Yolanda Gil, Carole A. Goble, Jennifer Golbeck, Paul Groth, David A. Holland, Jihie Kim, David Koop, Ales Krenek, Timothy M. McPhillips, Gaurang Mehta, Simon Miles, Dominic Metzger, Steve Munroe, James D. Myers, Beth Plale, Norbert Podhorszki, Varun Ratnakar, Emanuele Santos, Carlos Scheidegger, Karen Schuchardt, Margo I. Seltzer, Yogesh L. Simmhan, Cláudio T. Silva, Peter Slaughter, Eric G. Stephan, Robert Stevens 0001, Daniele Turi, Huy T. Vo, Michael Wilde, Jun Zhao 0003, Yong Zhao 0009	Concurr. Comput. Pract. Exp.	29
2007	Wings for Pegasus: Creating Large-Scale Scientific Applications Using Semantic Representations of Computational Workflows Yolanda Gil, Varun Ratnakar, Ewa Deelman, Gaurang Mehta, Jihie Kim	AAAI	4
2007	Connecting Scientific Data to Scientific Experiments with Provenance abstract As scientific workflows and the data they operate on, grow in size and complexity, the task of defining how those workflows should execute (which resources to use, where the resources must be in readiness for processing etc.) becomes proportionally more difficult. While "workflow compilers", such as Pegasus, reduce this burden, a further problem arises: since specifying details of execution is now automatic, a workflow's results are harder to interpret, as they are partly due to specifics of execution. By automating steps between the experiment design and its results, we lose the connection between them, hindering interpretation of results. To reconnect the scientific data with the original experiment, we argue that scientists should have access to the full provenance of their data, including not only parameters, inputs and intermediary data, but also the abstract experiment, refined into a concrete execution by the "workflow compiler". In this paper, we describe preliminary work on adapting Pegasus to capture the process of workflow refinement in the PASOA provenance system. Simon Miles, Ewa Deelman, Paul Groth, Karan Vahi, Gaurang Mehta, Luc Moreau 0001	eScience	5
2006	Managing Large-Scale Workflow Execution from Resource Provisioning to Provenance Tracking: The CyberShake Example abstract 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. Ewa Deelman, Scott Callaghan, Edward Field, Hunter Francoeur, Robert Graves, Vipin Gupta, Thomas H. Jordan, Carl Kesselman, Philip Maechling, John Mehringer, Gaurang Mehta, David Okaya, Karan Vahi	e-Science	12
2006	Automating Climate Science: Large Ensemble Simulations on the TeraGrid with the GriPhyN Virtual Data System abstract Ensemble simulations are a promising technique for identifying the signal of atmospheric response to extra-tropical sea surface temperature variability with high statistical significance. The basic idea is to perform multiple simulations from slightly different initial conditions and then to study the average signal of the ensemble. A significant obstacle to performing such ensemble simulations is the bookkeeping required to prepare, execute, and track the progress of hundreds of different computations. We describe an ensemble simulation experiment in which the Fast Ocean Atmosphere Model was run on the U.S. TeraGrid. In this experiment, we used the GriPhyN Virtual Data System to manage our ensemble simulations and their execution on distributed resources, achieving dramatic (order-of-magnitude) reductions in turnaround time relative to previous manual experiments. Veronika Nefedova, Robert L. Jacob, Ian T. Foster, Yun Liu 0032, Ewa Deelman, Gaurang Mehta, Mei-Hui Su, Karan Vahi	e-Science	7
2004	The Grid2003 Production Grid: Principles and Practice Ian T. Foster, Jerry Gieraltowski, Scott Gose, Natalia Maltsev, Edward N. May, Alexis A. Rodriguez, Dinanath Sulakhe, A. Vaniachine, Jim Shank, Saul Youssef, David Adams, Richard Baker 0003, Wensheng Deng, Dantong Yu, Iosif Legrand, Conrad Steenberg, M. Anzar Afaq, Eileen Berman, James Annis, L. A. T. Bauerdick, Michael Ernst, Ian Fisk, Lisa Giacchetti, Gregory E. Graham, Anne Heavey, Joseph Kaiser, Nickolai Kuropatkin, Ruth Pordes, Vijay Sekhri, John Weigand, Yujun Wu, Keith Baker, Lawrence Sorrillo, John Huth, Matthew Allen, Leigh Grundhoefer, John Hicks, Fred Luehring, Steve Peck, Robert Quick, Stephen C. Simms, George Fekete, Jan vandenBerg, Kihyeon Cho, Kihwan Kwon, Dongchul Son, Hyoungwoo Park, Shane Canon, Keith R. Jackson, David E. Konerding, Jason Lee 0001, Doug Olson, Iwona Sakrejda, Brian Tierney, Mark Green 0001, Russ Miller, James Letts, Terrence Martin, David Bury, Catalin Dumitrescu, Daniel Engh, Robert W. Gardner, Marco Mambelli, Yuri Smirnov, Jens-S. Vöckler, Michael Wilde, Yong Zhao 0009, Paul Avery, Richard Cavanaugh, Bockjoo Kim, Craig Prescott, Jorge Rodríguez 0002, Andrew Zahn, Shawn McKee, Christopher T. Jordan, James E. Prewett, Timothy L. Thomas, Horst Severini, Ben Clifford, Ewa Deelman, Larry Flon, Carl Kesselman, Gaurang Mehta, Nosa Olomu, Karan Vahi, Kaushik De, Patrick McGuigan, Mark Sosebee, Dan Bradley, Peter Couvares, Alan DeSmet, Carey Kireyev, Erik Paulson 0001, Alain J. Roy, Scott Koranda, Brian Moe, Bobby Brown, Paul Sheldon	HPDC	87
2003	Mapping Abstract Complex Workflows onto Grid Environments Ewa Deelman, Jim Blythe, Yolanda Gil, Carl Kesselman, Gaurang Mehta, Karan Vahi, Kent Blackburn, Albert Lazzarini, Adam Arbree, Richard Cavanaugh, Scott Koranda	J. Grid Comput.	5
2002	GriPhyN and LIGO, Building a Virtual Data Grid for Gravitational Wave Scientists abstract 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. Ewa Deelman, Carl Kesselman, Gaurang Mehta, Leila Meshkat, Laura Pearlman, Kent Blackburn, Phil Ehrens, Albert Lazzarini, Roy Williams, Scott Koranda	HPDC	3