Data intensive computing can be defined as computation involving large datasets and complicated I/O patterns. Data intensive computing is challenging because there is a five-orders-of-magnitude latency gap between main memory DRAM and spinning hard disks; the result is that an inordinate amount of time in data intensive computing is spent accessing data on disk. To address this problem we designed and built a prototype data intensive supercomputer named DASH that exploits flash-based Solid State Drive (SSD) technology and also virtually aggregated DRAM to fill the latency gap . DASH uses commodity parts including Intel® X25-E flash drives and distributed shared memory (DSM) software from ScaleMP®. The system is highly competitive with several commercial offerings by several metrics including achieved IOPS (input output operations per second), IOPS per dollar of system acquisition cost, IOPS per watt during operation, and IOPS per gigabyte (GB) of available storage. We present here an overview of the design of DASH, an analysis of its cost efficiency, then a detailed recipe for how we designed and tuned it for high data-performance, lastly show that running data-intensive scientific applications from graph theory, biology, and astronomy, we achieved as much as two orders-of- magnitude speedup compared to the same applications run on traditional architectures.

We describe an efficient, robust method for selecting and optimizing terms for a classification or filtering task. Terms are extracted from positive examples in training data based on several alternative term-selection algorithms, then combined additively after a simple term-score normalization step to produce a merged and ranked master term vector. The score threshold for the master vector is set via beta-gamma regulation over all the available training data. The process avoids para-meter calibrations and protracted training. It also results in compact profiles for run-time evaluation of test (new) documents. Results on TREC-2002 filtering-task datasets demonstrate substantial improvements over TREC-median results and rival both idealized IR-based results and optimized (and expensive) SVM-based classifiers in general effectiveness.