Exercising the NON-VON Primary Processing Subsystem

The Primary Processing Subsystem of the NON-VON supercomputer potentially may comprise thousands of custom nMOS integrated circuits. It is vital that faulty components be detected and located. This paper provides a collection of algorithms to exercise the Primary Processing Subsystem so that the manifestation of latent faults may be observed

A Knowledge-Based Expert Systems Primer and Catalog

For more than 20 years, artificial intelligence techniques have been applied to the development of computer programs that solve difficult problems. Although several expert systems are well known, it is all too easy to circumscribe the field based on these few examples. The purpose of this paper is to present the fundamentals of the field (the Primer), and to give a broad overview via concise descriptions of many rule-based expert systems and knowledge engineering frameworks (the Catalog)

Execution or OPS5 Production Systems on a Massively Parallel Machine

In recent years, the development of expert systems implemented by rule-based production systems has emerged as one of the dominant paradigms in the field of artificial intelligence. While production systems offer important advantages in large-scale AI applications, their use in such applications is typically very costly in execution time. In this paper, we describe an algorithm for executing production systems expressed in the OPS5 language on a massively parallel multiple-SIMD machine called NON-VON, portions of which are currently under construction at Columbia University. The algorithm, a parallel adaptation of Forgy's Rete Match, has been implemented and tested on an instruction-level simulator. We present a detailed performance analysis, based on the implemented code, for the averaged characteristics of six production systems having an average of 910 inference rules each. The analysis predicts an execution rate of more than 850 production firings per second using hardware comparable in cost to a VAX 11/780. By way of comparison, a LISP-based OPS5 interpreter running on a VAX 11/780 typically fires 1 to 5 rules per second, while a Bliss-based interpreter executes 5 to 12 rules per second

NON-VON's Performance on Certain Database Benchmarks

In a paper by Hawthorn and DeWitt, the projected performance of several proposed database machines was examined for three relational database queries. The present paper investigates the performance of a massively parallel machine called NON-VON for the same queries under comparable assumptions. In the case of simple queries, a NON-VON machine of comparable size to those considered by Hawthorn and DeWitt is found to be somewhat faster than the fastest machines examined in their study; for a more complex database operation, NON-VON is shown to be five to ten times faster than the fastest of these machines

Allocation and Manipulation of Records in the NON-VON Supercomputer

NON-VON is a highly parallel supercomputer, portions of which are now under construction at Columbia University. A full-scale NON-VON prototype might comprise as many as a million tiny processing elements, each associated with a small random access memory. Among the principal goals of the NON-VON Project is the development of programming languages and compilers that realize the machine's potential for massive parallelism while insulating the user from the details of its tree-structured physical topology. One conceptual metaphor that has proven useful in pursuing this goal is the notion of an intelligent record, a primitive data element of arbitrary size that functions as if it were associated with its own dedicated computer. This paper describes the essential mechanisms used to support intelligent records within a high-level parallel programming language environment. We then illustrate the use of these techniques in a few simple applications and explore certain time/space tradeoffs that characterize alternative record allocation schemes

Performance comparison of ide and scsi disks

It is widely believed that the IDE disks found in PCs are inexpensive but slow, whereas the SCSI disks used in servers and workstations are faster, more reliable, and more manageable. The belief that current IDE disks have performance and reliability disadvantages has been called into question by several recent reports. Thus we consider the possibility of achieving tremendous cost advantages by using IDE disks as the foundation of a storage system. In this paper, we give an extensive performance comparison of IDE and SCSI disks. We measure their performance on a variety of micro benchmarks and macro benchmarks, and we explain these results with the help of kernel instrumentation and device activity traces collected by a SCSI analyzer. We consider the impact of several factors, including sequential vs. random workloads, file system enhancements such as journaling and Soft Updates, I/O scheduling in the kernel vs. in the disk drive (as enabled by tagged queuing), and the use of RAID technology to obtain I/O parallelism. In our testbed we find that the IDE disk is faster than the SCSI disk for sequential I/O, but the SCSI disk is faster for random I/O. We also observe that the random I/O performance deficit of the IDE disk is partly overcome by kernel I/O scheduling, and is further mitigated by scheduling in the drive (as enabled by tagged queuing), and by the use of journaling and Soft Updates. Taken as a whole, our results lead us to conclude that RAID systems based on IDE drives can be both faster and significantly less expensive than SCSI RAID systems.

The NON-VON Database Machine: An Overview

The NON-VON machine (portions of which are presently under construction in the Department of Computer Science at Columbia, in cooperation with the Knowledge Base Management Systems Project at Stanford) was designed to apply computational parallelism on a rather massive scale to a large share of the information processing functions now performed by digital computers. The NON-VON architecture comprises a tree-structured Primary Processing Subsystem (PPS), which we are implementing using custom nMOS VLSI chips, Secondary Processing Subsystem (SPS) incorporating modified, highly intelligent disk drives. NON-VON should permit particularly dramatic performance improvements in very large scale data manipulation tasks, including relational database operations and external sorting. This paper briefly describes the structure and function of the NON-VON machine and its constituent processing subsystems

Modeling and optimizing I/O throughput of multiple disks on a bus

In modern I/O architectures, multiple disk drives are attached to each I/O controller. A study of the performance of such architectures under I/O-intensive workloads has revealed a performance impairment that results from apreviously unknown form of convoy behavior in disk I/O. In this paper, we describe measurements of the read performance of multiple disks that share a SCSI bus under a heavy workload, and develop and validate formulas that accurately characterize the observed performance (to within 12 % on several platforms for I/O sizes in the range 16{128 KB). Two terms in the formula clearly characterize the lost performance seen in our experiments. We describe techniques to deal with the performance impairment, via user-level workarounds that achieve greater overlap of bus transfers with disk seeks, and that increase the percentage of transfers that occur at the full bus bandwidth rather than at the lower bandwidth of a disk head. Experiments show bandwidth improvements of 10{20 % when using these user-level techniques, but only in the case of large I/Os