False Sharing Detection in OpenMP Applications Using OMPT API

Ghane, Millad; Malik, Abid M.; Chapman, Barbara; Qawasmeh, Ahmad

doi:10.1007/978-3-319-24595-9_8

Cited by 7 publications

(5 citation statements)

References 13 publications

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“…The false sharing [21] effect on the inter-device level is the primary source of performance degradation. It is highly possible that when device d 1 is executing iteration i needs accessing another data that currently resides on device d 2 .…”

Section: Rodinia Benchmarksmentioning

confidence: 99%

Towards a portable hierarchical view of distributed shared memory systems

Ghane

Chandrasekaran

Cheung

2020

Proceedings of the Eleventh International Workshop on Programming Models and Applications for Multicores and Manycores

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An ever-growing diversity in the architecture of modern supercomputers has led to challenges in developing scientific software. Utilizing heterogeneous and disruptive architectures (e.g., off-chip and, in the near future, on-chip accelerators) has increased the software complexity and worsened its maintainability. To that end, we need a productive software ecosystem that improves the usability and portability of applications for such systems while allowing every parallelism opportunity to be exploited.In this paper, we outline several challenges that we encountered in the implementation of Gecko, a hierarchical model for distributed shared memory architectures, using a directive-based programming model, and discuss our solutions. Such challenges include: 1) inferred kernel execution with respect to the data placement, 2) workload distribution, 3) hierarchy maintenance, and 4) memory management.We performed the experimental evaluation of our implementation by using the Stream and Rodinia benchmarks. These benchmarks represent several major scientific software applications commonly used by the domain scientists. Our results reveal how the Stream benchmark reaches a sustainable bandwidth of 80 GB/s and 1.8 TB/s for single Intel Xeon Processor and four NVIDIA V100 GPUs, respectively. Additionally, the srad_v2 in the Rodinia benchmark reaches the 88% speedup efficiency while using four GPUs.CCS Concepts • Computer systems organization → Heterogeneous (hybrid) systems;

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Section: Rodinia Benchmarksmentioning

confidence: 99%

Towards a portable hierarchical view of distributed shared memory systems

Ghane

Chandrasekaran

Cheung

2020

Proceedings of the Eleventh International Workshop on Programming Models and Applications for Multicores and Manycores

Self Cite

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“…However, this approach is limited to memory-bound applications and only works on OpenMP applications using the static scheduler. In order to detect false-sharing, a recent work uses the OMPT API to instrument OpenMP constructs, and collects hardware counters at a fine granularity [9]. The collected data then train a classifier which can then spot false sharing in applications.…”

Section: Related Workmentioning

confidence: 99%

ScalOMP: Analyzing the Scalability of OpenMP Applications

Daumen

Carribault

Thomas

2019

OpenMP: Conquering the Full Hardware Spectrum

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Achieving good scalability from parallel codes is becoming increasingly difficult due to the hardware becoming more and more complex. Performance tools help developers but their use is sometimes complicated and very iterative. In this paper we propose a simple methodology for assessing the scalability and for detecting performance problems in an OpenMP application. This methodology is implemented in a performance analysis tool named ScalOMP that relies on the capabilities of OMPT for analyzing OpenMP applications. ScalOMP reports the code regions with scalability issues and suggests optimization strategies for those issues. The evaluation shows that ScalOMP incurs low overhead and that its suggestions lead to significant performance improvement of several OpenMP applications.

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“…The OpenMP support for tasks with dependencies has shown a growing interest from the community of developers and researchers, in various topics such as scheduling [4], data locality [3] and more generally performance optimization [6,7]. Thus, Perez et al propose an extension of the OpenMP task directive to apply dependencies between different family lineage of domain dependencies [2].…”

Section: Related Workmentioning

confidence: 99%

Detecting Non-sibling Dependencies in OpenMP Task-Based Applications

Ricardo¹,

Capra

Carribault

et al. 2019

OpenMP: Conquering the Full Hardware Spectrum

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The advent of the multicore era led to the duplication of functional units through an increasing number of cores. To exploit those processors, a shared-memory parallel programming model is one possible direction. Thus, OpenMP is a good candidate to enable different paradigms: data parallelism (including loop-based directives) and control parallelism, through the notion of tasks with dependencies. But this is the programmer responsibility to ensure that data dependencies are complete such as no data races may happen. It might be complex to guarantee that no issue will occur and that all dependencies have been correctly expressed in the context of nested tasks. This paper proposes an algorithm to detect the data dependencies that might be missing on the OpenMP task clauses between tasks that have been generated by different parents. This approach is implemented inside a tool relying on the OMPT interface.

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False Sharing Detection in OpenMP Applications Using OMPT API

Cited by 7 publications

References 13 publications

Towards a portable hierarchical view of distributed shared memory systems

Towards a portable hierarchical view of distributed shared memory systems

ScalOMP: Analyzing the Scalability of OpenMP Applications

Detecting Non-sibling Dependencies in OpenMP Task-Based Applications

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