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

## TL;DR

This paper explores how to efficiently perform graph analytics on massive datasets using Intel Optane DC Persistent Memory, proposing principles that improve runtime performance and demonstrate competitiveness with cluster-based systems.

## Contribution

It introduces key runtime and algorithmic principles for graph analytics on large-memory systems with Optane PMM and evaluates their effectiveness on real-world data.

## Key findings

- Frameworks based on proposed principles outperform others
- Achieve performance comparable to large cluster systems
- Effective for analyzing extreme-scale graphs

## Abstract

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.