Publications

Refine Results

(Filters Applied) Clear All

Enabling on-demand database computing with MIT SuperCloud database management system

September 15, 2015
  |
Conference Paper
Author:
Andrew J. Prout
…
Published in:
HPEC 2015: IEEE Conf. on High Performance Extreme Computing, 15-17 September 2015.
Topic:
high performance computing
R&D area:
R&D group:

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. 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.
READ LESS

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...

READ MORE
Enabling on-demand database computing with MIT SuperCloud database management system

Improving big data visual analytics with interactive virtual reality

September 15, 2015
  |
Conference Paper
Author:
Andrew Moran
…
Published in:
HPEC 2015: IEEE Conf. on High Performance Extreme Computing, 15-17 September 2015.
Topic:
high performance computing
R&D area:
R&D group:

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 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.
READ LESS

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...

READ MORE
Improving big data visual analytics with interactive virtual reality

Big data strategies for data center infrastructure management using a 3D gaming platform

September 15, 2015
  |
Conference Paper
Author:
Matthew Hubbell
…
Published in:
HPEC 2015: IEEE Conf. on High Performance Extreme Computing, 15-17 September 2015.
Topic:
high performance computing
R&D area:
R&D group:

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 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.
READ LESS

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...

READ MORE
Big data strategies for data center infrastructure management using a 3D gaming platform

Parallel vectorized algebraic AES in MATLAB for rapid prototyping of encrypted sensor processing algorithms and database analytics

September 15, 2015
  |
Conference Paper
Author:
Jeremy Kepner
…
Published in:
HPEC 2015: IEEE Conf. on High Performance Extreme Computing, 15-17 September 2015.
Topic:
signal processing
R&D area:
R&D group:

Summary

The increasing use of networked sensor systems and networked databases has led to an increased interest in incorporating encryption directly into sensor algorithms and database analytics. MATLAB is the dominant tool for rapid prototyping of sensor algorithms and has extensive database analytics capabilities. The advent of high level and high performance Galois Field mathematical environments allows encryption algorithms to be expressed succinctly and efficiently. This work leverages the Galois Field primitives found the MATLAB Communication Toolbox to implement a mode of the Advanced Encrypted Standard (AES) based on first principals mathematics. The resulting implementation requires 100x less code than standard AES implementations and delivers speed that is effective for many design purposes. The parallel version achieves speed comparable to native OpenSSL on a single node and is sufficient for real-time prototyping of many sensor processing algorithms and database analytics.
READ LESS

Summary

The increasing use of networked sensor systems and networked databases has led to an increased interest in incorporating encryption directly into sensor algorithms and database analytics. MATLAB is the dominant tool for rapid prototyping of sensor algorithms and has extensive database analytics capabilities. The advent of high level and high...

READ MORE
Parallel vectorized algebraic AES in MATLAB for rapid prototyping of encrypted sensor processing algorithms and database analytics

Using a power law distribution to describe big data

September 15, 2015
  |
Conference Paper
Author:
Vijay N. Gadepally
…
Published in:
HPEC 2015: IEEE Conf. on High Performance Extreme Computing, 15-17 September 2015.
Topic:
high performance computing
R&D area:
R&D group:

Summary

The gap between data production and user ability to access, compute and produce meaningful results calls for tools that address the challenges associated with big data volume, velocity and variety. One of the key hurdles is the inability to methodically remove expected or uninteresting elements from large data sets. This difficulty often wastes valuable researcher and computational time by expending resources on uninteresting parts of data. Social sensors, or sensors which produce data based on human activity, such as Wikipedia, Twitter, and Facebook have an underlying structure which can be thought of as having a Power Law distribution. Such a distribution implies that few nodes generate large amounts of data. In this article, we propose a technique to take an arbitrary dataset and compute a power law distributed background model that bases its parameters on observed statistics. This model can be used to determine the suitability of using a power law or automatically identify high degree nodes for filtering and can be scaled to work with big data.
READ LESS

Summary

The gap between data production and user ability to access, compute and produce meaningful results calls for tools that address the challenges associated with big data volume, velocity and variety. One of the key hurdles is the inability to methodically remove expected or uninteresting elements from large data sets. This...

READ MORE
Using a power law distribution to describe big data

Portable Map-Reduce utility for MIT SuperCloud environment

September 15, 2015
  |
Conference Paper
Author:
Chansup Byun
…
Published in:
HPEC 2015: IEEE Conf. on High Performance Extreme Computing, 15-17 September 2015.
Topic:
supercomputing
R&D area:
R&D group:

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 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.
READ LESS

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...

READ MORE
Portable Map-Reduce utility for MIT SuperCloud environment

ASR-9 Weather Systems Processor technology refresh and upgrade

September 14, 2015
  |
Conference Paper
Author:
John Y. Cho
…
Published in:
37th Conf. on Radar Meteorology, 14-18 September, 2015.
Topic:
wind shear
R&D area:
R&D group:

Summary

The Weather Systems Processor (WSP) is an add-on system to the Airport Surveillance Radar-9 (ASR-9) that generates wind shear detection and storm tracking products for the terminal airspace. As the original system ages and pre-purchased replacement parts in the depot are used up, it becomes increasingly problematic to procure hardware components for repairs. Thus, a technical refresh is needed to sustain WSP operations into the future. This phase of the project targets the intermediate frequency digital receiver, the radar interface module, and the digital signal processor for replacement by updated hardware platforms. At the same time, the increase in computational capability allows for an upgrade in the signal processing algorithm, which will lead to data quality improvements.
READ LESS

Summary

The Weather Systems Processor (WSP) is an add-on system to the Airport Surveillance Radar-9 (ASR-9) that generates wind shear detection and storm tracking products for the terminal airspace. As the original system ages and pre-purchased replacement parts in the depot are used up, it becomes increasingly problematic to procure hardware...

