CADS: Core-Aware Dynamic Scheduler for Multicore Memory Controllers
This addresses performance inefficiencies in multicore processors for computing applications, though it is an incremental improvement over existing scheduling methods.
The paper tackles the bottleneck of memory controller scheduling in multicore processors by introducing CADS, a reinforcement learning-based scheduler that dynamically adapts to memory workloads, achieving 20% better CPI with PARSEC benchmarks and 16% better CPI with SPEC 2006 benchmarks.
Memory controller scheduling is crucial in multicore processors, where DRAM bandwidth is shared. Since increased number of requests from multiple cores of processors becomes a source of bottleneck, scheduling the requests efficiently is necessary to utilize all the computing power these processors offer. However, current multicore processors are using traditional memory controllers, which are designed for single-core processors. They are unable to adapt to changing characteristics of memory workloads that run simultaneously on multiple cores. Existing schedulers may disrupt locality and bank parallelism among data requests coming from different cores. Hence, novel memory controllers that consider and adapt to the memory access characteristics, and share memory resources efficiently and fairly are necessary. We introduce Core-Aware Dynamic Scheduler (CADS) for multicore memory controller. CADS uses Reinforcement Learning (RL) to alter its scheduling strategy dynamically at runtime. Our scheduler utilizes locality among data requests from multiple cores and exploits parallelism in accessing multiple banks of DRAM. CADS is also able to share the DRAM while guaranteeing fairness to all cores accessing memory. Using CADS policy, we achieve 20% better cycles per instruction (CPI) in running memory intensive and compute intensive PARSEC parallel benchmarks simultaneously, and 16% better CPI with SPEC 2006 benchmarks.