장명환/신민경’s paper has been accepted in

Title: An Efficient PIM-Based Graph Engine on a Single Machine,
Author: Myung-Hwan Jang, Min-Kyeong Shin, Taehyeong Park, Yongjun Park, and Sang-Wook Kim
Abstract
With the increasing size of real-world networks, efficient analysis of large-scale graphs has become an important research area. To this end, we can consider Processing-in-Memory (PIM), which integrates processing units and main memory into a single chip, as a promising solution. Many studies have focused on enabling highly efficient processing of memory-intensive tasks by using PIM’s high internal bandwidth. To the best of our knowledge, However, there have been no studies related to the scenarios where the entire graph does not fit in main memory and data movement across storage, memory, and cache should be considered. Motivated by this, we propose RealGraphPIM, a new PIM-based graph engine, that processes large-scale real-world graphs efficiently on top of the original RealGraph, a state-of-the-art CPU-based graph engine. RealGraphPIM employs (1) asynchronous I/O to reduce wasting time in an idle state and (2) column-wise partitioning to reduce CPU workloads, thereby issuing I/O requests more frequently. Experimental results on real-world datasets show that RealGraphPIM outperforms dramatically state-of-the-art graph engines including a naive version of RealGraphPIM.