READ MORE
ASR-9 Weather Systems Processor technology refresh and upgrade

Estimating lower vocal tract features with closed-open phase spectral analyses

September 6, 2015
  |
Conference Paper
Author:
Elizabeth C. Godoy
…
Published in:
INTERSPEECH 2015: 15th Annual Conf. of the Int. Speech Communication Assoc., 6-10 September 2015.
Topic:
speech processing
R&D area:
R&D group:

Summary

Previous studies have shown that, in addition to being speaker-dependent yet context-independent, lower vocal tract acoustics significantly impact the speech spectrum at mid-to-high frequencies (e.g 3-6kHz). The present work automatically estimates spectral features that exhibit acoustic properties of the lower vocal tract. Specifically aiming to capture the cyclicity property of the epilarynx tube, a novel multi-resolution approach to spectral analyses is presented that exploits significant differences between the closed and open phases of a glottal cycle. A prominent null linked to the piriform fossa is also estimated. Examples of the feature estimation on natural speech of the VOICES multi-speaker corpus illustrate that a salient spectral pattern indeed emerges between 3-6kHz across all speakers. Moreover, the observed pattern is consistent with that canonically shown for the lower vocal tract in previous works. Additionally, an instance of a speaker's formant (i.e. spectral peak around 3kHz that has been well-established as a characteristic of voice projection) is quantified here for the VOICES template speaker in relation to epilarynx acoustics. The corresponding peak is shown to be double the power on average compared to the other speakers (20 vs 10 dB).
READ LESS

Summary

Previous studies have shown that, in addition to being speaker-dependent yet context-independent, lower vocal tract acoustics significantly impact the speech spectrum at mid-to-high frequencies (e.g 3-6kHz). The present work automatically estimates spectral features that exhibit acoustic properties of the lower vocal tract. Specifically aiming to capture the cyclicity property of...

READ MORE
Estimating lower vocal tract features with closed-open phase spectral analyses

A unified deep neural network for speaker and language recognition

September 6, 2015
  |
Conference Paper
Author:
Frederick S. Richardson
…
Published in:
INTERSPEECH 2015: 15th Annual Conf. of the Int. Speech Communication Assoc., 6-10 September 2015.
Topic:
language recognition
R&D area:
R&D group:

Summary

Significant performance gains have been reported separately for speaker recognition (SR) and language recognition (LR) tasks using either DNN posteriors of sub-phonetic units or DNN feature representations, but the two techniques have not been compared on the same SR or LR task or across SR and LR tasks using the same DNN. In this work we present the application of a single DNN for both tasks using the 2013 Domain Adaptation Challenge speaker recognition (DAC13) and the NIST 2011 language recognition evaluation (LRE11) benchmarks. Using a single DNN trained on Switchboard data we demonstrate large gains in performance on both benchmarks: a 55% reduction in EER for the DAC13 out-of-domain condition and a 48% reduction in Cavg on the LRE11 30s test condition. Score fusion and feature fusion are also investigated as is the performance of the DNN technologies at short durations for SR.
READ LESS

Summary

Significant performance gains have been reported separately for speaker recognition (SR) and language recognition (LR) tasks using either DNN posteriors of sub-phonetic units or DNN feature representations, but the two techniques have not been compared on the same SR or LR task or across SR and LR tasks using the...

READ MORE
A unified deep neural network for speaker and language recognition

A near-quantum-limited Josephson traveling-wave parametric amplifier

September 3, 2015
  |
Journal Article
Author:
C. Macklin
…
Published in:
Sci., Vol. 350, No. 6258, 16 October 2015,pp. 307-10.
Topic:
optics
R&D area:
R&D group:

Summary

Detecting single photon level signals--carriers of both classical and quantum information--is particularly challenging for low-energy microwave frequency excitations. Here we introduce a superconducting amplifier based on a Josephson junction transmission line. Unlike current standing-wave parametric amplifiers, this traveling wave architecture robustly achieves high gain over a bandwidth of several gigahertz with sufficient dynamic range to read out 20 superconducting qubits. To achieve this performance, we introduce a sub-wavelength resonant phase matching technique that enables the creation of nonlinear microwave devices with unique dispersion relations. We benchmark the amplifier with weak measurements, obtaining a high quantum efficiency of 75% (70% including following amplifier noise). With a flexible design based on compact lumped elements, this Josephson amplifier has broad applicability to microwave metrology and quantum optics.
READ LESS

Summary

Detecting single photon level signals--carriers of both classical and quantum information--is particularly challenging for low-energy microwave frequency excitations. Here we introduce a superconducting amplifier based on a Josephson junction transmission line. Unlike current standing-wave parametric amplifiers, this traveling wave architecture robustly achieves high gain over a bandwidth of several gigahertz...

READ MORE
A near-quantum-limited Josephson traveling-wave parametric amplifier
561-570 of 2318