Work-preserving real-time emulation of meshes on butterfly networks

The emulation of a guest network G on a host network H is work-preserving and real-time if the inefficiency, that is the ratio WG/WH of the amounts of work done in both networks, and the slowdown of the emulation are O(1). In this thesis we show that an infinite number of meshes can be emulated on a butterfly in a work-preserving real-time manner, despite the fact that any emulation of an s x s-node mesh in a butterfly with load 1 has a dilation of Ω(logs). The recursive embedding of a mesh in a butterfly presented by Koch et al. (STOC 1989), which forms the basis for our work, is corrected and generalized by relaxing unnecessary constraints. An algorithm determining the parameter for each stage of the recursion is described and a rigorous analysis of the resulting emulation shows that it is work-preserving and real-time for an infinite number of meshes. Data obtained from simulated embeddings suggests possible improvements to achieve a truly work-preserving emulation of the class of meshes on the class of butterflies

Time Lower Bounds for Parallel Network Computations

Direct Acyclic Graphs (DAGs) are a suitable way to describe computations, expressing precedence constraints among operations. Beyond the representation of the execution of an algorithm, a DAG can effectively represent the execution of a parallel network. This last kind of DAG has a regular structure, consisting in the repetition over time of the original network; these common representations suggest a possible uniform approach in the study of execution of algorithms and emulation of networks. Both in parallel computing and computational complexity, DAGs have been extensively employed in the study of algorithmic features, as lower bounds for the execution/emulation time of algorithms/networks, the minimum quantity of memory needed for computing an algorithm or the minimum I/O complexity of an algorithm given a certain amount of fast memory cells. Developed techniques are quite different in their assumptions; one of the more fundamental differences is that some of them allow recomputation of intermediate results, while others disallow it, requiring the storage in memory of intermediate results for their further usages. In nowadays computations the trade-off between data recomputation and data storing is important both in parallel and in local elaborations, since in the former we can increase the bandwidth and reduce the latency with whom data can be accessed (by computing the same data in several points of the network), while in the latter we can avoid to pay the latency of the access in memory to reload data, by recomputing them possibly loading fewer data or using data already present in memory. So far it does not exist an universal technique able to foresee the strict lower bound for each execution of algorithm or emulation of network in each network and the known results derive from several theorems. On the contrary there are a lot of cases for which it neither exists a tight result; among these there are also emulations of extensively studied networks, such as multidimensional arrays. The first part of our thesis starts from this state-of-the-art: we propose a survey of several known lower bound techniques involving DAGs, followed by original theorems which clarify or solve open problems. In particular, in our survey we consider lower bound techniques for execution of algorithms and emulation of networks in parallel networks, showing their principles and their limits. In the discussion we show relationships among theorems, proving that no one of them is better of the others in general terms: there are counter-examples in which each theorem gives better bounds than others. We also exhibit examples where no bound among the considered techniques is tight. Moreover we generalize some theorems originally suited for network emulations, adapting them to execution of general DAGs in parallel networks, showing examples of their application. We also consider theorems for determining minimum I/O complexity, presenting similarities and differences with emulation theorems. One of the main results of the thesis is a new general technique which provides lower bounds almost tight (except for a logarithmic factor) in a class of network emulations including multidimensional arrays. We improve previously better known results which have a polynomial gap between lower bound and actual emulation time. Our theorem considers emulations with recomputation, giving results valid in the most general context. Finally we consider the role of recomputation in performance, trying to understand when it gives a real advantage respect to storing intermediate results in memory. In particular we introduce the problem in simple networks, showing a class of them in which recomputation can not improve I/O performance, ending in butterfly DAGs where recomputation can save a number of I/O accesses at least as big as the fast memory available during the computation. The approach used highlights the difficulty of exploit recomputation in executions of algorithms when their DAG representation exhibits an high bisection bandwidth