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D4M 2.0 Schema: a general purpose high performance schema for the Accumulo database

September 10, 2013
  |
Conference Paper
Author:
Jeremy Kepner
…
Published in:
HPEC 2013: IEEE Conf. on High Performance Extreme Computing, 10-12 September 2013.
Topic:
big data
R&D area:
R&D group:

Summary

Non-traditional, relaxed consistency, triple store databases are the backbone of many web companies (e.g., Google Big Table, Amazon Dynamo, and Facebook Cassandra). The Apache Accumulo database is a high performance open source relaxed consistency database that is widely used for government applications. Obtaining the full benefits of Accumulo requires using novel schemas. The Dynamic Distributed Dimensional Data Model (D4M) [http://www.mit.edu/~kepner/D4M] provides a uniform mathematical framework based on associative arrays that encompasses both traditional (i.e., SQL) and non-traditional databases. For non-traditional databases D4M naturally leads to a general purpose schema that can be used to fully index and rapidly query every unique string in a dataset. The D4M 2.0 Schema has been applied with little or no customization to cyber, bioinformatics, scientific citation, free text, and social media data. The D4M 2.0 Schema is simple, requires minimal parsing, and achieves the highest published Accumulo ingest rates. The benefits of the D4M 2.0 Schema are independent of the D4M interface. Any interface to Accumulo can achieve these benefits by using the D4M 2.0 Schema.
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Summary

Non-traditional, relaxed consistency, triple store databases are the backbone of many web companies (e.g., Google Big Table, Amazon Dynamo, and Facebook Cassandra). The Apache Accumulo database is a high performance open source relaxed consistency database that is widely used for government applications. Obtaining the full benefits of Accumulo requires using...

READ MORE
D4M 2.0 Schema: a general purpose high performance schema for the Accumulo database

LLSuperCloud: sharing HPC systems for diverse rapid prototyping

September 10, 2013
  |
Conference Paper
Author:
Albert I. Reuther
…
Published in:
HPEC 2013: IEEE Conf. on High Performance Extreme Computing, 10-12 September 2013.
Topic:
supercomputing
R&D area:
R&D group:

Summary

The supercomputing and enterprise computing arenas come from very different lineages. However, the advent of commodity computing servers has brought the two arenas closer than they have ever been. Within enterprise computing, commodity computing servers have resulted in the development of a wide range of new cloud capabilities: elastic computing, virtualization, and data hosting. Similarly, the supercomputing community has developed new capabilities in heterogeneous, massively parallel hardware and software. Merging the benefits of enterprise clouds and supercomputing has been a challenging goal. Significant effort has been expended in trying to deploy supercomputing capabilities on cloud computing systems. These efforts have resulted in unreliable, low performance solutions, which requires enormous expertise to maintain. LLSuperCloud provides a novel solution to the problem of merging enterprise cloud and supercomputing technology. More specifically LLSuperCloud reverses the traditional paradigm of attempting to deploy supercomputing capabilities on a cloud and instead deploys cloud capabilities on a supercomputer. The result is a system that can handle heterogeneous, massively parallel workloads while also providing high performance elastic computing, virtualization, and databases. The benefits of LLSuperCloud are highlighted using a mixed workload of C MPI, parallel MATLAB, Java, databases, and virtualized web services.
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Summary

The supercomputing and enterprise computing arenas come from very different lineages. However, the advent of commodity computing servers has brought the two arenas closer than they have ever been. Within enterprise computing, commodity computing servers have resulted in the development of a wide range of new cloud capabilities: elastic computing...

