K. M. Chandy
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Models for parallel processing within programs: application to CPU: I/O and I/O: I/O overlap
Approximate queueing models for internal parallel processing by individual programs in a multiprogrammed system are developed in this paper. The solution technique is developed by network decomposition. The models are formulated in terms of CPU:I/O and I/O:I/O overlap and applied to the analysis of these problems. The percentage performance improvement from CPU:I/O overlap is found to be greatest for systems which are in approximate CPU:I/O utilization balance and for low degrees of multiprogramming. The percentage improvement from I/O:I/O overlap is found to be greatest for systems in which the I/O system is more utilized than the CPU.
Memory management and response time
This paper presents a computationally tractable methodology for including accurately the effects of finite memory size and workload memory requirements in queueing network models of computer systems. Empirical analyses and analytic studies based on applying this methodology to an actual multiaccess interactive system are reported. Relations between workload variables such as memory requirement distribution and job swap time, and performance measures such as response time and memory utilization are graphically displayed. A multiphase, analytically soluble model is proposed as being broadly applicable to the analysis of interactive computer systems which use nonpaged memories.
A comparison of list schedules for parallel processing systems
The problem of scheduling two or more processors to minimize the execution time of a program which consists of a set of partially ordered tasks is studied. Cases where task execution times are deterministic and others in which execution times are random variables are analyzed. It is shown that different algorithms suggested in the literature vary significantly in execution time and that the B-schedule of Coffman and Graham is near-optimal. A dynamic programming solution for the case in which execution times are random variables is presented.
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