Lock–Unlock

Abstract

A plethora of optimized mutex lock algorithms have been designed over the past 25 years to mitigate performance bottlenecks related to critical sections and locks. Unfortunately, there is currently no broad study of the behavior of these optimized lock algorithms on realistic applications that consider different performance metrics, such as energy efficiency and tail latency. In this article, we perform a thorough and practical analysis of synchronization, with the goal of providing software developers with enough information to design fast, scalable, and energy-efficient synchronization in their systems. First, we perform a performance study of 28 state-of-the-art mutex lock algorithms, on 40 applications, on four different multicore machines. We consider not only throughput (traditionally the main performance metric) but also energy efficiency and tail latency, which are becoming increasingly important. Second, we present an in-depth analysis in which we summarize our findings for all the studied applications. In particular, we describe nine different lock-related performance bottlenecks, and we propose six guidelines helping software developers with their choice of a lock algorithm according to the different lock properties and the application characteristics. From our detailed analysis, we make several observations regarding locking algorithms and application behaviors, several of which have not been previously discovered: (i) applications stress not only the lock–unlock interface but also the full locking API (e.g., trylocks, condition variables); (ii) the memory footprint of a lock can directly affect the application performance; (iii) for many applications, the interaction between locks and scheduling is an important application performance factor; (vi) lock tail latencies may or may not affect application tail latency; (v) no single lock is systematically the best; (vi) choosing the best lock is difficult; and (vii) energy efficiency and throughput go hand in hand in the context of lock algorithms. These findings highlight that locking involves more considerations than the simple lock/unlock interface and call for further research on designing low-memory footprint adaptive locks that fully and efficiently support the full lock interface, and consider all performance metrics.

Research on Heterogeneous Data Fusion Technology of Substation Secondary Equipment

“…Each substation system often sends data to multiple master stations at the same time, which requires high concurrency and synchronization of real-time data in substations [9]. The report of the substation monitoring system adopts multithreading processing.…”

Mutable locks: Combining the best of spin and sleep locks

“…Recently, a study that compares a wide range of lock implementations has been presented by Guerraoui et al 22 The study clearly shows that no single lock is systematically the best, and that choosing the best one on basis of the hardware and workload characteristics may be not simple. In addition, the authors propose a practical selection procedure that can help the developer to choose a good locking algorithm depending on various factors, such as the number of cores/threads, the scheduler load and the lock contention level.…”

“…Specifically, in this study we include: (1) MCS with the spin-then-sleep policy (denoted as mcs-sts), 22 that is, an MCS lock where spinning threads go to sleep after a given time, (2) Malthusian locks (denoted as malthusian), and (3) Mutexee (denoted as mutexee). The details about these lock proposals can be found in Section 2.…”