Presto: Distributed Machine Learning and Graph Processing with Sparse Matrices by Shivaram Venkataraman, Erik Bodzsar, Indrajit Roy, Alvin AuYoung, and Robert S. Schreiber.
It is cumbersome to write machine learning and graph algorithms in data-parallel models such as MapReduce and Dryad. We observe that these algorithms are based on matrix computations and, hence, are inefﬁcient to implement with the restrictive programming and communication interface of such frameworks.
In this paper we show that array-based languages such as R  are suitable for implementing complex algorithms and can outperform current data parallel solutions. Since R is single-threaded and does not scale to large datasets, we have built Presto, a distributed system that extends R and addresses many of its limitations. Presto efﬁciently shares sparse structured data, can leverage multi-cores, and dynamically partitions data to mitigate load imbalance. Our results show the promise of this approach: many important machine learning and graph algorithms can be expressed in a single framework and are substantially faster than those in Hadoop and Spark.
Your mileage may vary but the paper reports that for PageRank, Presto is 40X faster than Hadoop and 15X Spark.
Unfortunately I can’t point you to any binary or source code for Presto.
Still, the description is an interesting one at a time of rapid development of computing power.