Autotuning of MPI Applications Using PTF

Sikora, Anna; Eduardo, César; Comprés, Isaías; Gerndt, Michael

doi:10.1145/2916026.2916028

Cited by 16 publications

(8 citation statements)

References 15 publications

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“…For example, the optimal MPI algorithm for a collective operation often depends on the message size, the target hardware architecture and number of processes used. In [33] Periscope is extended to automatically optimize the values of MPI configuration parameters. The approach supports performance analysis and tuning of MPI parameters at runtime.…”

Section: Related Workmentioning

confidence: 99%

A performance- and energy-oriented extended tuning process for time-step-based scientific applications

et al. 2020

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Scientific application codes are often long-running time-and energy-consuming parallel codes, and the tuning of these methods towards the characteristics of a specific hardware is essential for a good performance. However, since scientific software is often developed over many years, the application software usually survives several hardware generations, which might make a re-tuning of the existing codes necessary. To simplify the tuning process, it would be beneficial to have software with inherent tuning possibilities. In this article, we explore the possibilities of tuning methods for time-step-based applications. Two different time-step-based application classes are considered, which are solution methods for ordinary differential equations and particle simulation methods. The investigation comprises a broad range of tuning possibilities, starting from the choice of algorithms, the parallel programming model, static implementation variants, input characteristics as well as hardware parameters for parallel execution. An experimental investigation shows the different characteristics of the application classes on different multicore systems. The results show that a combination of offline and online tuning leads to good tuning results. However, due to the different input characteristics of the two application classes, regular versus irregular, different tuning aspects are most essential.

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Section: Related Workmentioning

confidence: 99%

A performance- and energy-oriented extended tuning process for time-step-based scientific applications

et al. 2020

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“…In [16], Sikora et al extend again Periscope as part of the AutoTune project to implement autotuning capabilities for MPI applications. e output of the framework proposed is a set of tuning recommendation that can be integrated into the production version of the code.…”

Section: Related Workmentioning

confidence: 99%

AITuning: Machine Learning-based Tuning Tool for Run-Time Communication Libraries

Fanfarillo¹,

Vento²

2019

Preprint

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we address the problem of tuning communication libraries by using a deep reinforcement learning approach. Reinforcement learning is a machine learning technique incredibly e ective in solving game-like situations. In fact, tuning a set of parameters in a communication library in order to get be er performance in a parallel application can be expressed as a game: Find the right combination/path that provides the best reward. Even though AITuning has been designed to be utilized with di erent run-time libraries, we focused this work on applying it to the OpenCoarrays run-time communication library, built on top of MPI-3. is work not only shows the potential of using a reinforcement learning algorithm for tuning communication libraries, but also demonstrates how the MPI Tool Information Interface, introduced by the MPI-3 standard, can be used e ectively by run-time libraries to improve the performance without human intervention.

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“…Studies have also led a subset of researchers to frame autotuning solutions at compile time [2,9,12,27,32] and a few others at run time of applications [37,3,40,8]. In addition, a few autotuning solutions with more emphasis to IOs were designed in [5].…”

Section: S Benedictmentioning

confidence: 99%

SCALE-EA: A Scalability Aware Performance Tuning Framework for OpenMP Applications

Benedict¹

2018

SCPE

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Abstract. HPC application developers, including OpenMP-based application developers, have stepped forward to endeavor the future design trends of exa-scale machines, such as, increased number of threads/cores, heterogeneous architectures, multiple levels of memories, and so forth; and, they have initiated procedures to address application level challenges, such as, data-driven scalability issues, energy consumption requirements, data availability needs, and so forth. Despite the existence of manual performance tuning solutions, users still deem it to be an intricate process. This paper proposes a scalability aware autotuning framework (SCALE-EA) that automatically identifies an efficient number of threads for OpenMP parallel regions using a Firefly Algorithm ( 1. Introduction. High Performance Computing (HPC) application developments are invariably cropping up among various scientific domains, such as, High Energy Physics (HEP), bioinformatics, eyewear computing, visualizations, electronic automation, graph-based machine learning, and so forth. OpenMP based programming model is indeed reaching out to become a prominent programming model among a sector of HPC application developers owing to the adequate doctrine of standards (OpenMP 4.0 and 4.5), ease of use, controlled programming support, smooth applicability to programmers belonging to various scientific disciplines, and due to the notion of having millions of cores in future exascale machines.However, the realization of efficiently utilizing HPC applications in its present form for future large scale machines requires innovative approaches to mitigate the following possible risky scenarios:1. the performance of applications becomes more sensitive to data movement, data availability, data provenance, data management policies, and so forth -a future software-cum-hardware computing system must consider the massive storage options of machines, resiliency nature of applications, dynamic computing behavior of applications, and the dynamic nature of the data access patterns of applications (big data). 2. the current implementations of OpenMP applications might not have considered the design aspects of emerging memory models (including data persistence of modern memory architectures), infrastructural improvements, future parallel data structures, and so forth. 3. the scalability of applications might get an impoverished lead as applications are usually not ported and tested for scalable machines. 4. the energy efficiency of applications could exhibit a daunting scenario when executed on machines with varying degrees of parallelism -smaller or larger. 5. the current OpenMP application developers might not have quantified the possible uncertainties that might evolve due to the underlying future parallel software frameworks. In short, to mitigate these challenges, programmers or developers have to diligently write scalable and energy efficient parallel algorithms by employing the apt scalability features of programming languages and by considering the underlying require...

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Autotuning of MPI Applications Using PTF

Cited by 16 publications

References 15 publications

A performance- and energy-oriented extended tuning process for time-step-based scientific applications

A performance- and energy-oriented extended tuning process for time-step-based scientific applications

AITuning: Machine Learning-based Tuning Tool for Run-Time Communication Libraries

SCALE-EA: A Scalability Aware Performance Tuning Framework for OpenMP Applications

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