Home → Magazine Archive → April 2015 (Vol. 58, No. 4) → Hadoop Superlinear Scalability → Abstract

Hadoop Superlinear Scalability

By Neil J. Gunther, Paul Puglia, Kristofer Tomasette

Communications of the ACM, Vol. 58 No. 4, Pages 46-55

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"We often see more than 100% speedup efficiency!" came the rejoinder to the innocent reminder that you cannot have more than 100% of anything. This was just the first volley from software engineers during a presentation on how to quantify computer-system scalability in terms of the speedup metric. In different venues, on subsequent occasions, that retort seemed to grow into a veritable chorus that not only was superlinear speedup commonly observed, but also the model used to quantify scalability for the past 20 years—Universal Scalability Law (USL)—failed when applied to superlinear speedup data.

Indeed, superlinear speedup is a bona fide phenomenon that can be expected to appear more frequently in practice as new applications are deployed onto distributed architectures. As demonstrated here using Hadoop MapReduce, however, the USL is not only capable of accommodating superlinear speedup in a surprisingly simple way, it also reveals that superlinearity, although alluring, is as illusory as perpetual motion.


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