Cost Estimation of Parallel Constrained Producer-Consumer Algorithms

Cost estimation is crucial in the performance modeling of parallel algorithms and allocation of computational resources on distributed systems. This paper presents a novel methodology for estimating the cost of constrained producer-consumer (CPC) algorithms. In CPC algorithms, the computation is performed by classes of nodes (tasks), separated in time. The methodology combines data flow analysis with communication latencies to determine the production and consumption of data on different processors, which helps in determining the amount of computations and communication. The cost metric that we develop in this paper uses computational imbalances and communication load, and determines a single cost value. The resulting metric is unique, as it provides the first model that targets CPC algorithms. It has wide application in Genetic Algorithms, molecular dynamics, scheduling schemes and computational epidemiology. We provide a general method for determining the application-specific constants of the cost metric. As an example, we extract the constants for EpiSimdemics (a highly scalable contagion simulator), and give guidelines for applying the procedure to other CPC algorithms. Our evaluations show that the cost metric estimated the execution times of a contagion simulator with less than a 6.5% error. The metric can be used in optimal assignment of computational resources.