READ MORE
LLSuperCloud: sharing HPC systems for diverse rapid prototyping

D4M 2.0 Schema: a general purpose high performance schema for the Accumulo database

September 10, 2013
  |
Conference Paper
Author:
Jeremy Kepner
…
Published in:
HPEC 2013: IEEE Conf. on High Performance Extreme Computing, 10-12 September 2013.
Topic:
supercomputing
R&D area:
R&D group:

Summary

Non-traditional, relaxed consistency, triple store databases are the backbone of many web companies (e.g., Google Big Table, Amazon Dynamo, and Facebook Cassandra). The Apache Accumulo database is a high performance open source relaxed consistency database that is widely used for government applications. Obtaining the full benefits of Accumulo requires using novel schemas. The Dynamic Distributed Dimensional Data Model (D4M) [http://www.mit.edu/~kepner/D4M] provides a uniform mathematical framework based on associative arrays that encompasses both traditional (i.e., SQL) and non-traditional databases. For non-traditional databases D4M naturally leads to a general purpose schema that can be used to fully index and rapidly query every unique string in a dataset. The D4M 2.0 Schema has been applied with little or no customization to cyber, bioinformatics, scientific citation, free text, and social media data. The D4M 2.0 Schema is simple, requires minimal parsing, and achieves the highest published Accumulo ingest rates. The benefits of the D4M 2.0 Schema are independent of the D4M interface. Any interface to Accumulo can achieve these benefits by using the D4M 2.0 Schema.
READ LESS

Summary

Non-traditional, relaxed consistency, triple store databases are the backbone of many web companies (e.g., Google Big Table, Amazon Dynamo, and Facebook Cassandra). The Apache Accumulo database is a high performance open source relaxed consistency database that is widely used for government applications. Obtaining the full benefits of Accumulo requires using...

READ MORE
D4M 2.0 Schema: a general purpose high performance schema for the Accumulo database

Very large graphs for information extraction (VLG) - summary of first-year proof-of-concept study

August 20, 2013
  |
Project Report
Author:
Benjamin A. Miller
…
Published in:
MIT Lincoln Laboratory Report VLG-1
Topic:
signal processing
R&D area:
R&D group:

Summary

In numerous application domains relevant to the Department of Defense and the Intelligence Community, data of interest take the form of entities and the relationships between them, and these data are commonly represented as graphs. Under the Very Large Graphs for Information Extraction effort--a one-year proof-of-concept study--MIT LL developed novel techniques for anomalous subgraph detection, building on tools in the signal processing research literature. This report documents the technical results of this effort. Two datasets--a snapshot of Thompson Reuters? Web of Science database and a stream of web proxy logs--were parsed, and graphs were constructed from the raw data. From the phenomena in these datasets, several algorithms were developed to model the dynamic graph behavior, including a preferential attachment mechanism with memory, a streaming filter to model a graph as a weighted average of its past connections, and a generalized linear model for graphs where connection probabilities are determined by additional side information or metadata. A set of metrics was also constructed to facilitate comparison of techniques. The study culminated in a demonstration of the algorithms on the datasets of interest, in addition to simulated data. Performance in terms of detection, estimation, and computational burden was measured according to the metrics. Among the highlights of this demonstration were the detection of emerging coauthor clusters in the Web of Science data, detection of botnet activity in the web proxy data after 15 minutes (which took 10 days to detect using state-of-the-practice techniques), and demonstration of the core algorithm on a simulated 1-billion-vertex graph using a commodity computing cluster.
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Summary

In numerous application domains relevant to the Department of Defense and the Intelligence Community, data of interest take the form of entities and the relationships between them, and these data are commonly represented as graphs. Under the Very Large Graphs for Information Extraction effort--a one-year proof-of-concept study--MIT LL developed novel...

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Very large graphs for information extraction (VLG) - summary of first-year proof-of-concept study

LLGrid: supercomputer for sensor processing

April 15, 2013
  |
Book Chapter
Author:
Jeremy Kepner
…
Published in:
Chapter 24 in Contemporary High Performance Computing, April 2013.
Topic:
supercomputing
R&D area:
R&D group:

Summary

MIT Lincoln Laboratory is a federally funded research and development center that applies advanced technology to problems of national interest. Research and development activities focus on long-term technology development as well as rapid system prototyping and demonstration. A key part of this mission is to develop and deploy advanced sensor systems. Developing the algorithms for these systems requires interactive access to large scale computing and data storage. Deploying these systems requires that the computing and storage capabilities are transportable and energy efficient. The LLGrid system of supercomputers allows hundreds of researchers simultaneous interactive access to large amounts of processing and storage for development and testing of their sensor processing algorithms. The requirements of the LLGrid user base are as diverse as the sensors they are developing: sonar, radar, infrared, optical, hyperspectral, video, bio and cyber. However, there are two common elements: delivering large amounts of data interactively to many processors and high level user interfaces that require minimal user training. The LLGrid software stack provides these capabilities on dozens of LLGrid computing clusters across Lincoln Laboratory. LLGrid systems range from very small (a few nodes) to very large (40+ racks).
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Summary

MIT Lincoln Laboratory is a federally funded research and development center that applies advanced technology to problems of national interest. Research and development activities focus on long-term technology development as well as rapid system prototyping and demonstration. A key part of this mission is to develop and deploy advanced sensor...

READ MORE
LLGrid: supercomputer for sensor processing

Taming biological big data with D4M

January 30, 2013
  |
Journal Article
Author:
Jeremy Kepner
…
Published in:
Lincoln Laboratory Journal, Vol. 20, No. 1, 2013, pp. 82-91.
Topic:
supercomputing
R&D area:
R&D group:

Summary

The supercomputing community has taken up the challenge of "taming the beast" spawned by the massive amount of data available in the bioinformatics domain: How can these data be exploited faster and better? MIT Lincoln Laboratory computer scientists demonstrated how a new Laboratory-developed technology, the Dynamic Distributed Dimensional Data Model (D4M), can be used to accelerate DNA sequence comparison, a core operation in bioinformatics.
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Summary

The supercomputing community has taken up the challenge of "taming the beast" spawned by the massive amount of data available in the bioinformatics domain: How can these data be exploited faster and better? MIT Lincoln Laboratory computer scientists demonstrated how a new Laboratory-developed technology, the Dynamic Distributed Dimensional Data Model...

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Taming biological big data with D4M

Novel graph processor architecture

January 30, 2013
  |
Journal Article
Author:
William S. Song
…
Published in:
Lincoln Laboratory Journal, Vol. 20, No. 1, 2013, pp. 92-104.
Topic:
supercomputing
R&D area:
R&D group:

Summary

Graph algorithms are increasingly used in applications that exploit large databases. However, conventional processor architectures are hard-pressed to handle the throughput and memory requirements of graph computation. Lincoln Laboratory's graph-processor architecture represents a fundamental rethinking of architectures. It utilizes innovations that include high-bandwidth three-dimensional (3D) communication links, a sparse matrix-based graph instruction set, accelerator-based architecture, a systolic sorter, randomized communications, a cacheless memory system, and 3D packaging.
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Summary

Graph algorithms are increasingly used in applications that exploit large databases. However, conventional processor architectures are hard-pressed to handle the throughput and memory requirements of graph computation. Lincoln Laboratory's graph-processor architecture represents a fundamental rethinking of architectures. It utilizes innovations that include high-bandwidth three-dimensional (3D) communication links, a sparse matrix-based...

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Novel graph processor architecture

Driving big data with big compute

September 10, 2012
  |
Conference Paper
Author:
Chansup Byun
…
Published in:
HPEC 2012: IEEE Conf. on High Performance Extreme Computing, 10-12 September 2012.
Topic:
high performance computing
R&D area:
R&D group:

Summary

Big Data (as embodied by Hadoop clusters) and Big Compute (as embodied by MPI clusters) provide unique capabilities for storing and processing large volumes of data. Hadoop clusters make distributed computing readily accessible to the Java community and MPI clusters provide high parallel efficiency for compute intensive workloads. Bringing the big data and big compute communities together is an active area of research. The LLGrid team has developed and deployed a number of technologies that aim to provide the best of both worlds. LLGrid MapReduce allows the map/reduce parallel programming model to be used quickly and efficiently in any language on any compute cluster. D4M (Dynamic Distributed Dimensional Data Model) provided a high level distributed arrays interface to the Apache Accumulo database. The accessibility of these technologies is assessed by measuring the effort to use these tools and is typically a few lines of code. The performance is assessed by measuring the insert rate into the Accumulo database. Using these tools a database insert rate of 4M inserts/second has been achieved on an 8 node cluster.
READ LESS

