Adaptive TB-LMI: An efficient memory controller and scheduler design

In the modern multi-core systems, concurrently executing applications share common resource such as main memory. Memory scheduling algorithms are developed to resolve memory contention among competing applications so that throughput is high and fairness of the overall multi-core system is guaranteed. In this paper, we present Adaptive Time-based Least Memory Intensive (Adaptive TB-LMI) scheduling, a new memory scheduling algorithm that addresses both fairness and system performance. Adaptive TB-LMI is based on TB-LMI which prioritizes applications according to their memory contention every pre-defined CPU cycle. Adaptive TB-LMI dynamically prioritizes applications according to their memory contention. Considering the previous algorithms with the best performance, for 16-core system, TB-LMI improves system throughput on average by 2.25X and 36% comparing to FCFS and TCM respectively. Adaptive TB-LMI is 6% better than the TB-LMI with static threshold. In terms of fairness and slowdown metrics, TB-LMI show improvements of 30% and 3X, respectively, compared to FCFS and, 18% and 8%, respectively, compared to TCM. Adaptive TB-LMI and TB-LMI are 15% more efficient in energy-delay product, although they are within 5% in terms of area overhead. Moreover, TCM has an area overhead of about 45% more than Adaptive TB-LMI. In terms of Energy-Delay Product, Adaptive TB-LMI is 10% and 24% better than TB-LMI and TCM, respectively. This is due to the dynamic capabilities of the adaptive algorithm to change the rate of the SQ, hence reducing the energy consumption.