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hpcwld  

High Performance Cluster Models Based on Kiefer-Wolfowitz Recursion
View on CRAN: Click here


Download and install hpcwld package within the R console
Install from CRAN:
install.packages("hpcwld")

Install from Github:
library("remotes")
install_github("cran/hpcwld")

Install by package version:
library("remotes")
install_version("hpcwld", "0.6-5")



Attach the package and use:
library("hpcwld")
Maintained by
Alexander Rumyantsev
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2014-07-16
Latest Update: 2022-06-26
Description:
Probabilistic models describing the behavior of workload and queue on a High Performance Cluster and computing GRID under FIFO service discipline basing on modified Kiefer-Wolfowitz recursion. Also sample data for inter-arrival times, service times, number of cores per task and waiting times of HPC of Karelian Research Centre are included, measurements took place from 06/03/2009 to 02/30/2011. Functions provided to import/export workload traces in Standard Workload Format (swf). Stability condition of the model may be verified either exactly, or approximately. Stability analysis: see Rumyantsev and Morozov (2017) , workload recursion: see Rumyantsev (2014) .
How to cite:
Alexander Rumyantsev (2014). hpcwld: High Performance Cluster Models Based on Kiefer-Wolfowitz Recursion. R package version 0.6-5, https://cran.r-project.org/web/packages/hpcwld. Accessed 05 Aug. 2026.
Previous versions and publish date:
(2026-07-09 07:47), 0.4 (2014-07-16 09:52), 0.5 (2015-02-17 19:32), 0.6-4 (2022-04-22 20:50)
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Complete documentation for hpcwld
Functions, R codes and Examples using the hpcwld R package
Some associated functions: ApproxC . DMC . DataToSWF . FromSWF . HPC_KRC . HPC_KRC2 . MaxThroughput2 . ToSWF . Wld . X . hpcwld-package . 
Some associated R codes: DMC.R . SWF.R . Stability.R . Workload.R .  Full hpcwld package functions and examples
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