CIS SIGSYS Boyu Zhang, UD
SIG-SYS Seminar Series
Speaker: Boyu Zhang, UD
Title: Enabling Scalable Data Analysis for Large Computational Structural Biology Datasets on Large Distributed Memory Systems Supported by the MapReduce Paradigm
Abstract:
Today, petascale distributed memory systems perform large-scale simulations and generate massive amounts of data in a distributed fashion at unprecedented rates. This massive amount of data presents new challenges for the scientists analyzing the data scientific meaning. In case of classification and clustering of this data, traditional analysis methods require the comparison of single records with each other in an iterative process, and therefore involve moving data across nodes of the system. When both the data and the number of nodes increase, classification and clustering methods can increase pressure on the storage and the bandwidth of the system. Thus, the methods become inefficient and do not scale. New methodologies are needed to analyze data when it is distributed across nodes of large distributed memory systems.
In general, when analyzing such scientific data, we focus on specific properties of the data records. For example, in structural biology datasets, properties include the molecular geometry or the location of a molecule in a docking pocket. Based on this observation, we propose a methodology that allows the scalable analysis for large datasets composed of millions of individual data records in a distributed manner on large distributed memory systems. The methodology is based on two general steps. The first step extracts concise properties or features of each data record and represents them as metadata in parallel. The second step performs the analysis (i.e., classification or clustering) on the extracted properties using machine-learning techniques.
We apply the methodology to three different computational structural biology datasets to (1) identify class memberships for large RNA sequences from their secondary structures; (2) identify geometrical features that can be used to predict class memberships for structural biology datasets containing ligand conformations from protein-ligand docking simulations; and (3) find recurrent folding patterns within and across trajectories (i.e., intra- and inter-trajectory respectively) in multiple trajectories sampled from folding simulations.
Since our method naturally fits in the MapReduce paradigm, we adapt it for different MapReduce frameworks (i.e., Hadoop, MapReduce-MPI, and DataMPI) and use the frameworks on high-end clusters for the three scientific challenges listed above. Our results show that our approach enables scalable classification and clustering analyses for large-scale computational structural biology datasets on large distributed memory systems. In addition, our method achieves better accuracy comparing to traditional analysis approaches.
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Boyu Zhang is a Ph.D. student in the Department of Computer and Information Sciences under the supervision of professor Michela Taufer. Her research focuses on high performance computing, big data analytics and scientific computing. This is a dry-run of her PhD proposal defense.
Tuesday, March 4, 2014 at 3:30pm to 4:45pm
Smith Hall, 102A
Smith Hall, University of Delaware, Newark, DE 19716, USA
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