Score-P measurement system code and event logs for Euro-Par 2018 paper: A Methodology for Performance Analysis of Applications Using Multi-layer I/O

Tschueter, Ronny;Herold, Christian;Wesarg, Bert;Weber, Matthias

Description

This artifact contains the prototype implementation of a novel approach for recording I/O operations across multi-layer I/O interfaces and a case study using that approach, as described in the accompanying paper, "A Methodology for Performance Analysis of Applications Using Multi-layer I/O".
In order to fully utilise the high bandwidth offered by modern high-performance computing storage systems, and to handle increasingly large volumes of data, parallel I/O operations are often implemented through libraries such as HDF5, NetCDF and MPI I/O. In many cases, multiple libraries may operate concurrently within a given software stack. This concurrency and operations between libraries can impact upon each other. Given this, it is essential to be able to effectively monitor I/O operations across all libraries utilised by a given application.
Here, a prototype methodology for recording calls to I/O libraries on multiple layers of the software stack is presented. This methodology is implemented as an extension of Open Trace Format Version 2 (OTF2) and the Score-P measurement infrasctructure. The implementation is provided here in the tarball scorep-4.0-io.tar.gz
In addition to the implementation, event logs generated by its test evaluation of the Met Office NERC Cloud model (MONC) simulator are provided. Therein, the I/O behavior of MONC using the Score-P implementation are provided. These logs, provided in the directory monc_112n_14N_10T_small, can be visualized in a tool such as Vampir.
Further instructions on recreating the analyses presented in the accompanying paper are provided in artifact.pdf.

Citations (0)

Mentions (0)

Metrics

Dataset Index

0.7

FAIR Score

85%

Citations

1

Mentions

0

Metrics Over Time

Publication Details

DOI

Publisher

figshare

License

GPL 3.0+

Assigned Domain

Subfield

Information Systems and Management

Field

Decision Sciences

Domain

Social Sciences

Confidence Score

63%

Source

Open Alex

Keywords

Applied Computer Science

Normalization Factors

FT

73.08

CTw

1.00

MTw

1.00