LLSuperCloud: Sharing HPC systems for diverse rapid prototyping

Reuther, Albert; Kepner, Jeremy; Arcand, William; Bestor, David; Bergeron, Bill; Byun, Chansup; Hubbell, Matthew; Michaleas, Peter; Mullen, Julie; Prout, Andrew; Rosa, Antonio

doi:10.1109/hpec.2013.6670329

Cited by 30 publications

(19 citation statements)

References 8 publications

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“…The authors were also supported by the Ida M. Green Fellowship through the MIT Office of the Dean of Graduate Education. Computational resources were provided in part by the MIT Supercloud [35]. J. W. was supported by the National Science Foundation Graduate Research Fellowship under Grant No.…”

Section: Acknowledgementsmentioning

confidence: 99%

Computational Insights Into Avidity of Polymeric Multivalent Binders

Zumbro¹,

Witten²,

Alexander‐Katz³

2019

Preprint

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Multivalent binding interactions are commonly found throughout biology to enhance weak monovalent binding such as between glycoligands and protein receptors. Designing multivalent polymers to bind to viruses and toxic proteins is a promising avenue for inhibiting their attachment and subsequent infection of cells. Several studies have focused on oligomeric multivalent inhibitors and how changing parameters such as ligand shape and size, and linker length and flexibility affect binding. However, experimental studies of how larger structural parameters of multivalent polymers such as degree of polymerization affect binding avidity to targets have mixed results with some finding an improvement with longer polymers and some finding no effect. Here, we use Brownian dynamics simulations to provide a theoretical understanding of how degree of polymerization affects the binding avidity of multivalent polymers. We show that longer polymers increase binding avidity to multivalent targets, but reach a limit in binding avidity at high degrees of polymerization. We also show that when interacting with multiple targets simultaneously, longer polymers are able to use inter-target interactions to promote clustering and improve binding efficiency. We expect our results to narrow the design space for optimizing the structure and effectiveness of multivalent inhibitors, as well as be useful to understand biological design strategies for multivalent binding.

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Section: Acknowledgementsmentioning

confidence: 99%

Computational Insights Into Avidity of Polymeric Multivalent Binders

Zumbro¹,

Witten²,

Alexander‐Katz³

2019

Preprint

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“…For almost 15 years the Lincoln Laboratory Supercomputing Center (LLSC, formerly the Lincoln Laboratory Grid (LLGrid) team) at MIT Lincoln Laboratory has provided interactive on-demand cluster computing resources to over 1,000 researchers at the Laboratory [1]. As part of the LLSC mission to deliver new and innovative technologies and methods, we have developed and built the MIT SuperCloud [21] to enabling scientists and engineers to quickly ramp up the pace of their research and rapid prototyping by leveraging big compute and big data storage assets. The SuperCloud is a fusion of the four large computing ecosystems: supercomputing, enterprise computing, big data and traditional databases into a coherent, unified platform.…”

Section: Introductionmentioning

confidence: 99%

Lessons Learned from a Decade of Providing Interactive, On-Demand High Performance Computing to Scientists and Engineers

Mullen

Reuther

Arcand

et al. 2018

Lecture Notes in Computer Science

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For decades, the use of HPC systems was limited to those in the physical sciences who had mastered their domain in conjunction with a deep understanding of HPC architectures and algorithms. During these same decades, consumer computing device advances produced tablets and smartphones that allow millions of children to interactively develop and share code projects across the globe. As the HPC community faces the challenges associated with guiding researchers from disciplines using high productivity interactive tools to effective use of HPC systems, it seems appropriate to revisit the assumptions surrounding the necessary skills required for access to large computational systems. For over a decade, MIT Lincoln Laboratory has been supporting interactive, ondemand high performance computing by seamlessly integrating familiar high productivity tools to provide users with an increased number of design turns, rapid prototyping capability, and faster time to insight. In this paper, we discuss the lessons learned while supporting interactive, on-demand high performance computing from the perspectives of the users and the team supporting the users and the system. Building on these lessons, we present an overview of current needs and the technical solutions we are building to lower the barrier to entry for new users from the humanities, social, and biological sciences.

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“…The project is exploring the heterogeneous big-data paradigms in health-care [46] and uses an MIT Lincoln Laboratory developed software environment [45] as its core platform for deploying high-performance scalable data analytics tools. The platform used requires full control of the software stack and system operations for the duration of the experiment.…”

Section: Supporting a Big-data Systems Experimentsmentioning

confidence: 99%

Hil

Hennessey

Tikale

Türk

et al. 2016

Proceedings of the Seventh ACM Symposium on Cloud Computing

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We propose a new Exokernel-like layer to allow mutually untrusting physically deployed services to efficiently share the resources of a data center. We believe that such a layer offers not only efficiency gains, but may also enable new economic models, new applications, and new security-sensitive uses. A prototype (currently in active use) demonstrates that the proposed layer is viable, and can support a variety of existing provisioning tools and use cases.

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LLSuperCloud: Sharing HPC systems for diverse rapid prototyping

Cited by 30 publications

References 8 publications

Computational Insights Into Avidity of Polymeric Multivalent Binders

Computational Insights Into Avidity of Polymeric Multivalent Binders

Lessons Learned from a Decade of Providing Interactive, On-Demand High Performance Computing to Scientists and Engineers

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