Redundant Arrays of IDE Drives

The next generation of high-energy physics experiments is expected to gather prodigious amounts of data. New methods must be developed to handle this data and make analysis at universities possible. We examine some techniques that use recent developments in commodity hardware. We test redundant arrays of integrated drive electronics (IDE) disk drives for use in offline high-energy physics data analysis. IDE redundant array of inexpensive disks (RAID) prices now equal the cost per terabyte of million-dollar tape robots! The arrays can be scaled to sizes affordable to institutions without robots and used when fast random access at low cost is important. We also explore three methods of moving data between sites; internet transfers, hot pluggable IDE disks in FireWire cases, and writable digital video disks (DVD-R).Comment: Submitted to IEEE Transactions On Nuclear Science, for the 2001 IEEE Nuclear Science Symposium and Medical Imaging Conference, 8 pages, 1 figure, uses IEEEtran.cls. Revised March 19, 2002 and published August 200

Assessing the Utility of a Personal Desktop Cluster

The computer workstation, introduced by Sun Microsystems in 1982, was the tool of choice for scientists and engineers as an interactive computing environment for the development of scientific codes. However, by the mid-1990s, the performance of workstations began to lag behind high-end commodity PCs. This, coupled with the disappearance of BSD-based operating systems in workstations and the emergence of Linux as an opensource operating system for PCs, arguably led to the demise of the workstation as we knew it. Around the same time, computational scientists started to leverage PCs running Linux to create a commodity-based (Beowulf) cluster that provided dedicated compute cycles, i.e., supercomputing for the rest of us, as a cost-effective alternative to large supercomputers, i.e., supercomputing for the few. However, as the cluster movement has matured, with respect to cluster hardware and open-source software, these clusters have become much more like their large-scale supercomputing brethren — a shared datacenter resource that resides in a machine room. Consequently, the above observations, when coupled with the ever-increasing performance gap between the PC and cluster supercomputer, provide the motivation for a personal desktop cluster workstation — a turnkey solution that provides an interactive and parallel computing environment with the approximate form factor of a Sun SPARCstation 1 “pizza box” workstation. In this paper, we present the hardware and software architecture of such a solution as well as its prowess as a developmental platform for parallel codes. In short, imagine a 12-node personal desktop cluster that achieves 14 Gflops on Linpack but sips only 150-180 watts of power, resulting in a performance-power ratio that is over 300% better than our test SMP platform

PC as physics computer for LHC?

In the last five years, we have seen RISC workstations take over the computing scene that was once controlled by mainframes and supercomputers. In this paper we will argue that the same phenomenon might happen again. A project, active since March this year in the Physics Data Processing group of CERN's CN division is described where ordinary desktop PCs running Windows (NT and 3.11) have been used for creating an environment for running large LHC batch jobs (initially the DICE simulation job of Atlas). The problems encountered in porting both the CERN library and the specific Atlas codes are described together with some encouraging benchmark results when comparing to existing RISC workstations in use by the Atlas collaboration. The issues of establishing the batch environment (Batch monitor, staging software, etc.) are also covered. Finally a quick extrapolation of commodity computing power available in the future is touched upon to indicate what kind of cost envelope could be sufficient for the simulation farms required by the LHC experiments

Performance measurement and analysis of PC based cluster server using SET of Architecture and modeling a scalable High performance cluster

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