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Enhancing HPC security with a user-based firewall

September 13, 2016
  |
Conference Paper
Author:
Andrew J. Prout
…
Published in:
HPEC 2016: IEEE Conf. on High Performance Extreme Computing, 13-15 September 2016.
Topic:
high performance computing
R&D area:
R&D group:

Summary

High Performance Computing (HPC) systems traditionally allow their users unrestricted use of their internal network. While this network is normally controlled enough to guarantee privacy without the need for encryption, it does not provide a method to authenticate peer connections. Protocols built upon this internal network, such as those used in MPI, Lustre, Hadoop, or Accumulo, must provide their own authentication at the application layer. Many methods have been employed to perform this authentication, such as operating system privileged ports, Kerberos, munge, TLS, and PKI certificates. However, support for all of these methods requires the HPC application developer to include support and the user to configure and enable these services. The user-based firewall capability we have prototyped enables a set of rules governing connections across the HPC internal network to be put into place using Linux netfilter. By using an operating system-level capability, the system is not reliant on any developer or user actions to enable security. The rules we have chosen and implemented are crafted to not impact the vast majority of users and be completely invisible to them. Additionally, we have measured the performance impact of this system under various workloads.
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Summary

High Performance Computing (HPC) systems traditionally allow their users unrestricted use of their internal network. While this network is normally controlled enough to guarantee privacy without the need for encryption, it does not provide a method to authenticate peer connections. Protocols built upon this internal network, such as those used...

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Enhancing HPC security with a user-based firewall

Benchmarking SciDB data import on HPC systems

September 13, 2016
  |
Conference Paper
Author:
Siddharth S. Samsi
…
Published in:
HPEC 2016: IEEE Conf. on High Performance Extreme Computing, 13-15 September 2016.
Topic:
high performance computing
R&D area:
R&D group:

Summary

SciDB is a scalable, computational database management system that uses an array model for data storage. The array data model of SciDB makes it ideally suited for storing and managing large amounts of imaging data. SciDB is designed to support advanced analytics in database, thus reducing the need for extracting data for analysis. It is designed to be massively parallel and can run on commodity hardware in a high performance computing (HPC) environment. In this paper, we present the performance of SciDB using simulated image data. The Dynamic Distributed Dimensional Data Model (D4M) software is used to implement the benchmark on a cluster running the MIT SuperCloud software stack. A peak performance of 2.2M database inserts per second was achieved on a single node of this system. We also show that SciDB and the D4M toolbox provide more efficient ways to access random sub-volumes of massive datasets compared to the traditional approaches of reading volumetric data from individual files. This work describes the D4M and SciDB tools we developed and presents the initial performance results. This performance was achieved by using parallel inserts, a in-database merging of arrays as well as supercomputing techniques, such as distributed arrays and single-program-multiple-data programming.
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Summary

SciDB is a scalable, computational database management system that uses an array model for data storage. The array data model of SciDB makes it ideally suited for storing and managing large amounts of imaging data. SciDB is designed to support advanced analytics in database, thus reducing the need for extracting...

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Benchmarking SciDB data import on HPC systems

From NoSQL Accumulo to NewSQL Graphulo: design and utility of graph algorithms inside a BigTable database

September 13, 2016
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Conference Paper
Author:
Dylan D. Hutchison
…
Published in:
HPEC 2016: IEEE Conf. on High Performance Extreme Computing, 13-15 September 2016.
Topic:
algorithms
R&D area:
R&D group:

Summary

Google BigTable's scale-out design for distributed key-value storage inspired a generation of NoSQL databases. Recently the NewSQL paradigm emerged in response to analytic workloads that demand distributed computation local to data storage. Many such analytics take the form of graph algorithms, a trend that motivated the GraphBLAS initiative to standardize a set of matrix math kernels for building graph algorithms. In this article we show how it is possible to implement the GraphBLAS kernels in a BigTable database by presenting the design of Graphulo, a library for executing graph algorithms inside the Apache Accumulo database. We detail the Graphulo implementation of two graph algorithms and conduct experiments comparing their performance to two main-memory matrix math systems. Our results shed insight into the conditions that determine when executing a graph algorithm is faster inside a database versus an external system—in short, that memory requirements and relative I/O are critical factors.
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Summary

Google BigTable's scale-out design for distributed key-value storage inspired a generation of NoSQL databases. Recently the NewSQL paradigm emerged in response to analytic workloads that demand distributed computation local to data storage. Many such analytics take the form of graph algorithms, a trend that motivated the GraphBLAS initiative to standardize...

