PDL ABSTRACT

The Blacklisting Memory Scheduler: Achieving High Performance and Fairness at Low Cost

Proceedings of the 32nd IEEE International Conference on Computer Design (ICCD), Seoul, South Korea, October 2014.

Lavanya Subramanian, Donghyuk Lee, Vivek Seshadri, Harsha Rastogi, Onur Mutlu

Carnegie Mellon University

http://www.pdl.cmu.edu/

In a multicore system, applications running on different cores interfere at main memory. This inter-application interference degrades overall system performance and unfairly slows down applications. Prior works have developed applicationaware memory request schedulers to tackle this problem. State-of-the-art application-aware memory request schedulers prioritize memory requests of applications that are vulnerable to interference, by ranking individual applications based on their memory access characteristics and enforcing a total rank order.

In this paper, we observe that state-of-the-art applicationaware memory schedulers have two major shortcomings. First, ranking applications individually with a total order based on memory access characteristics leads to high hardware cost and complexity. Second, ranking can unfairly slow down applications that are at the bottom of the ranking stack. To overcome these shortcomings, we propose the Blacklisting Memory Scheduler (BLISS), which achieves high system performance and fairness while incurring low hardware cost and complexity. BLISS design is based on two new observations. First, we find that, to mitigate interference, it is sufficient to separate applications into only two groups, one containing applications that cause interference and another containing applications vulnerable to interference, instead of ranking individual applications with a total order. Vulnerable-to-interference group is prioritized over the interference-causing group. Second, we show that this grouping can be efficiently performed by simply counting the number of consecutive requests served from each application – an application that has a large number of consecutive requests served is dynamically classified as interference-causing.

We evaluate BLISS across a wide variety of workloads and system configurations and compare its performance and complexity with five state-of-the-art memory schedulers. Our evaluations show that BLISS achieves 5% better system performance and 25% better fairness than the best-performing previous memory scheduler while greatly reducing critical path latency and hardware area cost of the memory scheduler (by 79% and 43%, respectively).

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