Online Performance Data Introspection with IPM

Aguilar, Xavier; Laure, Erwin; Fürlinger, Karl

doi:10.1109/hpcc.and.euc.2013.107

Cited by 8 publications

(3 citation statements)

References 13 publications

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“…In [11] the authors propose an introspection library for monitoring performance data at the application level. In this aspect, it is more general than the proposed approach.…”

Section: Related Workmentioning

confidence: 99%

An introspection monitoring library to improve MPI communication time

Jeannot

Sartori

2023

J Supercomput

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In this paper we describe how to improve communication time of MPI parallel applications with the use of a library that enables to monitor MPI applications and allows for introspection (the program itself can query the state of the monitoring system). Based on previous work, this library is able to see how collective communications are decomposed into point-to-point messages. It also features monitoring sessions that allow suspending and restarting the monitoring, limiting it to specific portions of the code. Experiments show that the monitoring overhead is very small and that the proposed features allow for dynamic and efficient rank reordering enabling up to 2-time reduction of communication parts of some program.

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“…In [11] the authors propose an introspection library for monitoring performance data at the application level. In this aspect, it is more general than the proposed approach.…”

Section: Related Workmentioning

confidence: 99%

An introspection monitoring library to improve MPI communication time

Jeannot

Sartori

2023

J Supercomput

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“…In [40], the authors state that there are few tools for monitoring the performance of HPC systems at runtime. According to the authors, performance data collected during runtime can be useful to distribute and balance the workload in different ways and for different purposes, for example, to reduce power consumption.…”

Section: Related Workmentioning

confidence: 99%

“…Ferreira et al [34], P2P-MPI [35], Fiala, et al [36], Silent error [36] State-machine replication Gioiosa et al [5], Aguilar et al [40], TAUoverSupermon [42] Monitoring system for performance…”

Section: Related Workmentioning

confidence: 99%

Running resilient MPI applications on a Dynamic Group of Recommended Processes

Camargo

Duarte

2018

J Braz Comput Soc

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High-performance computing systems run applications that can take several hours to execute and have to deal with the occurrence of a potentially large number of faults. Most of the existing fault-tolerant strategies for these systems assume crash faults that are permanent events are easily detected. This is not the case in several real systems, in particular in shared clusters, in which even the load variation may cause performance problems that are virtually equivalent to faults. In this work, we present a new model to deal with this problem in which processes execute tests among themselves in order to determine whether the processors (or cores) on which they are running are recommended or non-recommended. Processes classified as recommended form a Dynamic Group of Recommended Processes (DGRP) that runs the application. The DGRP is formed only by processes that have not been tested as non-recommended by all DGRP processes. A process not in the DGRP that is continuously tested as recommended can rejoin the DGRP after a round of consensus executed by DGRP processes. Experimental results are presented obtained from a MPI-based implementation in which the HyperQuickSort parallel sorting algorithm reconfigures itself at runtime to tolerate up to N − 1 faults (in a system with N processes) while sorting up to 1 billion integers.

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