Large scale graph analytics is essential to analyze relationships in big data sets. Thereto, the DARPA HIVE program targets a leap in power efficient graph analytics. In response to this program, Intel proposes the Programmable Unified Memory Architecture (PUMA). Based on graph workload analysis insights, PUMA consists of many multi-threaded cores, fine-grained memory and network accesses, a globally shared address space and powerful offload engines. In this talk, we will describe the PUMA architecture, both in terms of hardware and the software ecosystem. We will provide initial simulation based performance estimations, showing that for graph analysis applications, a PUMA node will outperform a conventional compute node by one to two orders of magnitude. Additionally, PUMA will continue to scale across multiple nodes, which is a challenge in conventional multinode setups.
