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

   https://doi.org/10.1007/978-981-13-7729-7_8  

   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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4. 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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5. High-throughput density functional theory screening of double transition metal MXene precursors

   https://doi.org/10.1038/s41597-023-02755-2  

   Nykiel, Kat; Strachan, Alejandro (November 2023, Scientific Data)

   Abstract MXenes are an emerging class of 2D materials of interest in applications ranging from energy storage to electromagnetic shielding. MXenes are synthesized by selective etching of layered bulk MAX phases into sheets of 2D MXenes. Their chemical tunability has been significantly expanded with the successful synthesis of double transition metal MXenes. While knowledge of the structure and energetics of double transition metal MAX phases is critical to designing and optimizing new MXenes, only a small subset of these materials been explored. We present a comprehensive dataset of key properties of MAX phases obtained using density functional theory within the generalized gradient approximation exchange-correlation functionals. Energetics and structure of 8,712 MAX phases have been calculated and stored in a queryable, open database hosted at nanoHUB. 

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