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Multiwavelength optical content-addressable parallel processor for high-speed parallel relational database processing

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APPLIED OPTICS 1999年第26期38卷 5594-5604页

作者： Choo, PY Detofsky, A Louri, A Univ Arizona Dept Elect & Comp Engn Tucson AZ 85721 USA

We present a novel, to our knowledge, architecture for parallel database processing called the multiwavelength optical content-addressable parallel processor (MW-OCAPP). The MW-OCAPP is designed to provide efficient parallel data retrieval and processing by means of moving the bulk of database operations from electronics to optics. It combines a parallel model of computation with the many-degrees-of-processing freedom that light provides. The MW-OCAPP uses a polarization and wavelength-encoding scheme to achieve a high level of parallelism. Distinctive features of the proposed architecture include (1) the use of a multiwavelength encoding scheme to enhance processing parallelism, (2) multicomparand word-parallel bit-parallel equality and magnitude comparison with an execution time independent of the data size or the word size, (3) the implementation of a suite of 11 database primitives, and (4) multicomparand two-dimensional data processing. The MW-OCAPP architecture realizes 11 relational database primitives: difference, intersection, union, conditional selection, maximum, minimum, join, product, projection, division, and update. Most of these operations execute in constant time, independent of the data size. We outline the architectural concepts and motivation behind the MW-OCAPP's design and describe the architecture required for implementing the equality and intersection-difference processing cores. Additionally, a physical demonstration of the multiwavelength equality operation is presented, and a performance analysis of the proposed system is provided. (C) 1999 Optical Society of America.

关键词： .Intersection multiwavelength execution time equality parallel Processor parallel relational database MW-OCAPP

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High performance parallel query processing on a 100 node ATM connected PC cluster

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IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS 1999年第1期E82D卷 54-63页

作者： Tamura, T Oguchi, M Kitsuregawa, M Mitsubishi Elect Corp Informat & Commun Syst Dev Ctr Kamakura Kanagawa 2470056 Japan Univ Tokyo Inst Ind Sci Tokyo 1068558 Japan

We developed a PC cluster system which consists of 100 PCs as a test bed for massively parallel query processing. Each PC employs the 200 MHz Pentium Pro CPU and is connected with others through an ATM switch. Because the query processing applications are insensitive to the communication latency and mainly perform integer operations, the ATM connected PC cluster approach can be considered a reasonable solution for high performance database servers with low costs. However, there has been no challenge to construct large scale PC clusters for database applications, as far as the authors know. Though we employed commodity components as much as possible. we developed the DBMS itself, because that was a key component for obtaining high performance in parallel query processing, and there seemed no system which could meet our demand. On each PC node, a server program which acts as a database kernel is running to process the queries in cooperation with other nodes. The kernel was designed to execute pipelined operators and handle voluminous data efficiently, to achieve high performance on complex decision support type queries. We used the standard benchmark, TPC-D, on a 100 GB database to verify the feasibility of our approach, through comparison of our system with commercial parallel systems. As a whole, our system exhibited sufficiently high performance which was competitive with the current TPC-D top records, in spite of not using indices. For some heavy queries in the benchmark, which have high selectivity and joinability, our system performed much better. In addition, we applied transposed file organization to the database for further performance improvement. The transposed file organization vertically partitions the tuples, enabling attribute-by-attribute access to the relations. This resulted in significant performance improvement by reducing the amount of disk I/O and shifting the bottleneck to computation.

关键词： PC cluster parallel relational database parallel query processing TPC-D benchmark

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