Management of large quantities of complex data is essential in many advanced application areas. Object-oriented (OO) database management system have been developed to effectively model and process the complex domain knowledge. They have been shown to outperform some existing relational systems. The existing implementations of OO database management systems attempt to improve the efficiency of OO queries by explicitly capturing the relationships among objects. However, the execution of complex queries involving the retrieval of objects from many classes and relationships among them causes the existing system to operate inefficiently. In this paper, we present parallel algorithms for the processing of queries against a large OO database. The algorithms are based on a closed model of query processing pattern-based access instead of the conventional value-based access. During processing, the algorithms avoid the execution of time-consuming join operations by making use of the explicitly stored object associations. Generation of large quantities of temporary data is avoided by marking objects using their identifiers and by employing a two-phase query processing strategy. A query is processed by concurrent multiple waves, thereby improving parallelism avoiding the complexities introduced in their sequential implementation. The correctness and the performance of the parallel algorithms have been tested and analyzed by running parallel programs on a 32-node transputer based parallel machine designed and developed at the IBM Research Center at Yorktown Heights, New York. Benchmark queries of different semantic complexities are generated, and their performance is analyzed for various data and query parameters.>

This paper presents an analysis and evaluation of the performance of a multicomputer system (SM3) in supporting two basic matrix operations, namely multiplication and inversion. The system supports the efficient execution of the above mentioned operations by 1) achieving a high-bandwidth data transfer among computers by switching main memory modules, 2) supporting network partitioning, 3) employing a hardware communication and synchronization scheme, 4) using a distributed control technique, and 5) providing means to dynamically transfer control. Timing equations are derived and evaluated in an attempt to analyze the performance. Different cases which arise due to the relative sizes of memory modules and matrices during matrix multiplication are analyzed. The cases of partial and maximal pivoting during inversion are also analyzed. The SM3 system is compared quantitatively and qualitatively to a hypercube architecture.