A Tool for Optimizing Runtime Parameters of Open MPI

Chaarawi, Mohamad; Squyres, Jeffrey M.; Gabriel, Edgar; Feki, Saber

doi:10.1007/978-3-540-87475-1_30

Cited by 31 publications

(10 citation statements)

References 6 publications

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“…In the second scenario, we demonstrate the flexibility and modularity of the OMPIO architecture by using the Open Tool for Parameter Optimizations (OTPO) [3] to tune collective I/O operations and parameters for a given test case. OTPO is a tool which can be used to optimize runtime parameters of Open MPI.…”

Section: Resultsmentioning

confidence: 99%

OMPIO: A Modular Software Architecture for MPI I/O

Chaarawi

Gabriel

Keller³

et al. 2011

Recent Advances in the Message Passing Interface

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Abstract. I/O is probably the most limiting factor on high-end machines for large scale parallel applications as of today. This paper introduces OMPIO, a new parallel I/O architecture for Open MPI. OMPIO provides a highly modular approach to parallel I/O by separating I/O functionality into smaller units (frameworks) and an arbitrary number of modules in each framework. Furthermore, each framework has a customized selection criteria that determines which module to use depending on the functionality of the framework as well as external parameters.

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Section: Resultsmentioning

confidence: 99%

OMPIO: A Modular Software Architecture for MPI I/O

Chaarawi

Gabriel

Keller³

et al. 2011

Recent Advances in the Message Passing Interface

Self Cite

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“…When predicting a good configuration is not feasible, auto-tuning may be used to adapt the software to specific application needs. The OPTO framework [18] tests all possible configuration combinations so as to automatically find the best one. Machine learning was also proposed as an alternative method [19].…”

Section: E Shared-buffer Reuse Order and Moesi Protocolmentioning

confidence: 99%

Analysis of MPI Shared-Memory Communication Performance from a Cache Coherence Perspective

Putigny

Ruelle

Goglin

2014

2014 IEEE International Parallel &Amp; Distributed Processing Symposium Workshops

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Shared memory MPI communication is an important part of the overall performance of parallel applications. However understanding the behavior of these data transfers is difficult because of the combined complexity of modern memory architectures with multiple levels of caches and complex cache coherence protocols, of MPI implementations, and of application needs.We analyze shared memory MPI communication from a cache coherence perspective through a new memory model. It captures the memory architecture characteristics with microbenchmarks that exhibit the limitations of the memory accesses involved in the data transfer. We model the performance of intra-node communication without requiring complex analytical models. The advantage of the approach consists in not requiring deep knowledge of rarely documented hardware features such as caching policies or prefetchers that make modeling modern memory subsystems hardly feasible.Our qualitative analysis based on this result leads to a better understanding of shared memory communication performance for scientific computing. We then discuss some possible optimizations such as buffer reuse order, cache flushing, and nontemporal instructions that could be used by MPI implementers.

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“…The Open Tool for Parameter Optimization [8] tunes parameters exposed by the OpenMPI runtime. In OpenMPI, many runtime tasks are delegated to modules, which implement different versions of communication algorithms (such as collectives) and map MPI operations onto lower-level network operations (such as for TCP, InfiniBand, Cray Gemini/Aries, etc.).…”

Section: Related Workmentioning

confidence: 99%

An Autonomic Performance Environment for Exascale

Huck

Porterfield²,

Chaimov

et al. 2015

JSFI

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Exascale systems will require new approaches to performance observation, analysis, and runtime decision-making to optimize for performance and efficiency. The standard "first-person" model, in which multiple operating system processes and threads observe themselves and record first-person performance profiles or traces for offline analysis, is not adequate to observe and capture interactions at shared resources in highly concurrent, dynamic systems. Further, it does not support mechanisms for runtime adaptation. Our approach, called APEX (Autonomic Performance Environment for eXascale), provides mechanisms for sharing information among the layers of the software stack, including hardware, operating and runtime systems, and application code, both new and legacy. The performance measurement components share information across layers, merging first-person data sets with information collected by third-person tools observing shared hardware and software states at node-and global-levels. Critically, APEX provides a policy engine designed to guide runtime adaptation mechanisms to make algorithmic changes, re-allocate resources, or change scheduling rules when appropriate conditions occur.

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A Tool for Optimizing Runtime Parameters of Open MPI

Cited by 31 publications

References 6 publications

OMPIO: A Modular Software Architecture for MPI I/O

OMPIO: A Modular Software Architecture for MPI I/O

Analysis of MPI Shared-Memory Communication Performance from a Cache Coherence Perspective

An Autonomic Performance Environment for Exascale

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