Scheduling Tasks to Maximize Usage of Aggregate Variables in Place

Single-assignment languages with copy semantics have a very simple and approachable programming model. A na¨ıve implementation of the copy semantics that copies the result of every computation to a new location, can result in poor performance. Whereas, an implementation that keeps the results in the same location, when possible, can achieve much higher performance. In this paper, we present a greedy algorithm for in-place computation of aggregate (array and structure) variables. Our algorithm greedily picks the most profitable opportunities for in-place computation, then updates the scheduling and in-place constraints in the program graph. The algorithm runs in O(TlogT + EW V + V 2 ) time, where T is the number of in-placeness opportunities, EW is the number of edges and V the number of computational nodes in a program graph. We evaluate the performance of the code generated by the LabVIEWTM compiler using our algorithm against the code that performs no in-place computation at all, ...

• Type: Conference
• Year: 2009
• Where: CC
• Authors: Samah Abu-Mahmeed, Cheryl McCosh, Zoran Budimlic, Ken Kennedy, Kaushik Ravindran, Kevin Hogan, Paul Austin, Steve Rogers, Jacob Kornerup
• Keywords: Algorithm, CC 2009, Copy Semantics, In-place Computation