NGraph: Parallel Graph Processing in Hybrid Memory Systems

Challenges in Parallel Graph Processing

Graph algorithms are becoming increasingly important for solving many problems in scientific computing, data mining and other domains. As these problems grow in scale, parallel computing resources are required to meet their computational and memory requirements. Unfortunately, the algorithms, software, and hardware that have worked well for developing mainstream parallel scientific applications...

Massively-Parallel Graph Processing

The goal of this project is to understand the challenges in porting graph algorithms to commodity, hybrid platforms; platforms that consist of processors optimized for sequential processing and accelerators optimized for massively-parallel processing. This study fills the gap between current graph processing platforms that are either expensive (e.g., supercomputers) or inefficient (e.g., commod...

Scalable Distributed Query Processing in Parallel Main-Memory Database Systems

The continuous increase in compute speed and main-memory capacity of modern servers triggered the development of a new generation of in-memory database systems. These systems completely rewrote the traditional database architecture to use main memory as primary storage. Discarding several now obsolete abstractions of disk-based database systems enabled unprecedented query performance on a singl...

Parallel Graph Rewriting Systems

Graph Processing in Main-Memory Column Stores