Showing papers by "Gianfranco Bilardi published in 1997"
TL;DR: The augmented postdominator tree (APT) is introduced, a data structure which can be constructed in space and time proportional to the size of the program and which supports enumeration of a number of useful control dependence sets inTime proportional to their size.
Abstract: The control dependence relation plays a fundamental role in program restructuring and optimization. The usual representation of this relation is the control dependence graph (CDG), but the size of the CDG can grow quadratically with the input programs, even for structured programs. In this article, we introduce the augmented postdominator tree (APT), a data structure which can be constructed in space and time proportional to the size of the program and which supports enumeration of a number of useful control dependence sets in time proportional to their size. Therefore, APT provides an optimal representation of control dependence. Specifically, the APT data structure supports enumeration of the set cd(e), which is the set of statements control dependent on control-flow edge e, of the set conds(w), which is the set of edges on which statement w is dependent, and of the set cdequiv(w), which is the set of statements having the same control dependences as w. Technically, APT can be viewed as a factored representation of the CDG where queries are processed using an approach known as filtering search.
53 citations
26 Aug 1997
TL;DR: This paper analyzes the cost of performing broadcast, product and prefix computation on the idealfat-tree, a model proposed here to capture distance and bandwidth properties common to a variety of fat-tree networks.
Abstract: This paper analyzes the cost of performing broadcast, product and prefix computation on the ideal fat-tree, a model proposed here to capture distance and bandwidth properties common to a variety of fat-tree networks. Algorithms are developed and analyzed in terms of the capacity of channels at different levels of the fat-tree. Non trivial lower bounds are derived establishing the optimality of our algorithms for a wide range of channel capacities.
8 citations
07 Aug 1997
TL;DR: This work proposes a simple run-time estimator for the bias of the branch and discusses how to combine it with schedule selection, and shows that this approach yields better performance in the case of highly unpredictable branches.
Abstract: A general theoretical framework is developed for the study of branch speculation. The framework yields a systematic way to select the schedule in a given set that, for any (estimated) bias of the branch, minimizes the expected execution time. Among other things, it is shown that in some cases the optimal schedule is neither of those resulting from aggressively speculating on any given outcome of the conditional. Our results can be useful in either static or dynamic approaches. We propose a simple run-time estimator for the bias and discuss how to combine it with schedule selection. A number of examples motivate and illustrate the techniques, and show that our approach yields better performance in the case of highly unpredictable branches.
1 citations