A study of the scalability of stop-the-world garbage collectors on multicores

Gidra, Lokesh; Thomas, Gaël; Sopena, Julien; Shapiro, Marc

doi:10.1145/2451116.2451142

Cited by 51 publications

(23 citation statements)

References 24 publications

(15 reference statements)

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“…As corroborated by a large amount of work in operating systems [12,14,18,19,35,61,70,74], databases [36,44,62,63,75], programming languages [37,58], parallel runtimes [7,9,41,52], key-value stores [15,51], and synchronization [22-24, 32, 45], system developers need to optimize software for the target platform to achieve good performance. We discuss below selected examples of multi-core optimizations.…”

Section: Related Workmentioning

confidence: 99%

“…Psaroudakis et al [63] describe how data placement and access patterns can affect the performance of database workloads on modern NUMA machines. In the same vein, Gidra et al [37,38] improve the performance of the JVM garbage collector by mainly optimizing memory placement.…”

Section: Related Workmentioning

confidence: 99%

“…This tendency makes the task of developers very challenging, for they need to fine-tune software to the underlying hardware in order to achieve performance (e.g., [12,18,19,30,37]). Furthermore, optimizing for specific multi-core topologies hinders software portability.…”

Section: Introductionmentioning

confidence: 99%

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Abstracting Multi-Core Topologies with MCTOP

Chatzopoulos

Guerraoui

Harris

et al. 2017

Proceedings of the Twelfth European Conference on Computer Systems

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Portability and efficiency are usually antagonists in multicore computing. In order to develop efficient code, one needs to take into account the topology of the target multi-cores (e.g., for locality). This clearly hampers code portability. In this paper, we show that you can have the cake and eat it too.We introduce MCTOP, an abstraction of multi-core topologies augmented with important low-level hardware information, such as memory bandwidths and communication latencies. We show how to automatically generate MCTOP using libmctop, our library that leverages the determinism of cache-coherence protocols to infer the topology of multi-cores using only latency measurements.MCTOP enables developers to accurately and portably define high-level performance optimization policies. We illustrate several such policies through four examples: (i-ii) thread placement in OpenMP and in a MapReduce library, (iii) a topology-aware mergesort algorithm, as well as (iv) automatic backoff schemes for locks. We illustrate the portability of these optimizations on five processors from Intel, AMD, and Oracle, with low effort.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Abstracting Multi-Core Topologies with MCTOP

Chatzopoulos

Guerraoui

Harris

et al. 2017

Proceedings of the Twelfth European Conference on Computer Systems

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“…Since an atomic operation becomes much slower over inter-socket links, proposals for scalable NUMAaware locks rely on hierarchically partitioned structures to maximize access locality [9][10]. On the system level, a recent study on the performance of garbage collectors on multisocket multicores analyzes synchronization patterns and systematically removes bottlenecks without completely redesigning the system [11]. We take inspiration from these works as we redesign our storage manager for multisockets.…”

Section: A Multisocket Multicoresmentioning

confidence: 99%

ATraPos: Adaptive transaction processing on hardware Islands

Porobic

Liarou

Tözün

et al. 2014

2014 IEEE 30th International Conference on Data Engineering

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Abstract-Nowadays, high-performance transaction processing applications increasingly run on multisocket multicore servers. Such architectures exhibit non-uniform memory access latency as well as non-uniform thread communication costs. Unfortunately, traditional shared-everything database management systems are designed for uniform inter-core communication speeds. This causes unpredictable access latencies in the critical path. While lack of data locality may be a minor nuisance on systems with fewer than 4 processors, it becomes a serious scalability limitation on larger systems due to accesses to centralized data structures.In this paper, we propose ATraPos, a storage manager design that is aware of the non-uniform access latencies of multisocket systems. ATraPos achieves good data locality by carefully partitioning the data as well as internal data structures (e.g., state information) to the available processors and by assigning threads to specific partitions. Furthermore, ATraPos dynamically adapts to the workload characteristics, i.e., when the workload changes, ATraPos detects the change and automatically revises the data partitioning and thread placement to fit the current access patterns and hardware topology.We prototype ATraPos on top of an open-source storage manager Shore-MT and we present a detailed experimental analysis with both synthetic and standard (TPC-C and TATP) benchmarks. We show that ATraPos exhibits performance improvements of a factor ranging from 1.4 to 6.7x for a wide collection of transactional workloads. In addition, we show that the adaptive monitoring and partitioning scheme of ATraPos poses a negligible cost, while it allows the system to dynamically and gracefully adapt when the workload changes.

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“…Furthermore, studies have shown that garbage collection can take up to one-third of total execution time of an application [9,26]. As such, any scalability study must decompose the overall execution time into at least two components: time spent in executing the application or mutator time and time spent in performing garbage collection or GC time.…”

Section: Introductionmentioning

confidence: 99%

Exploiting FIFO Scheduler to Improve Parallel Garbage Collection Performance

et al. 2016

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Recent studies have found that parallel garbage collection performs worse with more CPUs and more collector threads. As part of this work, we further investigate this phenomenon and find that poor scalability is worst in highly scalable Java applications. Our investigation to find the causes clearly reveals that efficient multi-threading in an application can prolong the average object lifespan, which results in less effective garbage collection. We also find that prolonging lifespan is the direct result of Linux's Completely Fair Scheduler due to its round-robin like behavior that can increase the heap contention between the application threads. Instead, if we use pseudo first-in-first-out to schedule application threads in large multicore systems, the garbage collection scalability is significantly improved while the time spent in garbage collection is reduced by as much as 21%. The average execution time of the 24 Java applications used in our study is also reduced by 11%. Based on this observation, we propose two approaches to optimally select scheduling policies based on application scalability profile. Our first approach uses the profile information from one execution to tune the subsequent executions. Our second approach dynamically collects profile information and performs policy selection during execution.

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A study of the scalability of stop-the-world garbage collectors on multicores

Cited by 51 publications

References 24 publications

Abstracting Multi-Core Topologies with MCTOP

Abstracting Multi-Core Topologies with MCTOP

ATraPos: Adaptive transaction processing on hardware Islands

Exploiting FIFO Scheduler to Improve Parallel Garbage Collection Performance

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