The paper describes a new string matching algorithm that is suitable for vector processors. The hardware implementation of the algorithm is also presented. The algorithm consists of two parts. In the first part, candidate strings that are similar to pattern strings are extracted from a text string (cutout part). Candidate strings may include noise strings, and these are removed in the second part of the algorithm. Each part is efficiently vectorized using vector instructions of conventional vector processors for numerical computations. Moreover, the cutoff part is implemented as an added instruction of the Integrated Database Processor (IDP). Measurements show that this algorithm using IDP is more than 10 times faster than a scalar program using the Aho-Corasick method.>

The problems of utilizing an extended vector processor called IDP (Integrated Database Processor) for nonnumerical processing are discussed. IDP has highly pipelined merge and search vector facilities in addition to conventional vector facilities. This extended vector processor can be utilized not only for primitive relational operations but also for the following applications: (1) complex relational query using multiple indexes; (2) multiway merge/sort utility, and (3) generation and test processing. Vectorization methods and performance improvements are described and analyzed.>

Several techniques for running Prolog programs on pipelined vector processors, such as the Hitachi S-820 or the Cray-2, are developed. This paper presents an automatic program transformation (vectorization) method of Prolog, which enables a type of or-parallel execution of Prolog programs using vector operations. Performance is evaluated on the Hitachi S-810 using the Eight-Queens problem. Its vector execution speed is 4.5 MLIPS (18 ms). This is eight or nine times faster than scalar execution. This result confirms the effectiveness of vectorization techniques and applicability of vector processors to Prolog execution and to symbol processing applications.

A new relational database system architecture based on a vector processing method is presented. The similarity between table and vector structures was studied. This similarity implies that vector processors have high potential for the improvement of Relational Data Base Management System (RDBMS) performance. In commercial RDBMSs, however, data records are internally organized in complex pointer structures, to which vector processing is not directly applicable. We propose a new vectorization method by which data in the pointer structures are dynamically rearranged into vector form, and collectively processed using a pipelined vector processor. A new pipelined vector processor called an Integrated Database Processor (IDP) which is particularly suitable for our vectorization strategy was developed. IDP is designed as an optional hardware for a general purpose computer. The vectorization method and IDP are applied to Hitachi's new, recently-announced, commercial RDBMS (called RDB1/IDP). In this paper, the vectorization method and the architecture of IDP are described. The performance improvement is also described and analyzed.