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Intel Optane DC Persistent Memory (Optane PMM) is a new kind ofbyte-addressable memory with higher density and lower cost than DRAM. Thisenables the design of affordable systems that support up to 6TB of randomlyaccessible memory. In this paper, we present key runtime and algorithmicprinciples to consider when performing graph analytics on extreme-scale graphson large-memory platforms of this sort.  To demonstrate the importance of these principles, we evaluate four existingshared-memory graph frameworks on large real-world web-crawls, using a machinewith 6TB of Optane PMM. Our results show that frameworks based on the runtimeand algorithmic principles advocated in this paper (i) perform significantlybetter than the others, and (ii) are competitive with graph analyticsframeworks running on large production clusters.

Single Machine Graph Analytics on Massive Datasets Using Intel Optane DC Persistent Memory