Having cut my teeth early on as a developer of optimizing, parallelizing compilers for high-performance supercomputers, I have a soft place in my heart for any innovative business uses or exploitation of parallelism. Although the bottom dropped out of the supercomputer market coinciding with the end of the Cold War, lessons learned from the heyday of "big iron" continue to reverberate and trickle down to the desktop.

Parallelism itself is not particularly new to the business intelligence (BI) world - high-end platforms have always had a niche in the BI universe, especially when considering the processing and storage requirements for data warehouses and OLAP applications. Parallelism has not been ignored in the data warehousing space; quite the contrary is true. There are some well-known products in place at many Fortune 500 companies, especially in the extract, transform and load (ETL) space, that were developed by companies started by refugees from the supercomputing world; and many data mining tools were developed for high-performance platforms.

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