Publications
Enforced sparse non-negative matrix factorization
May 23, 2016
Conference Paper
Published in:
30th IEEE Int. Parallel and Distributed Processing Symp., IPDPS 2016, 23-27 May 2016.
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Summary
Non-negative matrix factorization (NMF) is a dimensionality reduction algorithm for data that can be represented as an undirected bipartite graph. It has become a common method for generating topic models of text data because it is known to produce good results, despite its relative simplicity of implementation and ease of computation. One challenge with applying the NMF to large datasets is that intermediate matrix products often become dense, thus stressing the memory and compute elements of the underlying system. In this article, we investigate a simple but powerful modification of the alternating least squares method of determining the NMF of a sparse matrix that enforces the generation of sparse intermediate and output matrices. This method enables the application of NMF to large datasets through improved memory and compute performance. Further, we demonstrate, empirically, that this method of enforcing sparsity in the NMF either preserves or improves both the accuracy of the resulting topic model and the convergence rate of the underlying algorithm.
Summary
Non-negative matrix factorization (NMF) is a dimensionality reduction algorithm for data that can be represented as an undirected bipartite graph. It has become a common method for generating topic models of text data because it is known to produce good results, despite its relative simplicity of implementation and ease of...
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LLMapReduce: multi-level map-reduce for high performance data analysis
May 23, 2016
Conference Paper
Published in:
IEEE Int. Parallel and Distributed Processing Symp., IPDPS 2016, 23-27 May 2016.
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The map-reduce parallel programming model has become extremely popular in the big data community. Many big data workloads can benefit from the enhanced performance offered by supercomputers. LLMapReduce provides the familiar map-reduce parallel programming model to big data users running on a supercomputer. LLMapReduce dramatically simplifies map-reduce programming by providing simple parallel programming capability in one line of code. LLMapReduce supports all programming languages and many schedulers. LLMapReduce can work with any application without the need to modify the application. Furthermore, LLMapReduce can overcome scaling limits in the map-reduce parallel programming model via options that allow the user to switch to the more efficient single-program-multiple-data (SPMD) parallel programming model. These features allow users to reduce the computational overhead by more than 10x compared to standard map-reduce for certain applications. LLMapReduce is widely used by hundreds of users at MIT. Currently LLMapReduce works with several schedulers such as SLURM, Grid Engine and LSF.
Summary
The map-reduce parallel programming model has become extremely popular in the big data community. Many big data workloads can benefit from the enhanced performance offered by supercomputers. LLMapReduce provides the familiar map-reduce parallel programming model to big data users running on a supercomputer. LLMapReduce dramatically simplifies map-reduce programming by providing...
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Storage and Database Management for Big Data
May 3, 2016
Book Chapter
Published in:
Big Data: Storage, Sharing, and Security
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The ability to collect and analyze large amounts of data is a growing problem within the scientific community. The growing gap between data and user calls for innovative tools that address the challenges faced by big data volume, velocity, and verity. While there has been great progress in the world of database technologies in the past few years, there are still many fundamental considerations that must be made by scientists. For example, which of the seemingly infinite technologies are the best to use for my problem? Answers to such questions require careful understanding of the technology field in addition to the types of problems that are being solved. This chapter aims to address many of the pressing questions faced by individuals interesting in using sotrage or database technologies to solve their big data problems.
Summary
The ability to collect and analyze large amounts of data is a growing problem within the scientific community. The growing gap between data and user calls for innovative tools that address the challenges faced by big data volume, velocity, and verity. While there has been great progress in the world...
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D4M and large array databases for management and analysis of large biomedical imaging data
January 28, 2016
Conference Paper
Published in:
North East Database Day 2016, 28 January 2016.
Summary
Advances in medical imaging technologies have enabled the acquisition of increasingly large datasets. Current state-of-the-art confocal or multi-photon imaging technology can produce biomedical datasets in excess of 1 TB per dataset. Typical approaches for analyzing large datasets rely on downsampling the original datasets or leveraging distributed computing resources where small subsets of images are processed independently. These approaches require significant overhead on the part of the programmer to load the desired sub-volume from an array of image files into memory. Databases are well suited for indexing and retrieving components of very large datasets and show significant promise for the analysis of 3D volumetric images. In particular, array-based databases such as SciDB utilize an architecture that supports massive parallel processing while also providing database services such as data management and fast parallel queries. In this paper, we will present a new set of tools that leverage the D4M (Dynamic Distributed Dimensional Data Model) toolbox for analyzing giga-voxel biomedical datasets. By combining SciDB and the D4M toolbox, we demonstrate that we can access large volumetric data and perform large-scale bioinformatics analytics efficiently and interactively. We show that it is possible to achieve an ingest rate of 2.8 million entries per second for importing large datasets into SciDB. These tools provide more efficient ways to access random sub-volumes of massive datasets and to process the information that typically cannot be loaded into memory. This work describes the D4M and SciDB tools that we developed and presents the initial performance results.
