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Since 1994, we have been doing research at Goddard Space Flight Center on implementing a wide variety of applications on commodity based computing clusters. This talk is about these clusters and haw they are used on these applications including ones for remote sensing.
The utility of cluster computers (Beowulfs) for efficient parallel image processing of large-scale mosaics and stereo image correlations is presented.
Computing requirements vary by industry, and it follows that NASA and other research organizations have computing demands that fall outside the mainstream. While cloud computing made rapid inroads for tasks such as powering web applications, performance issues on highly distributed tasks hindered early adoption for scientific computation. One venture to address this problem is Nebula, NASA's homegrown cloud project tasked with delivering science-quality cloud computing resources. However, another industry development is Amazon's high-performance computing (HPC) instances on Elastic Cloud Compute (EC2) that promises improved performance for cluster computation. This paper presents results from a series of benchmarks run on Amazon EC2 and discusses the efficacy of current commercial cloud technology for running scientific applications across a cluster. In particular, a 240-core cluster of cloud instances achieved 2 TFLOPS on High-Performance Linpack (HPL) at 70% of theoretical computational performance. The cluster's local network also demonstrated sub-100 ?s inter-process latency with sustained inter-node throughput in excess of 8 Gbps. Beyond HPL, a real-world Hadoop image processing task from NASA's Lunar Mapping and Modeling Project (LMMP) was run on a 29 instance cluster to process lunar and Martian surface images with sizes on the order of tens of gigapixels. These results demonstrate that while not a rival of dedicated supercomputing clusters, commercial cloud technology is now a feasible option for moderately demanding scientific workloads.
Embedded and real-time systems, like other computing systems, seek to maximize computing power for a given price, and thus can significantly benefit from the advancing capabilities of cluster computing.
Lockheed Martin Space Operations - Stennis Programs (LMSO) at the John C Stennis Space Center (NASA/SSC) has designed and built a Beowulf computer cluster which is owned by NASA/SSC and operated by LMSO. The design and construction of the cluster are detailed in this paper. The cluster is currently used for Computational Fluid Dynamics (CFD) simulations. The CFD codes in use and their applications are discussed. Examples of some of the work are also presented. Performance benchmark studies have been conducted for the CFD codes being run on the cluster. The results of two of the studies are presented and discussed. The cluster is not currently being utilized to its full potential; therefore, plans are underway to add more capabilities. These include the addition of structural, thermal, fluid, and acoustic Finite Element Analysis codes as well as real-time data acquisition and processing during test operations at NASA/SSC. These plans are discussed as well.
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WIGLAF (a Web Interface Generator and Legacy Application Facade) is a computer program that provides a Web-based, distributed, graphical-user-interface (GUI) framework that can be adapted to any of a broad range of application programs, written in any programming language, that are executed remotely on any cluster computer system. WIGLAF enables the rapid development of a GUI for controlling and monitoring a specific application program running on the cluster and for transferring data to and from the application program. The only prerequisite for the execution of WIGLAF is a Web-browser program on a user's personal computer connected with the cluster via the Internet. WIGLAF has a client/server architecture: The server component is executed on the cluster system, where it controls the application program and serves data to the client component. The client component is an applet that runs in the Web browser. WIGLAF utilizes the Extensible Markup Language to hold all data associated with the application software, Java to enable platform-independent execution on the cluster system and the display of a GUI generator through the browser, and the Java Remote Method Invocation software package to provide simple, effective client/server networking.
An application for parallel computation on a combined cluster of powerful workstations and supercomputers was developed. A Parallel Virtual Machine (PVM) is used as message passage language on a macro-tasking parallelization of the Aerodynamic Inverse Design and Analysis for a Full Engine computer code. The heterogeneous nature of the cluster is perfectly handled by the controlling host machine. Communication is established via Ethernet with the TCP/IP protocol over an open network. A reasonable overhead is imposed for internode communication, rendering an efficient utilization of the engaged processors. Perhaps one of the most interesting features of the system is its versatile nature, that permits the usage of the computational resources available that are experiencing less use at a given point in time.
A general-purpose Fortran-90 code for calculation of the electromagnetic scattering and absorption properties of multiple sphere clusters is described. The code can calculate the efficiency factors and scattering matrix elements of the cluster for either fixed or random orientation with respect to the incident beam and for plane wave or localized- approximation Gaussian incident fields. In addition, the code can calculate maps of the electric field both interior and exterior to the spheres.The code is written with message passing interface instructions to enable the use on distributed memory compute clusters, and for such platforms the code can make feasible the calculation of absorption, scattering, and general EM characteristics of systems containing several thousand spheres.
This paper describes the cluster machines used for a wide variety of applications, and then discusses a image data processing application in more detail.
In this paper we outline the results of using three generations of cluster machines at JPL for a select set of applications drawn from a larger development effort.
Two clustering techniques were used for terrain mapping by computer of test sites in Yellowstone National Park. One test was made with multispectral scanner data using a composite technique which consists of (1) a strictly sequential statistical clustering which is a sequential variance analysis, and (2) a generalized K-means clustering. In this composite technique, the output of (1) is a first approximation of the cluster centers. This is the input to (2) which consists of steps to improve the determination of cluster centers by iterative procedures. Another test was made using the three emulsion layers of color-infrared aerial film as a three-band spectrometer. Relative film densities were analyzed using a simple clustering technique in three-color space. Important advantages of the clustering technique over conventional supervised computer programs are (1) human intervention, preparation time, and manipulation of data are reduced, (2) the computer map, gives unbiased indication of where best to select the reference ground control data, (3) use of easy to obtain inexpensive film, and (4) the geometric distortions can be easily rectified by simple standard photogrammetric techniques.
Stereo image correlation is an essential ingredient to Mars missions for mapping and ranging purposes.
The performance of a workstation cluster used for the solution of the Reynolds-averaged Navier-Stokes equations is compared with a conventional vector supercomputer architecture. The application simulation of the steady flowfield about a transonic transport was computed using an implicit diagonal scheme in an overset mesh framework. Static load balancing was used, while coarse grain decomposition was achieved by solution of a grid zone per processor. Price/performance ratios are estimated for several scenarios in which such clusters may be utilized.
Recent advances in low-end processor and network technology have made it possible to build a "supercomputer" out of commodity components. We develop simple models of the NAS Parallel Benchmarks version 2 (NPB 2) to explore the cost/performance trade-offs involved in building a balanced parallel computer supporting a scientific workload. We develop closed form expressions detailing the number and size of messages sent by each benchmark. Coupling these with measured single processor performance, network latency, and network bandwidth, our models predict benchmark performance to within 30%. A comparison based on total system cost reveals that current commodity technology (200 MHz Pentium Pros with 100baseT Ethernet) is well balanced for the NPBs up to a total system cost of around $1,000,000.
Recent advances in low-end processor and network technology have made it possible to build a "supercomputer" out of commodity components. We develop simple models of the NAS Parallel Benchmarks version 2 (NPB 2) to explore the cost/performance trade-offs involved in building a balanced parallel computer supporting a scientific workload. By measuring single processor benchmark performance, network latency, and network bandwidth, and using closed form expressions detailing the number and size of messages sent by each benchmark, our models predict benchmark performance to within 30%. A comparison based on total system cost reveals that current commodity technology (200 MHz Pentium Pros with 100baseT Ethernet) is well balanced for the NPBs up to a total system cost of around $ 1,000,000.
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