CSC/ECE 506 Fall 2007/wiki1 9 arubha

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Array Processing is a computer architectural concept that was first put to use in the early 1960s. As scientific computing evolved, the need to process large amounts of data using a common algorithm became important. Computers with an array of processing elements (PEs), controlled by a common control unit (CU) were built. The PEs were usually ALUs, capable of performing simple mathematical operations. The CPU itself would perform the job of the CU.

As computer architectures evolved, a new concept called the Vector processing was developed during the 1970s. In vector processing, a PE usually consists of a collection of functional units that operate on vectors of data. This greatly simplifies the interconnections and reduces data dependency, compared to array processing.

Vector processors and array processors form the basic building blocks of some of the early and most successful supercomputers. Vector and array processing techniques are extensively used by applications like ocean mapping, 3D modeling, molecular modeling, weather forecasting, wind tunnel simulations. The Airbus A380 project made use of the NEC SX-5, scalable vector processor architecture supercomputers, to run simulations and fine tune the design even before the aircraft's maiden flight.

Past Trends

The earliest array processors were used to operate on matrix-like data. The CU would load all the ALUs with a common instruction. The ALUs would get data inputs from a array of memory locations, containing different values from the matrix. This concept of using separate ALUs for each data element, but performing the same operation, is classifed as the Single-Instruction-Multiple-Data (SIMD) under the Flynn Taxonomy.

The first implementation of a vector processing based computer system was the CDC STAR-100. It was developed by the Control Data Corporation (CDC) in the early 1970s and was capable of performing 100 million floating point operations (MFLOPS). The CDC STAR-100 combined scalar and vector computations. Though it was able to achieve a peak performance of 20 MFLOPS, when fully loaded, it's performance for real-life data sets was much lower.

The first system to fully exploit the vector processing architecture was the Cray-1. The Cray-1, again developed by CDC, was able to overcome some of the pitfalls encounterd during the STAR-100 project. The STAR-100 took a lot of time decoding vector instructions and also had to re-fetch data every time an instruction asked for it. The Cray-1 moved away from the memory-memory architecture and introduced a set of CPU registers which would not only pre-fetch frequently used data,but also successive instructions, thus introducing pipelining. This enabled Cray-1 to work on more flexible data-sets and also improved it's instruction fetch & decoding times. But the registers introduced a limit on the vector sizes and also made the system expensive. Even with the drawbacks, the Cray-1 managed to perform at 80 MFLOPS.

Following the Cray-1, several supercomputers based on vector processing principles, followed. Companies like Fujitsu with VP100 & VP200, Hitachi with S810 and NEC with SX series, entered the fray. Floating Point Systems (FPS) came up with a minicomputer with floating point co-processors, the AP-120B. It consisted of array coprocessors exclusively performing floating point operations. It made the conventional minicomputer faster for floating point operations and also less expensive. With advances in the technology, the computers based on vector and array processing models, kept getting faster, owing to faster clock speeds and faster switching gates.

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