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1. Scalable Software Infrastructure for Integrating Supercomputing with Volunteer Computing and Cloud Computing

   Ritu Arora, Carlos Redondo ( April 2019 , Communications in computer and information science)

   Volunteer Computing (VC) is a computing model that uses donated computing cycles on the devices such as laptops, desktops, and tablets to do scientific computing. BOINC is the most popular software framework for VC and it helps in connecting the projects needing computing cycles with the volunteers interested in donating the computing cycles on their resources. It has already enabled projects with high societal impact to harness several PetaFLOPs of donated computing cycles. Given its potential in elastically augmenting the capacity of existing supercomputing resources for running High-Throughput Computing (HTC) jobs, we have extended the BOINC software infrastructure and have made it amenable for integration with the supercomputing and cloud computing environments. We have named the extension of the BOINC software infrastructure as BOINC@TACC, and are using it to route *qualified* HTC jobs from the supercomputers at the Texas Advanced Computing Center (TACC) to not only the typically volunteered devices but also to the cloud computing resources such as Jetstream and Chameleon. BOINC@TACC can be extremely useful for those researchers/scholars who are running low on allocations of compute-cycles on the supercomputers, or are interested in reducing the turnaround time of their HTC jobs when the supercomputers are over-subscribed. We have also developed a web-application for TACC users so that, through the convenience of their web-browser, they can submit their HTC jobs for running on the resources volunteered by the community. An overview of the BOINC@TACC project is presented in this paper. The BOINC@TACC software infrastructure is open-source and can be easily adapted for use by other supercomputing centers that are interested in building their volunteer community and connecting them with the researchers needing multi-petascale (and even exascale) computing power for their HTC jobs 

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2. nanoHUB@home: Expanding nanoHUBThroughVolunteer Computing

   Clark, Steven ; Haley, Benjamin ; Hunt, Martin ; Denny, Nathan ; Anderson, David ( October 2019 , Gateways 2019)

   Volunteer computing (VC) uses consumer digital electronics products, such as PCs, mobile devices, and game consoles, for high-throughput scientific computing. Device owners participate in VC by installing a program which, in the background, downloads and executes jobs from servers operated by science projects. Most VC projects use BOINC, an open-source middleware system for VC. BOINC allows scientists create and operate VC projects and enables volunteers to participate in these projects. Volunteers install a single application (the BOINC client) and then choose projects to support. We have developed a BOINC project, nanoHUB@home, to make use of VC in support of the nanoHUB science gateway. VC has greatly expanded the computational resources available for nanoHUB simulations. We are using VC to support “speculative exploration”, a model of computing that explores the input parameters of online simulation tools published through the nanoHUB gateway, pre-computing results that have not been requested by users. These results are stored in a cache, and when a user launches an interactive simulation our system first checks the cache. If the result is already available it is returned to the user immediately, leaving the computational resources free and not re-computing existing results. The cache is also useful for machine learning (ML) studies, building surrogate models for nanoHUB simulation tools that allow us to quickly estimate results before running an expensive simulation. VC resources also allow us to support uncertainty quantification (UQ) in nanoHUB simulation tools, to go beyond simulations and deliver real-world predictions. Models are typically simulated with precise input values, but real-world experiments involve imprecise values for device measurements, material properties, and stimuli. The imprecise values can be expressed as a probability distribution of values, such as a Gaussian distribution with a mean and standard deviation, or an actual distribution measured from experiments. Stochastic collocation methods can be used to predict the resulting outputs given a series of probability distributions for inputs. These computations require hundreds or thousands of simulation runs for each prediction. This workload is well-suited to VC, since the runs are completely separate, but the results of all runs are combined in a statistical analysis. 

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3. nanoHUB@home: Expanding nanoHUB through Volunteer Computing

   Haley, Benjamin ; Clark, Steven ; Denny, Nathan ; Desai, Saaketh ; Hunt, Marton ; Anderson, David ( September 2019 , Gateways 2019)

