Parallelism-Aware Batch-Scheduling: Enhancing both Performance and Fairness of Shared DRAM Systems

Onur Mutlu and Thomas Moscibroda

Abstract

In a chip-multiprocessor (CMP) system, the DRAM system is shared among cores. In a shared DRAM system, requests from a thread can not only delay requests from other threads by causing bank/bus/row-buffer conflicts but they can also destroy other threads’ DRAM-bank-level parallelism. Requests whose latencies would otherwise have been overlapped could effectively become serialized. As a result both fairness and system throughput degrade, and some threads can starve for long time periods.

This paper proposes a fundamentally new approach to designing a shared DRAM controller that provides quality of service to threads, while also improving system throughput. Our parallelism-aware batch scheduler (PAR-BS) design is based on two key ideas. First, PARBS processes DRAM requests in batches to provide fairness and to avoid starvation of requests. Second, to optimize system throughput, PAR-BS employs a parallelism-aware DRAM scheduling policy that aims to process requests from a thread in parallel in the DRAM banks, thereby reducing the memory-related stall-time experienced by the thread. PAR-BS seamlessly incorporates support for system-level thread priorities and can provide different service levels, including purely opportunistic service, to threads with different priorities.

We evaluate the design trade-offs involved in PAR-BS and compare it to four previously proposed DRAM scheduler designs on 4-, 8-, and 16-core systems. Our evaluations show that, averaged over 100 4-core workloads, PAR-BS improves fairness by 1.11X and system throughput by 8.3% compared to the best previous scheduling technique, Stall-Time Fair Memory (STFM) scheduling. Based on simple request prioritization rules, PAR-BS is also simpler to implement than STFM.

Details

Publication typeInproceedings
Published in35th International Symposium on Computer Architecture (ISCA)
PublisherAssociation for Computing Machinery, Inc.
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