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Cloud computing offers some great opportunities for science, but most cloud computing platforms are I/O and memory limited, and hence are poor matches for data-intensive computing. After 4 years of research software development we are now instrumenting and benchmarking our analysis pipelines; numbers, lessons learned, and future plans will be discussed. Everything is open source.
Random algorithms and probabilistic data structures are algorithmically efficient and can provide shockingly good practical results. I will give a practical introduction, with live demos and bad jokes, to this fascinating algorithmic niche. I will conclude with some discussions of how our group has applied this to large sequencing data sets (although this will not be the focus of the talk).