  We present ASYMP, a distributed graph processing system developed for the
timely analysis of graphs with trillions of edges. ASYMP has several
distinguishing features including a robust fault tolerance mechanism, a
lockless architecture which scales seamlessly to thousands of machines, and
efficient data access patterns to reduce per-machine overhead. ASYMP is used to
analyze the largest graphs at Google, and the graphs we consider in our
empirical evaluation here are, to the best of our knowledge, the largest
considered in the literature.
  Our experimental results show that compared to previous graph processing
frameworks at Google, ASYMP can scale to larger graphs, operate on more crowded
clusters, and complete real-world graph mining analytic tasks faster. First, we
evaluate the speed of ASYMP, where we show that across a diverse selection of
graphs, it runs Connected Component 3-50x faster than state of the art
implementations in MapReduce and Pregel. Then we demonstrate the scalability
and parallelism of this framework: first by showing that the running time
increases linearly by increasing the size of the graphs (without changing the
number of machines), and then by showing the gains in running time while
increasing the number of machines. Finally, we demonstrate the fault-tolerance
properties for the framework, showing that inducing 50% of our machines to fail
increases the running time by only 41%.
