We developed a PC cluster system consists of 100 PCs. Each PC employs the 200MHz Pentium Pro CPU and is connected with others through an ATM switch. We picked up two kinds of data intensive applications. One is decision support query processing. And the other is data mining, specifically, association rule mining.As a high speed network, ATM technology has recently come to be a de facto standard. While other high performance network standards are also available, ATM networks are widely used from local area to widely distributed environments. One of the problems of the ATM networks is its high latencies, in contrast to their higher bandwidths. This is usually considered a serious flaw of ATM in composing high performance massively parallel processors. However, applications such as large scale database analyses are insensitive to the communication latency, requiring only the bandwidth.On the other hand, the performance of personal computers is increasing rapidly these days while the prices of PCs continue to fall at a much faster rate than workstations'. The 200MHz Pentium Pro CPU is competitive in integer performance to the processor chips found in workstations. Although it is still weak at floating point operations, they are not frequently used in database applications.Thus, by combining PCs and ATM switches we can construct a large scale parallel platform very easily and very inexpensively. In this paper, we examine how such a system can help the data warehouse processing, which currently runs on expensive high-end mainframes and/or workstation servers.In our first experiment, we used the most complex query of the standard benchmark, TPC-D, on a 100 GB database to evaluate the system compared with commercial parallel systems. Our PC cluster exhibited much higher performance compared with those in current TPC benchmark reports. Second, we parallelized association rule mining and ran large scale data mining on the PC cluster. Sufficiently high linearity was obtained. Thus we believe that such commoditybased PC clusters will play a very important role in large scale database processing.
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