How Many Threads will be too Many? On the Scalability of OpenMP Implementations

Iwainsky, Christian; Shudler, Sergei; Calotoiu, Alexandru; Strube, Alexandre; Knobloch, Michael; Bischof, Christian; Wolf, Felix

doi:10.1007/978-3-662-48096-0_35

Cited by 22 publications

(13 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Engelmann [65] evaluates MPI runs for up to 2 24 nodes using simulations. Iwainsky et al [97] experiment with the scalability of OpenMP constructs. Results show that the OpenMP overhead is compiler-dependent and scales linearly with the number of threads, which is undesirable for massively parallel processors.…”

Section: Mpi and Openmp According To Gropp And Snirmentioning

confidence: 99%

The Landscape of Exascale Research

et al. 2020

View full text Add to dashboard Cite

The next generation of supercomputers will break the exascale barrier. Soon we will have systems capable of at least one quintillion (billion billion) floating-point operations per second (10 18 FLOPS). Tremendous amounts of work have been invested into identifying and overcoming the challenges of the exascale era. In this work, we present an overview of these efforts and provide insight into the important trends, developments, and exciting research opportunities in exascale computing. We use a three-stage approach in which we (1) discuss various exascale landmark studies, (2) use data-driven techniques to analyze the large collection of related literature, and (3) discuss eight research areas in depth based on influential articles. Overall, we observe that great advancements have been made in tackling the two primary exascale challenges: energy efficiency and fault tolerance. However, as we look forward, we still foresee two major concerns: the lack of suitable programming tools and the growing gap between processor performance and data bandwidth (i.e., memory, storage, networks). Although we will certainly reach exascale soon, without additional research, these issues could potentially limit the applicability of exascale computing.

show abstract

Section: Mpi and Openmp According To Gropp And Snirmentioning

confidence: 99%

The Landscape of Exascale Research

et al. 2020

View full text Add to dashboard Cite

show abstract

“…The model generator has already shown to confirm known performance models of real applications as well as discover previously unknown scalability bottlenecks [13,27], and has also been validated using a wide range of synthetic functions [12]. Furthermore, specific usage examples include modeling the performance of OpenMP constructs [18] and the isoefficiency functions of task-based programs [25].…”

Section: Empirical Modelingmentioning

confidence: 99%

Understanding the Scalability of Molecular Simulation Using Empirical Performance Modeling

Shudler

Vrabec

Wolf

2019

Programming and Performance Visualization Tools

Self Cite

View full text Add to dashboard Cite

Molecular dynamics (MD) simulation allows for the study of static and dynamic properties of molecular ensembles at various molecular scales, from monatomics to macromolecules such as proteins and nucleic acids. It has applications in biology, materials science, biochemistry, and biophysics. Recent developments in simulation techniques spurred the emergence of the computational molecular engineering (CME) field, which focuses specifically on the needs of industrial users in engineering. Within CME, the simulation code ms2 allows users to calculate thermodynamic properties of bulk fluids. It is a parallel code that aims to scale the temporal range of the simulation while keeping the execution time minimal. In this paper, we use empirical performance modeling to study the impact of simulation parameters on the execution time. Our approach is a systematic workflow that can be used as a blueprint in other fields that aim to scale their simulation codes. We show that the generated models can help users better understand how to scale the simulation with minimal increase in execution time.

show abstract

“…The collected data then train a classifier which can then spot false sharing in applications. Automated performance modeling can be used to examine the scalability of OpenMP runtime constructs [12], or to analyze the memory access patterns of OpenMP applications [4].…”

Section: Related Workmentioning

confidence: 99%

ScalOMP: Analyzing the Scalability of OpenMP Applications

Daumen

Carribault

Thomas

2019

OpenMP: Conquering the Full Hardware Spectrum

View full text Add to dashboard Cite

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.

show abstract

How Many Threads will be too Many? On the Scalability of OpenMP Implementations

Cited by 22 publications

References 21 publications

The Landscape of Exascale Research

The Landscape of Exascale Research

Understanding the Scalability of Molecular Simulation Using Empirical Performance Modeling

ScalOMP: Analyzing the Scalability of OpenMP Applications

Contact Info

Product

Resources

About