Near-Data-Processing Architectures Performance Estimation and Ranking using Machine Learning Predictors

Abstract

The near-data processing (NDP) paradigm has emerged as a promising solution for the memory wall challenges of future computing architectures. Modern 3D-stacked DRAM systems can be exploited to prevent unnecessary data movement between the main memory and the CPU. To date, no standardized simulation frameworks or benchmarks are available for the systematic evaluation of NDP systems. Identifying which type of high-performance 3D memory is suitable to use in an NDP system remains a challenge. This is mainly due to the fact that understanding the interactions between modern workloads and the memory subsystem is not a trivial task. Each memory type has its advantages and drawbacks. Additionally, memory access patterns vary greatly across applications. As a result, the performance of a given application on a given memory type is difficult to intuitively predict. There is no specific memory type that can effectively provide high performance for all applications.In this work, we propose a machine learning framework that can efficiently decide which NDP system is suitable for an application. The framework relies on performance prediction based on an input set of application characteristics. For each NDP system we are examining, we build a machine learning model that can accurately predict performance of previously unseen applications on this system. Our models are on average 200x faster than architectural simulation. They can accurately predict performance with coefficients of determination ranging between 0.88 and 0.92, and root mean square errors ranging between 0.08 and 0.19.