Capability for Multi-Core and Many-Core Memory Systems: A Case-Study With Xeon Processors

Abstract

The general volume of data has exploded to unimaginable levels in the past decade. Therefore, the big data analytics has become an area of focus. Many frameworks have been developed for data analytics, such as Hadoop, Spark, etc. Most of the frameworks are built on multi-core or many-core memory systems, requiring developers and users to have an in-depth understanding of the architectures to take full advantage of the hardware. In this paper, we present a comprehensive study of both multi-core and many-core memory systems and discuss the different characteristics, including core, cache, memory, and the on-chip network. Furthermore, we propose a simple but effective mechanism for cache false-sharing overhead that can reduce a large number of LLC-load/store instructions and LLC cache-misses. In addition, we conduct detailed experiments with Ligra, a graph analytics framework, on four different-sized datasets. The results show that it can achieve up to a 2.5× and 9.5× speed-up for multi-core and many-core memory systems, respectively. We finally share our key findings and discuss the platform development on multi-core and many-core memory systems.

Keywords

• Data analytics
• multi-core
• many-core
• cache false-sharing