Performance Impact of Emerging Memory Technologies on Big Data Applications
Mu-Tien Chang, I. Stephen Choi, Dimin Niu, Hongzhong Zheng · 2018
This paper presents a performance analysis framework for studying emerging memories. The key component of the framework is a memory-latency programmable emulator, which is based on a FPGA-attached server system. The emulator allows users extend read and/or write latency. In addition, we use regression models to enable system performance studies for memory latencies beyond hardware limitations. Finally, we demonstrate Spark application case studies, analyzing the impact of two key characteristics of emerging memories: extended memory access times and enlarged memory capacities. Results show that the benefit of high capacity memory could outweigh the performance loss due to longer memory latency.