3D DRAM for Graph Processing Applications

3D DRAM for Graph Processing

Overview

This research explores the application of 3D DRAM technology to accelerate graph processing workloads. Graph algorithms are fundamental to many applications including social network analysis, knowledge graphs, biological networks, and scientific computing.

Research Direction

• Investigating how 3D DRAM’s high bandwidth and vertical integration can benefit graph traversal and processing
• TCAD simulation of 3D DRAM architectures optimized for graph workloads
• Analyzing memory access patterns and bandwidth requirements of common graph algorithms
• Co-designing memory hierarchy for improved performance and energy efficiency

TCAD Research

• Technology Computer-Aided Design (TCAD) simulations to validate architectural assumptions
• Physical-level modeling of 3D DRAM structures
• Performance and power analysis of graph processing kernels

Applications

• Graph neural networks (GNNs)
• Biological network analysis
• Knowledge graph processing
• Social network analytics
• Scientific graph algorithms

Significance

Understanding how emerging 3D memory technologies can be leveraged for graph-intensive workloads is crucial for future explorations of specialized hardware accelerators in data science and machine learning.