Download Automatic Algorithm Recognition and Replacement: A New by Robert Metzger PDF

By Robert Metzger

Parallel computation becomes the norm within the coming many years. regrettably, advances in parallel have a long way outpaced parallel purposes of software program. There are presently methods to employing parallelism to functions. One is to write down thoroughly new purposes in new languages. yet leaving behind purposes that paintings is unacceptable to such a lot nonacademic clients of high-performance desktops. the opposite technique is to transform current purposes to a parallel shape. this is performed manually or immediately. Even partial luck in doing the task immediately has noticeable financial advantages.This e-book describes a essentially new theoretical framework for locating bad algorithms in an software application and changing them with ones that parallelize the code.

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Extra resources for Automatic Algorithm Recognition and Replacement: A New Approach to Program Optimization

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It is intended to be complementary to existing vectorization techniques. The chief contribution of this work is the definition of a computation graph, which is a modified extension of the program dependence graph (18). This graph represents loop nests. It is created by working from the inner-most loop in a nest outwards. A loop is unrolled so that each of its loop carried dependencies are between two consecutive iterations. The graph of the loop is replicated three times, representing the “initial,” “middle,” and “final” iterations.

Variation: This system presupposes that a vectorizing compiler has already processed their input first. They do not address the issue of variation. but the side effect of their approach is that some sources of variation will be eliminated by the scalar and loop transformations that such a compiler must perform to be effective. Focus: The issue of focus is not discussed explicitly. The inference one can draw from the paper is that they intend to process all loops that were not vectorized by traditional data dependence analysis and loop transformations.

Algorithm pattern matching determines whether a subprogram is an algorithm instance of any member of a database of algorithm patterns. l l Algorithm instance testing is at least as difficult as graph isomorphism testing. topological properties of heuristic algorithms need tractice. rogram and replace them ngrams is an unsolvable @alence testing. c is an algorithm instance prphism testing. 1 Overview This chapter surveys and evaluates a variety of projects related to our work that can be roughly categorized as Program Understanding efforts.

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