Summary
Advances in medical imaging technologies have enabled the acquisition of increasingly large datasets. Current state-of-the-art confocal or multi-photon imaging technology can produce biomedical datasets in excess of 1 TB per dataset. Typical approaches for analyzing large datasets rely on downsampling the original datasets or leveraging distributed computing resources where small...
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Scalability of VM provisioning systems
January 1, 2016
Conference Paper
Published in:
HPEC 2016: IEEE Conf. on High Performance Extreme Computing, 13-15 September 2016.
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Virtual machines and virtualized hardware have been around for over half a century. The commoditization of the x86 platform and its rapidly growing hardware capabilities have led to recent exponential growth in the use of virtualization both in the enterprise and high performance computing (HPC). The startup time of a virtualized environment is a key performance metric for high performance computing in which the runtime of any individual task is typically much shorter than the lifetime of a virtualized service in an enterprise context. In this paper, a methodology for accurately measuring the startup performance on an HPC system is described. The startup performance overhead of three of the most mature, widely deployed cloud management frameworks (OpenStack, OpenNebula, and Eucalyptus) is measured to determine their suitability for workloads typically seen in an HPC environment. A 10x performance difference is observed between the fastest (Eucalyptus) and the slowest (OpenNebula) framework. This time difference is primarily due to delays in waiting on networking in the cloud-init portion of the startup. The methodology and measurements presented should facilitate the optimization of startup across a variety of virtualization environments.
Summary
Virtual machines and virtualized hardware have been around for over half a century. The commoditization of the x86 platform and its rapidly growing hardware capabilities have led to recent exponential growth in the use of virtualization both in the enterprise and high performance computing (HPC). The startup time of a...
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Percolation model of insider threats to assess the optimum number of rules
November 5, 2015
Journal Article
Published in:
Environ. Syst. Decis., Vol. 35, 2015, pp. 504-10.
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Rules, regulations, and policies are the basis of civilized society and are used to coordinate the activities of individuals who have a variety of goals and purposes. History has taught that over-regulation (too many rules) makes it difficult to compete and under-regulation (too few rules) can lead to crisis. This implies an optimal number of rules that avoids these two extremes. Rules create boundaries that define the latitude at which an individual has to perform their activities. This paper creates a Toy Model of a work environment and examines it with respect to the latitude provided to a normal individual and the latitude provided to an insider threat. Simulations with the Toy Model illustrate four regimes with respect to an insider threat: under-regulated, possibly optimal, tipping point, and over-regulated. These regimes depend upon the number of rules (N) and the minimum latitude (Lmin) required by a normal individual to carry out their activities. The Toy Model is then mapped onto the standard 1D Percolation Model from theoretical physics, and the same behavior is observed. This allows the Toy Model to be generalized to a wide array of more complex models that have been well studied by the theoretical physics community and also show the same behavior. Finally, by estimating N and Lmin, it should be possible to determine the regime of any particular environment.
Summary
Rules, regulations, and policies are the basis of civilized society and are used to coordinate the activities of individuals who have a variety of goals and purposes. History has taught that over-regulation (too many rules) makes it difficult to compete and under-regulation (too few rules) can lead to crisis. This...
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Improving big data visual analytics with interactive virtual reality
September 15, 2015
Conference Paper
Published in:
HPEC 2015: IEEE Conf. on High Performance Extreme Computing, 15-17 September 2015.
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For decades, the growth and volume of digital data collection has made it challenging to digest large volumes of information and extract underlying structure. Coined 'Big Data', massive amounts of information has quite often been gathered inconsistently (e.g from many sources, of various forms, at different rates, etc.). These factors impede the practices of not only processing data, but also analyzing and displaying it in an efficient manner to the user. Many efforts have been completed in the data mining and visual analytics community to create effective ways to further improve analysis and achieve the knowledge desired for better understanding. Our approach for improved big data visual analytics is two-fold, focusing on both visualization and interaction. Given geo-tagged information, we are exploring the benefits of visualizing datasets in the original geospatial domain by utilizing a virtual reality platform. After running proven analytics on the data, we intend to represent the information in a more realistic 3D setting, where analysts can achieve an enhanced situational awareness and rely on familiar perceptions to draw in-depth conclusions on the dataset. In addition, developing a human-computer interface that responds to natural user actions and inputs creates a more intuitive environment. Tasks can be performed to manipulate the dataset and allow users to dive deeper upon request, adhering to desired demands and intentions. Due to the volume and popularity of social media, we developed a 3D tool visualizing Twitter on MIT's campus for analysis. Utilizing emerging technologies of today to create a fully immersive tool that promotes visualization and interaction can help ease the process of understanding and representing big data.
