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Scalability! But at what COST?

frankmcsherry.org · 2,922 words · saved by 1 readers

Michael Isard, Derek Murray, and I recently sent in a HotOS submission (it’s not blind, so no harm talking about it, we think). The subject is hinted at from the post title (stolen from the paper title): Big data systems may scale well, but this can often be just because they introduce a lot of overhead. Rather than making your computation go faster, the systems introduce substantial overheads which can require large compute clusters just to bring under control. In many cases, you’d be better off running the same computation on your laptop. Here is the set-up: we took several recent graph-processing publications from the systems community, and compared the measurements they report to simple single-threaded implementations running on my work laptop (RIP). We wrote competent implementations, but we didn’t obsess deeply over fancy algorithms, cunning data-dependent tweaks, or what have you. I’ll show you the code, and you decide. We evaluated PageRank (20 iterations) and graph connectivit

Michael Isard, Derek Murray, and I recently sent in a HotOS submission (it’s not blind, so no harm talking about it, we think). The subject is hinted at from the post title (stolen from the paper title): Big data systems may scale well, but this can often be just because they introduce a lot of overhead. Rather than making your computation go faster, the systems introduce substantial overheads which can require large compute clusters just to bring under control. In many cases, you’d be better off running the same computation on your laptop. Methodology Here is the set-up: we took several recen

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