There are two distinct types of MIMD (Multiple Instruction, Multiple Data) computers: the shared memory machine, e.g. Butterfly, and the distributed memory machine, e.g. Hypercubes, Transputer arrays. Typically these utilize different programming models: the shared memory machine has monitors, semaphores and fetch-and-add; whereas the distributed memory machine uses message passing. Moreover there are two popular types of operating systems: a multi-tasking, asynchronous operating system and a crystalline, loosely synchronous operating system.
In this paper I firstly describe the Butterfly, Hypercube and Transputer array MIMD computers, and review monitors, semaphores, fetch-and-add and message passing; then I explain the two types of operating systems and give examples of how they are implemented on these MIMD computers. Next I discuss the advantages and disadvantages of shared memory machines with monitors, semaphores and fetch-and-add, compared to distributed memory machines using message passing, answering questions such as “is one model ‘easier’ to program than the other?” and “which is ‘more efficient‘?”. One may think that a shared memory machine with monitors, semaphores and fetch-and-add is simpler to program and runs faster than a distributed memory machine using message passing but we shall see that this is not necessarily the case. Finally I briefly discuss which type of operating system to use and on which type of computer. This of course depends on the algorithm one wishes to compute. 相似文献
Capturing the waiting times (at an external queue) for a customer to access a movable resource is an important step towards measuring customer service and system performance in manufacturing, logistics, communication and health care systems. Such waiting time measures are typically used for sizing resource and buffer capacities, and thereby minimising customer waiting time probabilities. In this regard, semi-open queuing networks (SOQNs), which decouple the arriving customers/transactions from the network resources using a synchronisation station (also known as a semaphore queue), can potentially capture the customer/transaction waiting times/costs more precisely and provide a rich network modelling construct. Hence, modelling manufacturing or service systems using SOQNs is an important step towards measuring customer flow times (sojourn times) wherein the customer waiting times at an external queue are a critical component. In this paper, we present several stochastic models for manufacturing and service systems using SOQNs and also discuss the potential applications of SOQNs. We then review the solution methods for SOQNs and also compare the numerical accuracies for three promising methods. Finally, we include the potential research areas in SOQNs. 相似文献