Signal reliability, as a measure of digital systems' reliability, has not been used until recently due to lack of efficient evaluation methods. A new approach to the evaluation of signal reliability is presented in this work. A reliability transfer function of digital systems is defined and a method for its evaluation is presented. This approach provides a new insight into the problem of digital system reliability. Furthermore, it simplifies signal reliability calculations and can easily be mechanized.

Most of the replacement algorithms devised and implemented largely depend on program behavior, in other words, to optimally select the parameters of these algorithms program behavior or at least a probability model of it should be known. The page fault frequency (PFF) algorithm adapts to dynamic changes in program behavior during execution. Therefore its performance is expected to be less dependent on prior knowledge of the program behavior during execution. Therefore its performance is expected to be less dependent on prior knowledge of the program behavior and input data.

The discrete Monte Carlo method for the solution of partial differential equations has been studied theoretically and experimentally. A special purpose digital computer, the PDE machine, was designed and constructed from macromodules, and Monte Carlo solutions for illustrative problems were obtained. Error analysis has been made and experimental outcomes were compared with theoretical results. Using Monte Carlo methods the PDE machine has been proved to be very efficient from the viewpoint of accuracy and speed in comparison to the general purpose digital computer. Speeds of one solution every 2 seconds were obtained for Poisson's equations with a typical accuracy of better than 2 percent of the range of solutions.

Efficient memory management is important for optimizing computer usage. Intuition, simulation, experience, and analysis have contributed to the design of space management algorithms. Analytical models require accurate and concise descriptions of the system's environment. The referencing pattern, describing the sequence of memory addresses, is the environment for memory management problems. One referencing model assigns probabilities to positions in an LRU stack of memory pages. “Local” behavior is easily described using this model. However, the distinctly different behavior among instruction and data references is lost. In this paper we generalize the LRU stack model to an arbitrary number of memory spaces which are selected by the transitions of a Markov chain. The additional detail affects the behavior of the models for a working set management strategy.