High-Performance and Energy-Effcient Memory Scheduler Design for Heterogeneous Systems

TL;DR

This paper introduces the Staged Memory Scheduler (SMS), a novel memory controller design that significantly improves performance and fairness in heterogeneous systems by decoupling key scheduling tasks.

Contribution

The paper proposes a new staged approach to memory scheduling that simplifies design and enhances performance and fairness in systems with CPU-GPU shared memory.

Findings

SMS achieves 41.2% performance improvement over previous methods.

SMS provides better fairness among CPU and GPU requests.

The design is less complex and more power-efficient.

Abstract

When multiple processor cores (CPUs) and a GPU integrated together on the same chip share the off-chip DRAM, requests from the GPU can heavily interfere with requests from the CPUs, leading to low system performance and starvation of cores. Unfortunately, state-of-the-art memory scheduling algorithms are ineffective at solving this problem due to the very large amount of GPU memory traffic, unless a very large and costly request buffer is employed to provide these algorithms with enough visibility across the global request stream. Previously-proposed memory controller (MC) designs use a single monolithic structure to perform three main tasks. First, the MC attempts to schedule together requests to the same DRAM row to increase row buffer hit rates. Second, the MC arbitrates among the requesters (CPUs and GPU) to optimize for overall system throughput, average response time, fairness and quality of service. Third, the MC manages the low-level DRAM command scheduling to complete requests while ensuring compliance with all DRAM timing and power constraints. This paper proposes a fundamentally new approach, called the Staged Memory Scheduler (SMS), which decouples the three primary MC tasks into three significantly simpler structures that together improve system performance and fairness. Our evaluation shows that SMS provides 41.2% performance improvement and fairness improvement compared to the best previous state-of-the-art technique, while enabling a design that is significantly less complex and more power-efficient to implement.