Summary

Big Data (as embodied by Hadoop clusters) and Big Compute (as embodied by MPI clusters) provide unique capabilities for storing and processing large volumes of data. Hadoop clusters make distributed computing readily accessible to the Java community and MPI clusters provide high parallel efficiency for compute intensive workloads. Bringing the...

READ MORE
Driving big data with big compute

Scalable cryptographic authentication for high performance computing

September 10, 2012
  |
Conference Paper
Author:
Andrew J. Prout
…
Published in:
HPEC 2012: IEEE Conf. on High Performance Extreme Computing, 10-12 September 2012.
Topic:
supercomputing
R&D area:
R&D group:

Summary

High performance computing (HPC) uses supercomputers and computing clusters to solve large computational problems. Frequently HPC resources are shared systems and access to restricted data sets or resources must be authenticated. These authentication needs can take multiple forms, both internal and external to the HPC cluster. A computational stack that uses web services among nodes in the HPC may need to perform authentication between nodes of the same job or a job may need to reach out to data sources outside the HPC. Traditional authentication mechanisms such as passwords or digital certificates encounter issues with the distributed and potentially disconnected nature of HPC systems. Distributing and storing plain-text passwords or cryptographic keys among nodes in a HPC system without special protection is a poor security practice. Systems that reach back to the user's terminal for access to the authenticator are possible, but only in fully interactive supercomputing where connectivity to the user's terminal can be guaranteed. Point solutions can be enabled for these use cases, such as software-based role or self-signed certificates, however they require significant expertise in digital certificates to configure. A more general solution is called for that is both secure and easy to use. This paper presents an overview of a solution implemented on the interactive, on-demand LLGrid computing system at MIT Lincoln Laboratory and its use to solve one such authentication problem.
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Summary

High performance computing (HPC) uses supercomputers and computing clusters to solve large computational problems. Frequently HPC resources are shared systems and access to restricted data sets or resources must be authenticated. These authentication needs can take multiple forms, both internal and external to the HPC cluster. A computational stack that...

READ MORE
Scalable cryptographic authentication for high performance computing

Driving big data with big compute

September 10, 2012
  |
Conference Paper
Author:
Chansup Byun
…
Published in:
HPEC 2012: IEEE Conf. on High Performance Extreme Computing, 10-12 September 2012.
Topic:
supercomputing
R&D area:
R&D group:

Summary

Big Data (as embodied by Hadoop clusters) and Big Compute (as embodied by MPI clusters) provide unique capabilities for storing and processing large volumes of data. Hadoop clusters make distributed computing readily accessible to the Java community and MPI clusters provide high parallel efficiency for compute intensive workloads. Bringing the big data and big compute communities together is an active area of research. The LLGrid team has developed and deployed a number of technologies that aim to provide the best of both worlds. LLGrid MapReduce allows the map/reduce parallel programming model to be used quickly and efficiently in any language on any compute cluster. D4M (Dynamic Distributed Dimensional Data Model) provided a high level distributed arrays interface to the Apache Accumulo database. The accessibility of these technologies is assessed by measuring the effort to use these tools and is typically a few lines of code. The performance is assessed by measuring the insert rate into the Accumulo database. Using these tools a database insert rate of 4M inserts/second has been achieved on an 8 node cluster.
READ LESS

Summary

Big Data (as embodied by Hadoop clusters) and Big Compute (as embodied by MPI clusters) provide unique capabilities for storing and processing large volumes of data. Hadoop clusters make distributed computing readily accessible to the Java community and MPI clusters provide high parallel efficiency for compute intensive workloads. Bringing the...

READ MORE
Driving big data with big compute
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