Summary
For decades, the growth and volume of digital data collection has made it challenging to digest large volumes of information and extract underlying structure. Coined 'Big Data', massive amounts of information has quite often been gathered inconsistently (e.g from many sources, of various forms, at different rates, etc.). These factors...
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Enabling on-demand database computing with MIT SuperCloud database management system
September 15, 2015
Conference Paper
Published in:
HPEC 2015: IEEE Conf. on High Performance Extreme Computing, 15-17 September 2015.
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The MIT SuperCloud database management system allows for rapid creation and flexible execution of a variety of the latest scientific databases, including Apache Accumulo and SciDB. It is designed to permit these databases to run on a High Performance Computing Cluster (HPCC) platform as seamlessly as any other HPCC job. It ensures the seamless migration of the databases to the resources assigned by the HPCC scheduler and centralized storage of the database files when not running. It also permits snapshotting of databases to allow researchers to experiment and push the limits of the technology without concerns for data or productivity loss if the database becomes unstable.
Summary
The MIT SuperCloud database management system allows for rapid creation and flexible execution of a variety of the latest scientific databases, including Apache Accumulo and SciDB. It is designed to permit these databases to run on a High Performance Computing Cluster (HPCC) platform as seamlessly as any other HPCC job...
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Big data strategies for data center infrastructure management using a 3D gaming platform
September 15, 2015
Conference Paper
Published in:
HPEC 2015: IEEE Conf. on High Performance Extreme Computing, 15-17 September 2015.
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High Performance Computing (HPC) is intrinsically linked to effective Data Center Infrastructure Management (DCIM). Cloud services and HPC have become key components in Department of Defense and corporate Information Technology competitive strategies in the global and commercial spaces. As a result, the reliance on consistent, reliable Data Center space is more critical than ever. The costs and complexity of providing quality DCIM are constantly being tested and evaluated by the United States Government and companies such as Google, Microsoft and Facebook. This paper will demonstrate a system where Big Data strategies and 3D gaming technology is leveraged to successfully monitor and analyze multiple HPC systems and a lights-out modular HP EcoPOD 240a Data Center on a singular platform. Big Data technology and a 3D gaming platform enables the relative real time monitoring of 5000 environmental sensors, more than 3500 IT data points and display visual analytics of the overall operating condition of the Data Center from a command center over 100 miles away. In addition, the Big Data model allows for in depth analysis of historical trends and conditions to optimize operations achieving even greater efficiencies and reliability.
Summary
High Performance Computing (HPC) is intrinsically linked to effective Data Center Infrastructure Management (DCIM). Cloud services and HPC have become key components in Department of Defense and corporate Information Technology competitive strategies in the global and commercial spaces. As a result, the reliance on consistent, reliable Data Center space is...
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Portable Map-Reduce utility for MIT SuperCloud environment
September 15, 2015
Conference Paper
Published in:
HPEC 2015: IEEE Conf. on High Performance Extreme Computing, 15-17 September 2015.
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The MIT Map-Reduce utility has been developed and deployed on the MIT SuperCloud to support scientists and engineers at MIT Lincoln Laboratory. With the MIT Map-Reduce utility, users can deploy their applications quickly onto the MIT SuperCloud infrastructure. The MIT Map-Reduce utility can work with any applications without the need for any modifications. For improved performance, the MIT Map-Reduce utility provides an option to consolidate multiple input data files per compute task as a single stream of input with minimal changes to the target application. This enables users to reduce the computational overhead associated with the cost of multiple application starting up when dealing with more than one piece of input data per compute task. With a small change in a sample MATLAB image processing application, we have observed approximately 12x speed up by reducing the application startup overhead. Currently the MIT Map-Reduce utility can work with several schedulers such as SLURM, Grid Engine and LSF.
Summary
The MIT Map-Reduce utility has been developed and deployed on the MIT SuperCloud to support scientists and engineers at MIT Lincoln Laboratory. With the MIT Map-Reduce utility, users can deploy their applications quickly onto the MIT SuperCloud infrastructure. The MIT Map-Reduce utility can work with any applications without the need...
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