• Arizona State University
  • co-PI: Oliver Beckstein

The lab addresses problems in molecular biology and physical chemistry and develops computational tools to answer them. As part of the SPIDAL project, they will use the libraries developed by the partners to analyze data-intensive biomolecular simulations. Algorithms for efficiently analyzing large simulations will be implemented as part of the popular MDAnalysis package. By bringing “BigData” tools to the computational biophysics community, we try to answer questions on how proteins function in healthy conditions and how they malfunction in diseases, ultimately leading to physics-inspired answers on how to improve human health. The SPIDAL project will support the lab’s overarching aim to quantitatively predict the function and activity of proteins from the knowledge of their structures alone and so to better understand biological phenomena at the molecular level, in particular transmembrane transport processes catalyzed by membrane proteins such as secondary active transporters and ion channels. The work contributes to a range of related fields such as structural biology, physiology, nanobiotechnology as well as ultimately to the development of new drugs.


  • University of Kansas
  • co-PI: John D. Paden

Work at the Center for Remote Sensing of Ice Sheets’ (CReSIS) in relation to SPIDAL is to use HPC computing to solve large global optimization problems related to internal layer tracking and generation of ice bed digital elevation models from 3-D images generated from the petabyte-scale CReSIS radar dataset (more found here). CReSIS plans to work with the SPIDAL algorithm development group to apply SPIDAL optimization and image processing routines to its datasets and will also be looking at tightly coupling these algorithms to the signal processing to produce more accurate results with increased efficiency. CReSIS is helping represent the polar community by acting as a conduit between the polar community and the SPIDAL algorithm development group on other problems in polar science that would benefit from the HPC capabilities being developed by SPIDAL.


  • Indiana University
  • co-PI: Judy Qiu

The Salsa Research Group at IU originally introduced Twister iterative computation using long-running processes or threads with in-memory caching of invariant data. The research team explored collective communication (e.g. broadcast, scatter, gather or reduce) on Twister and Twister4Azure, showing different implementations of abstractions on different infrastructure (HPC or Azure). Rather than building full software environments, they then took the next step by building on the Apache Big Data Stack, adding a separate communication abstraction called Harp. The Harp prototype introduces the Map-Collective concept as a plug-in to Hadoop Ecosystem. Harp adds both Map-Collective and data structure (key-value, graph, array, pixel) abstractions to Hadoop. The Harp library provides a common set of data abstractions and related collective communication abstractions to transform Map-Reduce programming models into Map-Collective models, thereby addressing large collectives which are a distinctive feature of data intensive and data mining applications. Harp is currently supported by Hadoop 1.2.1 and Hadoop 2.2.0. The Harp architecture is an extension on next generation MapReduce frameworks with YARN resource manager, providing support to MapCollective applications. Its use in SPIDAL is still being fully realized.