acm-header
Sign In

Communications of the ACM

Practice

Hadoop Superlinear Scalability


Hadoop Superlinear Scalability, illustration

Credit: Steve Ball

back to top 

"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.


 

No entries found

Log in to Read the Full Article

Sign In

Sign in using your ACM Web Account username and password to access premium content if you are an ACM member, Communications subscriber or Digital Library subscriber.

Need Access?

Please select one of the options below for access to premium content and features.

Create a Web Account

If you are already an ACM member, Communications subscriber, or Digital Library subscriber, please set up a web account to access premium content on this site.

Join the ACM

Become a member to take full advantage of ACM's outstanding computing information resources, networking opportunities, and other benefits.
  

Subscribe to Communications of the ACM Magazine

Get full access to 50+ years of CACM content and receive the print version of the magazine monthly.

Purchase the Article

Non-members can purchase this article or a copy of the magazine in which it appears.
Sign In for Full Access
» Forgot Password? » Create an ACM Web Account