A future is a language construct that allows programmers to expose parallelism in applicative languages such as MultiLisp [5] with minimal effort. In this paper we describe a technique for implementing futures, which we call leapfrogging, that reduces blocking due to load imbalance. The utility of leapfrogging is enhanced by the fact that it is completely platform-independent, is free from deadlock, and places a bound on stack sizes that is at most a small constant times the maximum stack size encountered during a sequential execution of the same computation. We demonstrate the performance of leapfrogging using a prototype implementation written in C++.

This paper presents an Approximate Mean Value Analysis model of deflection routing in Shuffle-Loop interconnection networks. (The methodology is readily extended to other network topologies.) In contrast to most previous work on deflection routing, the model makes no assumptions about traffic patterns, nor does it assume that messages that cannot be admitted to the network are lost. The technique allows the network to be modeled in its entirety: all processors, switches, and memory modules, and their steady-state interactions, are modeled explicitly. The results of the model are found to be in close agreement with the results of simulation experiments.

This paper concerns the parallel simulation of queueing network models (QNMs) using the conservative (Chandy-Misra) paradigm. Most empirical studies of conservative parallel simulation have used QNMs as benchmarks. For the most part, these studies concluded that the conservative paradigm is unsuitable for speeding up the simulation of QNMs, or that it is only suitable for simulating a very limited subclass of these models (e.g., those containing only FCFS servers). In this paper we argue that these are unnecessarily pessimistic conclusions. On the one hand, we show that the structure of some QNMs inherently limits the attainable simulation speedup. On the other hand, we show that QNMs without such limitations can be efficiently simulated using some recently introduced implementation techniques.We present an analytic method for determining an upper bound on speedup, and use this method to identify QNM structures that will exhibit poor simulation performance. We then survey a number of promising implementation techniques, some of which are quite general in nature and others of which apply specifically to QNMs. We show how to extend the latter to a larger class of service disciplines than had been considered previously.