CRPC Integrates Distributed Computing with Web Technologies
Source: HPCwire, June 21, 1996
Syracuse, NY -- Center for Research on Parallel Computation (CRPC) researchers at the Northeast Parallel Architectures Center (NPAC) at Syracuse University and collaborators have been developing concepts and prototypes of "Compute-Webs" over the past year. This work is partly motivated by the integration of information processing and computation for both a better programming environment and a natural support of data-intensive computing. The World Wide Web itself represents the largest available computer, with some 20 million potential nodes worldwide. This potential is expected to grow by a factor of 10 as the Information Superhighway is fully deployed.
The group's first prototype was built on compute-extended Web servers using the standard CGI mechanism. It was successfully applied to the factorization of the RSA 130 decimal digit number using the latest sieving algorithm, which was distributed to a net of Web servers and clients in a load-balanced, fault-tolerant fashion. This work was presented at SUPERCOMPUTING '95 and won the High-Performance Computing Challenge Award for Most Geographically Dispersed and Heterogeneous Factoring on the World-Wide Computer in the Teraflop Challenge contest.
Clearly, the current Web is not the place to explore complex parallel algorithms with stringent latency and synchronization requirements. The RSA 130 problem was "embarrassingly parallel" and suitable for the high functionality but modest performance of the Web. There are at least two natural extensions of this work, MetaWeb and WebFlow, which implement coarse-grain software integration and are insensitive to the modest bandwidth and high latency of geographically distributed computing and current HTTP Web servers.
The researchers found that the CGI-enhanced Web servers that supported RSA 130 factoring did not provide the standard support expected from clustered computing packages. They are designing their new system, MetaWeb, as a cluster or MetaComputer management system built on top of Web servers. MetaWeb includes load balancing, fault tolerance, process management, automatic minimization of job impact on user workstations, security, and accounting support. There are two immediate examples of advantages of this Web-based approach: It automatically provides MetaComputing linkage for all platforms, including Windows as well as UNIX operating systems, and it can naturally use "real" databases such as DB2/Oracle, which have already been linked to the Web.
All system and job information will be directly stored in a relational or object database. Initially, MetaWeb will use CGI scripts linking to existing Perl or C modules, and eventually migrate to a full Java-based system. Another important feature of this proposed system is the natural linkage of scientific data and performance visualization. The group intends to link University of Illinois CRPC researcher Dan Reed's Pablo performance analysis environment to the Web compute servers so that users can both store the performance trace in the associated databases and display them either offline or in real time using Java applets.
Web technology has evolved dramatically since the group's first RSA 130 project. The group sees the growing role of Java both for servers and clients, and VRML for visualization and data specification in the Compute-Webs. They see the low-level Web computing model WebVM as given by a mesh of interacting computationally extended Web servers that form the base infrastructure for a variety of high-level programming environments. It starts from the Intranet domain and current Web technologies, opens up for new technology insertions via portable transparent module API design, and gradually builds reliable worldwide scalability.
MetaWeb facilitates this process by adding system/cluster management and performance visualization support. WebFlow, a natural early high-level programming environment, imposes a dataflow programming paradigm on top of WebVM and offers Java-based tools for visual authoring of computational graphs as well as "little language"-based scripted support for adaptive programmatic variations of the dataflow topology. The initial application of WebFlow is to adaptive mesh refinement in a set of projects that includes the binary black hole grand challenge and environmental simulations from University of Texas CRPC researcher Mary Wheeler.
WebFlow inherits concepts from previous coarse-grain dataflow-based computations, popularized by systems such as AVS, Khoros, HENCE, or CODE. The Web supports dataflow because this is the model by which distributed information is accessed in the Web client-server model. Furthermore, the already established Web-based framework for electronic publication can be extended to support Web publication of software modules within a standardized plug-and-play interface. WebFlow integrates computing, parallel I/O, and information such as database and VRML visualization services in this paradigm.
By augmenting the developing WebVM/WebFlow framework with the solid MetaWeb system management, and by linking the sites of the WebFlow HPCC developers' network, this project can provide the foundation for a true Web-based MetaComputer that can span the globe. This will allow HPCC researchers to fully leverage the Web's primary strength: universal access to common tools and standards for computing, authoring, and information. The resulting environment is very appropriate for supporting the geographically distributed collaboration and computation envisioned in the current NSF resolicitation of the supercomputer centers.
For more information about this project, see the NPAC projects Web site http://old-npac.ucs.indiana.edu/projects/webbasedhpcc/index.html. For information about cluster computing packages, see the Cluster Computing Review Web site http://old-npac.ucs.indiana.edu/techreports/hypertext/sccs-748/index.html or the first issue of the NHSE Review Web site http://nhse.cs.rice.edu/NHSEreview.
Reprinted with permission from the Spring 1996 issue of Parallel Computing
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