Yoshifumi Takamoto

A scalable, cost-effective, and flexible disk system using high-performance embedded-processors

As a scalable, cost-effective, and flexible solution for data-intensive systems, we are exploring active-network-storage (ANS), which is an array of ANS disk drives. The ANS drive improves flexibility by using a modular software design; that is, users can specify functions of the ANS drive by loading/unloading the corresponding modules on it. To keep the ANS drive cost-effective, users are allowed to choose whether native code modules or platform-independent Java-bytecode modules are executed on the drive. We forecast that a current high-performance embedded-processor is powerful enough to enable this modular design to be implemented and to provide a scalable, cost-effective, and flexible ANS system. We have confirmed our forecast by conducting an experiment with an ANS drive prototype with a 200 MHz embedded-processor running database sequential scanning and NFS, which are typical off-loaded functions with different characteristics. To evaluate scalability and cost-effectiveness of the ANS system, we estimated the throughput from measurements on our ANS prototype, and we compared it with the throughput that was measured on a 450 MHz Pentium II Xeon server. Our estimation indicates that the scan throughput of the ANS system increases up to 71 MB/s while that of the server saturates at 25 MB/s because of its CPU bottleneck. The NFS read/write throughputs of two ANS drives surpassed the server maximum throughputs.

A relational database system architecture based on a vector processing method

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 Hitachis 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.

A relational database system architecture based on a vector-processing method

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 Hitachis 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.