The Timing Drive Placement (TDP) system balances wirability and timing constraints so that the final released design meets timing criteria. This is achieved by dynamically evaluating the timing of critical paths during placement. TDP is significant because convergence to a timed wirable solution early in the physical design cycle is achieved, or else it becomes apparent that logic changes are required.

This paper presents quadratic programming models of memory conflict in multiprocessor systems where main memory consists of a set of memory modules common to all processors. Two jobs (programs) are said to be in conflict, or subject to memory conflict, whenever at a given time portions of them must be executed in the same memory module by different processors. We are interested in minimizing the total conflict by the proper assignment of jobs to main memory. Two allocation models are considered: one in which the jobs-to-memory assignment is to be made independent of any particular processors-to-jobs schedule, that is, expected memory conflict is to be minimized over the space of all schedules; and the second in which a definite processor schedule is assumed to be available. For both models, algorithms are formulated for the assignment of jobs to memory.

This paper presents an approach to achieve high central processing unit (CPU) availability with an increase in performance by multiprocessing on an instruction stream level, where instruction fetching/executing is done by closely coupled processing units (PU's). A treatment is given of the necessary control for coordination of the PU's. This processing interaction is accomplished by microcode shared by the units. Either PU can be interchanged in any processing function, and the total processing complex comprises a single CPU as far as the external world (i. e., the operating system and user's programs) is concerned. The results of manual simulation on two sample problems are given along with a comparison of processing with a single PU and with another instruction stream multiprocessing scheme presented in [4].

This paper is concerned with approximation methods for handling the classical assignment problem. These methods permit solution of large scale assignment