  Intel Optane DC Persistent Memory (Optane PMM) is a new kind of
byte-addressable memory with higher density and lower cost than DRAM. This
enables the design of affordable systems that support up to 6TB of randomly
accessible memory. In this paper, we present key runtime and algorithmic
principles to consider when performing graph analytics on extreme-scale graphs
on large-memory platforms of this sort.
  To demonstrate the importance of these principles, we evaluate four existing
shared-memory graph frameworks on large real-world web-crawls, using a machine
with 6TB of Optane PMM. Our results show that frameworks based on the runtime
and algorithmic principles advocated in this paper (i) perform significantly
better than the others, and (ii) are competitive with graph analytics
frameworks running on large production clusters.