   Volunteer computing (VC) uses consumer digital electronics products, such as PCs, mobile devices, and game consoles, for high-throughput scientific computing. Device owners participate in VC by installing a program which, in the background, downloads and executes jobs from servers operated by science projects. Most VC projects use BOINC, an open-source middleware system for VC. BOINC allows scientists create and operate VC projects and enables volunteers to participate in these projects. Volunteers install a single application (the BOINC client) and then choose projects to support. We have developed a BOINC project, nanoHUB@home, to make use of VC in support of the nanoHUB science gateway. VC has greatly expanded the computational resources available for nanoHUB simulations. We are using VC to support “speculative exploration”, a model of computing that explores the input parameters of online simulation tools published through the nanoHUB gateway, pre-computing results that have not been requested by users. These results are stored in a cache, and when a user launches an interactive simulation our system first checks the cache. If the result is already available it is returned to the user immediately, leaving the computational resources free and not re-computing existing results. The cache is also useful for machine learning (ML) studies, building surrogate models for nanoHUB simulation tools that allow us to quickly estimate results before running an expensive simulation. VC resources also allow us to support uncertainty quantification (UQ) in nanoHUB simulation tools, to go beyond simulations and deliver real-world predictions. Models are typically simulated with precise input values, but real-world experiments involve imprecise values for device measurements, material properties, and stimuli. The imprecise values can be expressed as a probability distribution of values, such as a Gaussian distribution with a mean and standard deviation, or an actual distribution measured from experiments. Stochastic collocation methods can be used to predict the resulting outputs given a series of probability distributions for inputs. These computations require hundreds or thousands of simulation runs for each prediction. This workload is well-suited to VC, since the runs are completely separate, but the results of all runs are combined in a statistical analysis. 

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4. Greyfish: An Out-of-the-Box, Reusable, Portable Cloud Storage Service

   https://doi.org/10.1145/3332186.3333055  

   Redondo, Carlos ; Arora, Ritu ( July 2019 , PEARC '19: Proceedings of the Practice and Experience in Advanced Research Computing on Rise of the Machines (learning))

   A scalable storage system is an integral requirement for supporting large-scale cloud computing jobs. The raw space on storage systems is made usable with the help of a software layer which is typically called a filesystem (e.g., Google's Cloud Filestore). In this paper, we present the design and implementation of an open-source and free cloud-based filesystem named as "Greyfish" that can be installed on the Virtual Machines (VMs) hosted on different cloud computing systems, such as Jetstream and Chameleon. Greyfish helps in: (1) storing files and directories for different user-accounts in a shared space on the cloud, (2) managing file-access permissions, and (3) purging files when needed. It is currently being used in the implementation of the Gateway-In-A-Box (GIB) project. A simplified version of Greyfish, known as Reef, is already in production in the BOINC@TACC project. Science gateway developers will find Greyfish useful for creating local filesystems that can be mounted in containers. By doing so, they can independently do quick installations of self-contained software solutions in development and test environments while mounting the filesystems on large-scale storage platforms in the production environments only. 

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5. SciTokens: Capability-Based Secure Access to Remote Scientific Data

   https://doi.org/10.1145/3219104. 3219135  

   Withers, Alex ; Bockelman, Brian ; Weitzel, Derek ; Brown, Duncan ; Gaynor, Jeff ; Basney, Jim ; Tannenbaum, Todd ; Miller, Zach ( July 2018 , PEARC ’18: Practice and Experience in Advanced Research Computing)

   The management of security credentials (e.g., passwords, secret keys) for computational science workflows is a burden for scientists and information security officers. Problems with credentials (e.g., expiration, privilege mismatch) cause workflows to fail to fetch needed input data or store valuable scientific results, distracting scientists from their research by requiring them to diagnose the problems, re-run their computations, and wait longer for their results. In this paper, we introduce SciTokens, open source software to help scientists manage their security credentials more reliably and securely. We describe the SciTokens system architecture, design, and implementation addressing use cases from the Laser Interferometer Gravitational-Wave Observatory (LIGO) Scientific Collaboration and the Large Synoptic Survey Telescope (LSST) projects. We also present our integration with widely-used software that supports distributed scientific computing, including HTCondor, CVMFS, and XrootD. SciTokens uses IETF-standard OAuth tokens for capability-based secure access to remote scientific data. The access tokens convey the specific authorizations needed by the workflows, rather than general-purpose authentication impersonation credentials, to address the risks of scientific workflows running on distributed infrastructure including NSF resources (e.g., LIGO Data Grid, Open Science Grid, XSEDE) and public clouds (e.g., Amazon Web Services, Google Cloud, Microsoft Azure). By improving the interoperability and security of scientific workflows, SciTokens 1) enables use of distributed computing for scientific domains that require greater data protection and 2) enables use of more widely distributed computing resources by reducing the risk of credential abuse on remote systems. 

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