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mergingTools (version 1.0.1)

Tools to Merge Hardware Event Monitors (HEMs) Coming from Separate Subexperiments into One Single Dataframe

Description

Implementation of two tools to merge Hardware Event Monitors (HEMs) from different subexperiments. Hardware Reading and Merging (HRM), which uses order statistics to merge; and MUlti-Correlation HEM (MUCH) which merges using a multivariate normal distribution. The reference paper for HRM is: S. Vilardell, I. Serra, R. Santalla, E. Mezzetti, J. Abella and F. J. Cazorla, "HRM: Merging Hardware Event Monitors for Improved Timing Analysis of Complex MPSoCs," in IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, vol. 39, no. 11, pp. 3662-3673, Nov. 2020, . For MUCH: S. Vilardell, I. Serra, E. Mezzetti, J. Abella, and F. J. Cazorla. 2021. "MUCH: exploiting pairwise hardware event monitor correlations for improved timing analysis of complex MPSoCs". In Proceedings of the 36th Annual ACM Symposium on Applied Computing (SAC '21). Association for Computing Machinery. . This work has been supported by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No. 772773).

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Version

Install

install.packages('mergingTools')

Monthly Downloads

144

Version

1.0.1

License

GPL-3

Maintainer

Sergi Vilardell

Last Published

September 13th, 2023

Functions in mergingTools (1.0.1)

HRM_merge

HRM merge
get_independent_matrix

Generate independent HEMs
T2080_code2name

T2080 HEM codes to HEM names
code2hem

Code to HEM name
data_hrm_raw_vignette

Data generated for HRM merging
data_much_raw_vignette

Data generated for MUCH merging
MUCH_merge

MUCH Merge
simulate_and_merge

Simulate the MVG and merge the HEMs
generate_mvg_params

Generate multivariate Gaussian distribution parameters
correlation_matrix

Compute correlation matrix
process_raw_experiments

Process raw experimental data