Adaptive correlated prefetch with large-scale hybrid memory system for stream processing

Abstract

Owing to the exponential growth of real-time data generation, the importance of stream processing is ever increasing. However, the data processing paradigm of stream processing is quite different, so it is difficult to expect high performance from memory systems applied to existing data centers. To solve this problem, two main solutions are suggested in this paper. First, a hybrid main memory and small buffer architecture are designed to reflect the execution characteristics of stream processing. Second, a hardware-based prefetch module supports correlation prefetching. Stream processing tends to accept incoming data in the main memory, so the prefetch module is used to divert data from the main memory layer to the buffer layer based on an intelligent clustering algorithm. This clustering algorithm affects the rapidly changing data access pattern of stream processing applications. By using heterogeneous main memories, not only can one enjoy the fast access latency of DRAM but also its nonvolatility, scalability, and low power consumption. The proposed hybrid memory architecture with our prefetch buffer structure can improve the buffer hit rate by 9–14% over other prefetch methods, reduce energy consumption by 26% over the conventional DRAM-only model, and achieve similar execution time over the 1/8-size DRAM space of the DRAM-only model.