62 research outputs found
Distributed Computing in a Pandemic
The current COVID-19 global pandemic caused by the SARS-CoV-2 betacoronavirus has resulted in over a million deaths and is having a grave socio-economic impact, hence there is an urgency to find solutions to key research challenges. Much of this COVID-19 research depends on distributed computing. In this article, I review distributed architectures -- various types of clusters, grids and clouds -- that can be leveraged to perform these tasks at scale, at high-throughput, with a high degree of parallelism, and which can also be used to work collaboratively. High-performance computing (HPC) clusters will be used to carry out much of this work. Several bigdata processing tasks used in reducing the spread of SARS-CoV-2 require high-throughput approaches, and a variety of tools, which Hadoop and Spark offer, even using commodity hardware. Extremely large-scale COVID-19 research has also utilised some of the world's fastest supercomputers, such as IBM's SUMMIT -- for ensemble docking high-throughput screening against SARS-CoV-2 targets for drug-repurposing, and high-throughput gene analysis -- and Sentinel, an XPE-Cray based system used to explore natural products. Grid computing has facilitated the formation of the world's first Exascale grid computer. This has accelerated COVID-19 research in molecular dynamics simulations of SARS-CoV-2 spike protein interactions through massively-parallel computation and was performed with over 1 million volunteer computing devices using the Folding@home platform. Grids and clouds both can also be used for international collaboration by enabling access to important datasets and providing services that allow researchers to focus on research rather than on time-consuming data-management tasks
The application of Hadoop in structural bioinformatics
The paper reviews the use of the Hadoop platform in structural bioinformatics applications. For structural bioinformatics, Hadoop provides a new framework to analyse large fractions of the Protein Data Bank that is key for high-throughput studies of, for example, protein-ligand docking, clustering of protein-ligand complexes and structural alignment. Specifically we review in the literature a number of implementations using Hadoop of high-throughput analyses and their scalability. We find that these deployments for the most part use known executables called from MapReduce rather than rewriting the algorithms. The scalability exhibits a variable behaviour in comparison with other batch schedulers, particularly as direct comparisons on the same platform are generally not available. Direct comparisons of Hadoop with batch schedulers are absent in the literature but we note there is some evidence that Message Passing Interface implementations scale better than Hadoop. A significant barrier to the use of the Hadoop ecosystem is the difficulty of the interface and configuration of a resource to use Hadoop. This will improve over time as interfaces to Hadoop, e.g. Spark improve, usage of cloud platforms (e.g. Azure and Amazon Web Services (AWS)) increases and standardised approaches such as Workflow Languages (i.e. Workflow Definition Language, Common Workflow Language and Nextflow) are taken up
Distributed Computing in a Pandemic
The current COVID-19 global pandemic caused by the SARS-CoV-2 betacoronavirus has resulted in over a million deaths and is having a grave socio-economic impact, hence there is an urgency to find solutions to key research challenges. Much of this COVID-19 research depends on distributed computing. In this article, I review distributed architectures -- various types of clusters, grids and clouds -- that can be leveraged to perform these tasks at scale, at high-throughput, with a high degree of parallelism, and which can also be used to work collaboratively. High-performance computing (HPC) clusters will be used to carry out much of this work. Several bigdata processing tasks used in reducing the spread of SARS-CoV-2 require high-throughput approaches, and a variety of tools, which Hadoop and Spark offer, even using commodity hardware. Extremely large-scale COVID-19 research has also utilised some of the world's fastest supercomputers, such as IBM's SUMMIT -- for ensemble docking high-throughput screening against SARS-CoV-2 targets for drug-repurposing, and high-throughput gene analysis -- and Sentinel, an XPE-Cray based system used to explore natural products. Grid computing has facilitated the formation of the world's first Exascale grid computer. This has accelerated COVID-19 research in molecular dynamics simulations of SARS-CoV-2 spike protein interactions through massively-parallel computation and was performed with over 1 million volunteer computing devices using the Folding@home platform. Grids and clouds both can also be used for international collaboration by enabling access to important datasets and providing services that allow researchers to focus on research rather than on time-consuming data-management tasks
Distributed Computing in a Pandemic: A Review of Technologies Available for Tackling COVID-19
The current COVID-19 global pandemic caused by the SARS-CoV-2 betacoronavirus
has resulted in over a million deaths and is having a grave socio-economic
impact, hence there is an urgency to find solutions to key research challenges.
