Sidekick: Near Data Processing for Clustering Enhanced by Automatic Memory Disaggregation

Near Data Processing (NDP) is a promising solution for data mining/analysis techniques, which extract useful information from big data. In this paper, we propose a novel NDP-enabled memory disaggregation system called Sidekick, based on a type-2 CXL device and enhanced by an automated allocation technique for clustering algorithms. The key enabler of our migration technique is to understand clustering workflows in a unit of the program context, which is the function call stack for functions, threads, and memory allocations to drive the automated decision. The proposed technique relates the migrated computation tasks with a series of function calls and performs GA-based optimization to identify the optimal allocation scenario for a target clustering algorithm. In Scikit-learn, a popular machine learning library, we use the genetic algorithm to find the optimal memory allocation policy and the operation offloading policy using the program context. The results show that the proposed technique increases the clustering performance as compared to the case, which only uses disaggregated memory without NDP cores, by up to 92% in terms of execution time, while reducing the majority of remote CXL memory accesses.