On the Application Task Granularity and the Interplay with the Scheduling Overhead in Many-Core Shared Memory Systems

Akhmetova, Dana; Kestor, Gokcen; Gioiosa, Roberto; Laure, Erwin

doi:10.1109/cluster.2015.65

Cited by 7 publications

(10 citation statements)

References 22 publications

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“…To enable a detailed and accurate analysis of task granularity, tgp resorts to vertical profiling [47], 4 collecting a carefully selected set of metrics from the whole system stack, aligning them via offline analysis. Moreover, thanks to calling-context profiling [5], 5 tgp identifies classes and methods where optimizations related to task granularity are needed, guiding developers towards useful optimizations through actionable profiles [88].…”

Section: Task-granularity Profilingmentioning

confidence: 99%

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Analysis and Optimization of Task Granularity on the Java Virtual Machine

Rosà

Romera

Binder

2019

ACM Trans. Program. Lang. Syst.

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Task granularity, i.e., the amount of work performed by parallel tasks, is a key performance attribute of parallel applications. On the one hand, fine-grained tasks (i.e., small tasks carrying out few computations) may introduce considerable parallelization overheads. On the other hand, coarse-grained tasks (i.e., large tasks performing substantial computations) may not fully utilize the available CPU cores, leading to missed parallelization opportunities. In this article, we provide a better understanding of task granularity for task-parallel applications running on a single Java Virtual Machine in a shared-memory multicore. We present a new methodology to accurately and efficiently collect the granularity of each executed task, implemented in a novel profiler (available open-source) that collects carefully selected metrics from the whole system stack with low overhead, and helps developers locate performance and scalability problems. We analyze task granularity in the DaCapo, ScalaBench, and Spark Perf benchmark suites, revealing inefficiencies related to fine-grained and coarse-grained tasks in several applications. We demonstrate that the collected task-granularity profiles are actionable by optimizing task granularity in several applications, achieving speedups up to a factor of 5.90×. Our results highlight the importance of analyzing and optimizing task granularity on the Java Virtual Machine.

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Section: Task-granularity Profilingmentioning

confidence: 99%

“…AspectJ [70] is a mainstream AOP language and weaver. 4 In addition to AOP, AspectJ has been used for various instrumentation tasks. Since version 5, AspectJ provides a reflection API which is fully aware of the AspectJ type system [138].…”

Section: Reification Of Supertype Informationmentioning

confidence: 99%

Analysis and Optimization of Task Granularity on the Java Virtual Machine

Rosà

Romera

Binder

2019

ACM Trans. Program. Lang. Syst.

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“…o job represents per-job overhead due to coordination needs of cores and memory pages [38], [39], and o task is the Since the dominated application in HPC workload is compute-bound, we interest in how the added I/O bound applications will influence the performance of HPC node. Our experiments in Sect.…”

Section: Discrete-event Simulatormentioning

confidence: 99%

Improving Per-Node Computing Efficiency by an Adaptive Lock-Free Scheduling Model

Zheng

Zhou

et al. 2018

IEICE Trans. Inf. & Syst.

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Job scheduling on many-core computers with tens or even hundreds of processing cores is one of the key technologies in High Performance Computing (HPC) systems. Despite many scheduling algorithms have been proposed, scheduling remains a challenge for executing highly effective jobs that are assigned in a single computing node with diverse scheduling objectives. On the other hand, the increasing scale and the need for rapid response to changing requirements are hard to meet with existing scheduling models in an HPC node. To address these issues, we propose a novel adaptive scheduling model that is applied to a single node with a many-core processor; this model solves the problems of scheduling efficiency and scalability through an adaptive optimistic control mechanism. This mechanism exposes information such that all the cores are provided with jobs and the tools necessary to take advantage of that information and thus compete for resources in an uncoordinated manner. At the same time, the mechanism is equipped with adaptive control, allowing it to adjust the number of running tools dynamically when frequent conflict happens. We justify this scheduling model and present the simulation results for synthetic and real-world HPC workloads, in which we compare our proposed model with two widely used scheduling models, i.e. multi-path monolithic and two-level scheduling. The proposed approach outperforms the other models in scheduling efficiency and scalability. Our results demonstrate that the adaptive optimistic control affords significant improvements for HPC workloads in the parallelism of the node-level scheduling model and performance.

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“…where o job represents the per-job overhead due to the coordination needs of cores and memory pages [50,51] and o task is the incremental cost to place each task. The values for o job and o task are based on estimates from real-world data-intensive workloads in the real world: o job = 0.1m s and o task = 50ns.…”

Section: Parameters For Simulation the Scheduler Decision Time (mentioning

confidence: 99%

Optimizing Job Coscheduling by Adaptive Deadlock-Free Scheduler

Zheng

et al. 2018

Mathematical Problems in Engineering

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It is ubiquitous that multiple jobs coexist on the same machine, because tens or hundreds of cores are able to reside on the same chip. To run multiple jobs efficiently, the schedulers should provide flexible scheduling logic. Besides, corunning jobs may compete for the shared resources, which may lead to performance degradation. While many scheduling algorithms have been proposed for supporting different scheduling logic schemes and alleviating this contention, job coscheduling without performance degradation on the same machine remains a challenging problem. In this paper, we propose a novel adaptive deadlock-free scheduler, which provides flexible scheduling logic schemes and adopts optimistic lock control mechanism to coordinate resource competition among corunning jobs. This scheduler exposes all underlying resource information to corunning jobs and gives them necessary utensils to make use of that information to compete resource in a free-for-all manner. To further relieve performance degradation of coscheduling, this scheduler enables the automated control over the number of active utensils when frequent conflict becomes the performance bottleneck. We justify our adaptive deadlock-free scheduling and present simulation results for synthetic and real-world workloads, in which we compare our proposed scheduler with two prevalent schedulers. It indicates that our proposed approach outperforms the compared schedulers in scheduling efficiency and scalability. Our results also manifest that the adaptive deadlock-free control facilitates significant improvements on the parallelism of node-level scheduling and the performance for workloads.

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On the Application Task Granularity and the Interplay with the Scheduling Overhead in Many-Core Shared Memory Systems

Cited by 7 publications

References 22 publications

Analysis and Optimization of Task Granularity on the Java Virtual Machine

Analysis and Optimization of Task Granularity on the Java Virtual Machine

Improving Per-Node Computing Efficiency by an Adaptive Lock-Free Scheduling Model

Optimizing Job Coscheduling by Adaptive Deadlock-Free Scheduler

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