PUMA: From Simultaneous to Parallel for Shared Memory System in Multi-core

In contemporary multi-core systems, memory is shared among a number of concurrent threads. Memory contention and interference are becoming increasingly severe incurring such problems as performance degradation, unfair resource sharing and priority inversion. In this paper, we aim at the challenge of improving performance and fairness for concurrent threads while minimizing energy consumption in main memory. Therefore, we propose PUMA, a novel solution that reduces memory contention and interference by judiciously partitioning threads among cores and allocating each core exclusive memory banks and bandwidth based on thread’s characteristics. Our results demonstrate that PUMA is able to improve both performance and fairness while reducing energy consumption significantly compared to existing memory management approaches.