Exploring database workloads on future clustered many-core architectures

Abstract

Decision Support System (DSS) workloads are known to be one of the most time-consuming database workloads that process large data sets. Traditionally, DSS queries have been accelerated using large-scale multiprocessor. In this work we analyze the benefits of using future many-core architectures, more specifically on-chip clustered many-core architectures, for such workloads for accelerating DSS query execution and study their performance behavior. To achieve this goal we propose data-parallel versions of the original database scan and join algorithms. In our experiments we study the behavior of three queries from the standard DSS benchmark TPC-H executing on the Intel Single Chip Cloud Computing experimental processor (Intel SCC). The results show that parallelism can be well exploited by such architectures and also how the computational workload compared to the data size of each executed query can influence performance. Our results show linear scalability for queries where the computation to data size ratio is balanced.