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From NoSQL Accumulo to NewSQL Graphulo: design and utility of graph algorithms inside a BigTable database

LLMapReduce: multi-level map-reduce for high performance data analysis

May 23, 2016
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Conference Paper
Author:
Chansup Byun
…
Published in:
IEEE Int. Parallel and Distributed Processing Symp., IPDPS 2016, 23-27 May 2016.
Topic:
high performance computing
R&D area:
R&D group:

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 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.
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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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LLMapReduce: multi-level map-reduce for high performance data analysis

Scalability of VM provisioning systems

January 1, 2016
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Conference Paper
Author:
Michael S. Jones
…
Published in:
HPEC 2016: IEEE Conf. on High Performance Extreme Computing, 13-15 September 2016.
Topic:
high performance computing
R&D area:
R&D group:

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 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.
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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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Scalability of VM provisioning systems

Sampling operations on big data

November 8, 2015
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Conference Paper
Author:
Vijay N. Gadepally
…
Published in:
2015 Asilomar Conf. on Signals, Systems and Computers, 8-11 November 2015.
Topic:
big data
R&D area:
R&D group:

Summary

The 3Vs -- Volume, Velocity and Variety -- of Big Data continues to be a large challenge for systems and algorithms designed to store, process and disseminate information for discovery and exploration under real-time constraints. Common signal processing operations such as sampling and filtering, which have been used for decades to compress signals are often undefined in data that is characterized by heterogeneity, high dimensionality, and lack of known structure. In this article, we describe and demonstrate an approach to sample large datasets such as social media data. We evaluate the effect of sampling on a common predictive analytic: link prediction. Our results indicate that greatly sampling a dataset can still yield meaningful link prediction results.
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Summary

The 3Vs -- Volume, Velocity and Variety -- of Big Data continues to be a large challenge for systems and algorithms designed to store, process and disseminate information for discovery and exploration under real-time constraints. Common signal processing operations such as sampling and filtering, which have been used for decades...

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Sampling operations on big data

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

September 15, 2015
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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.
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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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Big data strategies for data center infrastructure management using a 3D gaming platform

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.
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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...

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Using a power law distribution to describe big data

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

Sampling large graphs for anticipatory analytics

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

Summary

The characteristics of Big Data - often dubbed the 3V's for volume, velocity, and variety - will continue to outpace the ability of computational systems to process, store, and transmit meaningful results. Traditional techniques for dealing with large datasets often include the purchase of larger systems, greater human-in-the-loop involvement, or more complex algorithms. We are investigating the use of sampling to mitigate these challenges, specifically sampling large graphs. Often, large datasets can be represented as graphs where data entries may be edges, and vertices may be attributes of the data. In particular, we present the results of sampling for the task of link prediction. Link prediction is a process to estimate the probability of a new edge forming between two vertices of a graph, and it has numerous application areas in understanding social or biological networks. In this paper we propose a series of techniques for the sampling of large datasets. In order to quantify the effect of these techniques, we present the quality of link prediction tasks on sampled graphs, and the time saved in calculating link prediction statistics on these sampled graphs.
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Summary

The characteristics of Big Data - often dubbed the 3V's for volume, velocity, and variety - will continue to outpace the ability of computational systems to process, store, and transmit meaningful results. Traditional techniques for dealing with large datasets often include the purchase of larger systems, greater human-in-the-loop involvement, or...

READ MORE
Sampling large graphs for anticipatory analytics
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