OS Scheduling Algorithms for Improving the Performance of Multithreaded Workloads

TL;DR

This paper proposes new OS scheduling algorithms that optimize cache-to-cache transfers and remote DRAM accesses in multicore servers, significantly improving performance for multithreaded workloads by reducing latency.

Contribution

It introduces a unified approach with new algorithms that consider cache affinity and remote memory access patterns to enhance OS scheduling for multicore systems.

Findings

Up to 16.79% reduction in overall latency.

New algorithms outperform existing methods in synthetic workloads.

Effective for varying remote cache and DRAM latencies.

Abstract

Major chip manufacturers have all introduced multicore microprocessors. Multi-socket systems built from these processors are used for running various server applications. However to the best of our knowledge current commercial operating systems are not optimized for multi-threaded workloads running on such servers. Cache-to-cache transfers and remote memory accesses impact the performance of such workloads. This paper presents a unified approach to optimizing OS scheduling algorithms for both cache-to-cache transfers and remote DRAM accesses that also takes cache affinity into account. By observing the patterns of local and remote cache-to-cache transfers as well as local and remote DRAM accesses for every thread in each scheduling quantum and applying different algorithms, we come up with a new schedule of threads for the next quantum taking cache affinity into account. This new schedule cuts down both remote cache-to-cache transfers and remote DRAM accesses for the next scheduling quantum and improves overall performance. We present two algorithms of varying complexity for optimizing cache-to-cache transfers. One of these is a new algorithm which is relatively simpler and performs better when combined with algorithms that optimize remote DRAM accesses. For optimizing remote DRAM accesses we present two algorithms. Though both algorithms differ in algorithmic complexity we find that for our workloads they perform equally well. We used three different synthetic workloads to evaluate these algorithms. We also performed sensitivity analysis with respect to varying remote cache-to-cache transfer latency and remote DRAM latency. We show that these algorithms can cut down overall latency by up to 16.79% depending on the algorithm used.