Much of this COVID-19 research depends on distributed computing. In this
article, I review distributed architectures -- various types of clusters, grids
and clouds -- that can be leveraged to perform these tasks at scale, at
high-throughput, with a high degree of parallelism, and which can also be used
to work collaboratively. High-performance computing (HPC) clusters will be used
to carry out much of this work. Several bigdata processing tasks used in
reducing the spread of SARS-CoV-2 require high-throughput approaches, and a
variety of tools, which Hadoop and Spark offer, even using commodity hardware.
Extremely large-scale COVID-19 research has also utilised some of the world's
fastest supercomputers, such as IBM's SUMMIT -- for ensemble docking
high-throughput screening against SARS-CoV-2 targets for drug-repurposing, and
high-throughput gene analysis -- and Sentinel, an XPE-Cray based system used to
explore natural products. Grid computing has facilitated the formation of the
world's first Exascale grid computer. This has accelerated COVID-19 research in
molecular dynamics simulations of SARS-CoV-2 spike protein interactions through
massively-parallel computation and was performed with over 1 million volunteer
computing devices using the Folding@home platform. Grids and clouds both can
also be used for international collaboration by enabling access to important
datasets and providing services that allow researchers to focus on research
rather than on time-consuming data-management tasks.Comment: 21 pages (15 excl. refs), 2 figures, 3 table
Extending Molecular Docking Desktop Applications with Cloud Computing Support and Analysis of Results
Structure-based virtual screening simulations, which are often used in drug discovery, can be very computationally demanding. This is why user-friendly domain-specific web or desktop applications that enable running simulations on powerful computing infrastructures have been created. This article investigates how domain-specific desktop applications can be extended to use cloud computing and how they can be part of scenarios that require sharing and analysing previous molecular docking results. A generic approach based on interviews with scientists and analysis of existing systems is proposed. A proof of concept is implemented using the Raccoon2 desktop application for virtual screening, WS-PGRADE workflows, gUSE services with the CloudBroker Platform, the structural alignment tool DeepAlign, and the ligand similarity tool LIGSIFT. The presented analysis illustrates that this approach of extending a domainspecific desktop application can use different types of clouds, thus facilitating the execution of virtual screening simulations by life scientists without requiring them to abandon their favourite desktop environment and providing them resources without major capital investment. It also shows that storing and sharing molecular docking results can produce additional conclusions such as viewing similar docking input files for verification or learning
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Heterogeneous Cloud Systems Based on Broadband Embedded Computing
Computing systems continue to evolve from homogeneous systems of commodity-based servers within a single data-center towards modern Cloud systems that consist of numerous data-center clusters virtualized at the infrastructure and application layers to provide scalable, cost-effective and elastic services to devices connected over the Internet. There is an emerging trend towards heterogeneous Cloud systems driven from growth in wired as well as wireless devices that incorporate the potential of millions, and soon billions, of embedded devices enabling new forms of computation and service delivery. Service providers such as broadband cable operators continue to contribute towards this expansion with growing Cloud system infrastructures combined with deployments of increasingly powerful embedded devices across broadband networks. Broadband networks enable access to service provider Cloud data-centers and the Internet from numerous devices. These include home computers, smart-phones, tablets, game-consoles, sensor-networks, and set-top box devices. With these trends in mind, I propose the concept of broadband embedded computing as the utilization of a broadband network of embedded devices for collective computation in conjunction with centralized Cloud infrastructures. I claim that this form of distributed computing results in a new class of heterogeneous Cloud systems, service delivery and application enablement. To support these claims, I present a collection of research contributions in adapting distributed software platforms that include MPI and MapReduce to support simultaneous application execution across centralized data-center blade servers and resource-constrained embedded devices. Leveraging these contributions, I develop two complete prototype system implementations to demonstrate an architecture for heterogeneous Cloud systems based on broadband embedded computing. Each system is validated by executing experiments with applications taken from bioinformatics and image processing as well as communication and computational benchmarks. This vision, however, is not without challenges. The questions on how to adapt standard distributed computing paradigms such as MPI and MapReduce for implementation on potentially resource-constrained embedded devices, and how to adapt cluster computing runtime environments to enable heterogeneous process execution across millions of devices remain open-ended. This dissertation presents methods to begin addressing these open-ended questions through the development and testing of both experimental broadband embedded computing systems and in-depth characterization of broadband network behavior. I present experimental results and comparative analysis that offer potential solutions for optimal scalability and performance for constructing broadband embedded computing systems. I also present a number of contributions enabling practical implementation of both heterogeneous Cloud systems and novel application services based on broadband embedded computing
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