XCBC and XNIT - tools for cluster implementation and management in research and training

The Extreme Science and Engineering Discovery Environment has created a suite of software designed to facilitate the local management of computer clusters for scientific research and integration of such clusters with the US open research national cyberinfrastructure. This suite of software is distributed in two ways. One distribution is called the XSEDE-compatible basic cluster (XCBC), a Rocks Roll that does an “all at once, from scratch” installation of core components. The other distribution is called the XSEDE National Integration Toolkit (XNIT), so that specific tools can be downloaded and installed in portions as appropriate on existing clusters. In this paper, we describe the software included in XCBC and XNIT, and examine the use of XCBC installed on the LittleFe cluster design created by the Earlham College Cluster Computing Group as a teaching tool to show the deployment of XCBC from Rocks. In addition, the demonstration of the commercial Limulus HPC200 Deskside Cluster solution is shown as a viable, off-the-shelf cluster that can be adapted to become an XSEDE-like cluster through the use of the XNIT repository. We demonstrate that both approaches to cluster management – use of SCBC to build clusters from scratch and use of XNIT to expand capabilities of existing clusters – aid cluster administrators in administering clusters that are valuable locally and facilitate integration and interoperability of campus clusters with national cyberinfrastructure. We also demonstrate that very economical clusters can be useful tools in education and research.This document was developed with support from National Science Foundation (NSF) grant OCI-1053575. The LittleFe project has been funded in part by a grant from Intel, Inc. to Charlie Peck as well as NSF grants 1258604 and ACI-1347089. This research has also been supported in part by the Indiana University Pervasive Technology Institute, which was established with a major grant from the Lilly Endowment, Inc

Workshop Report: Campus Bridging: Reducing Obstacles on the Path to Big Answers 2015

For the researcher whose experiments require large-scale cyberinfrastructure, there exists significant challenges to successful completion. These challenges are broad and go far beyond the simple issue that there are not enough large-scale resources available; these solvable issues range from a lack of documentation written for a non-technical audience to a need for greater consistency with regard to system configuration and consistent software configuration and availability on the large-scale resources at national tier supercomputing centers, with a number of other challenges existing alongside the ones mentioned here. Campus Bridging is a relatively young discipline that aims to mitigate these issues for the academic end-user, for whom the entire process can feel like a path comprised entirely of obstacles. The solutions to these problems must by necessity include multiple approaches, with focus not only on the end user but on the system administrators responsible for supporting these resources as well as the systems themselves. These system resources include not only those at the supercomputing centers but also those that exist at the campus or departmental level and even on the personal computing devices the researcher uses to complete his or her work. This workshop report compiles the results of a half-day workshop, held in conjunction with IEEE Cluster 2015 in Chicago, IL.NSF XSED

Smart Distributed Processing Technologies For Hedge Fund Management

Distributed processing cluster design using commodity hardware and software has proven to be a technological breakthrough in the field of parallel and distributed computing. The research presented herein is the original investigation on distributed processing using hybrid processing clusters to improve the calculation efficiency of the compute-intensive applications. This has opened a new frontier in affordable supercomputing that can be utilised by businesses and industries at various levels. Distributed processing that uses commodity computer clusters has become extremely popular over recent years, particularly among university research groups and research organisations. The research work discussed herein addresses a bespoke-oriented design and implementation of highly specific and different types of distributed processing clusters with applied load balancing techniques that are well suited for particular business requirements. The research was performed in four phases, which are cohesively interconnected, to find a suitable solution using a new type of distributed processing approaches. The first phase is an implementation of a bespoke-type distributed processing cluster using an existing network of workstations as a calculation cluster based on a loosely coupled distributed process system design that has improved calculation efficiency of certain legacy applications. This approach has demonstrated how to design an innovative, cost-effective, and efficient way to utilise a workstation cluster for distributed processing. The second phase is to improve the calculation efficiency of the distributed processing system; a new type of load balancing system is designed to incorporate multiple processing devices. The load balancing system incorporates hardware, software and application related parameters to assigned calculation tasks to each processing devices accordingly. Three types of load balancing methods are tested, static, dynamic and hybrid, which each of them has their own advantages, and all three of them have further improved the calculation efficiency of the distributed processing system.   The third phase is to facilitate the company to improve the batch processing application calculation time, and two separate dedicated calculation clusters are built using small form factor (SFF) computers and PCs as separate peer-to-peer (P2P) network based calculation clusters. Multiple batch processing applications were tested on theses clusters, and the results have shown consistent calculation time improvement across all the applications tested. In addition, dedicated clusters are built using SFF computers with reduced power consumption, small cluster size, and comparatively low cost to suit particular business needs. The fourth phase incorporates all the processing devices available in the company as a hybrid calculation cluster utilises various type of servers, workstations, and SFF computers to form a high-throughput distributed processing system that consolidates multiple calculations clusters. These clusters can be utilised as multiple mutually exclusive multiple clusters or combined as a single cluster depending on the applications used. The test results show considerable calculation time improvements by using consolidated calculation cluster in conjunction with rule-based load balancing techniques. The main design concept of the system is based on the original design that uses first principle methods and utilises existing LAN and separate P2P network infrastructures, hardware, and software. Tests and investigations conducted show promising results where the company’s legacy applications can be modified and implemented with different types of distributed processing clusters to achieve calculation and processing efficiency for various applications within the company. The test results have confirmed the expected calculation time improvements in controlled environments and show that it is feasible to design and develop a bespoke-type dedicated distributed processing cluster using existing hardware, software, and low-cost SFF computers. Furthermore, a combination of bespoke distributed processing system with appropriate load balancing algorithms has shown considerable calculation time improvements for various legacy and bespoke applications. Hence, the bespoke design is better suited to provide a solution for the calculation of time improvements for critical problems currently faced by the sponsoring company