Hardware Considerations for Faster and Deeper Insights from your Large Scale Graph

Fundamentally graph analytics workloads exhibit different compute, memory and network characteristics as compared to the traditional workloads. The traditional hardware arch efficiency techniques pose significant bottlenecks to obtaining performance at scale with graph workloads. This session discusses the design considerations for a pointer-chasing graph analytics hardware, drawing parallels to Intel’s Programmable Integrated Unified Memory Architecture (PIUMA) innovation whose goal is to enable real-time analytics on large-scale data to drive deeper and faster insights.

  • Nikhil Deshpande, Director, AI and HPC Innovations, Intel, Office of the CTO

Download the session slides here.