Wld: Workload of a High Performance Cluster model

Description Usage Arguments Value Author(s) References Examples

Description

This function computes the Kiefer-Wolfowitz modified vector for a HPC model. This vector contains the work left on each of 'm' servers of a cluster for the time of the arival of a task. Two methods are available, one for the case of concurrent server release (all the servers end a single task simultaneously), other for independent release (service times on each server are independent).

Usage

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Wld(T, S, N, m, method = "concurrent")

Arguments

T

Interarrival times of tasks

S

Service times of tasks (a vector of length n, or a matrix nrows=n, ncols='m').

N

Number of cores each task needs

m

Number of cores/servers for a HPC

method

Independent or concurrent

Value

A dataset is returned, containing 'delay' as a vector of delays exhibited by each task, 'total_cores' as the total busy CPUs in time of arrival of each task, and 'workload' as total work left at each CPU.

Author(s)

Alexander Rumyantsev (Institute of Applied Mathematical Research, Karelian Research Centre, RAS)

References

E.V. Morozov, A.Rumyantsev. Stability analysis of a multiprocessor model describing a high performance cluster. XXIX International Seminar on Stability Problems for Stochastic Models and V International Workshop "Applied Problems in Theory of Probabilities and Mathematical Statistics related to modeling of information systems". Book of Abstracts. 2011. Pp. 82–83.

Examples

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Wld(T=rexp(1000,1), S=rexp(1000,1), round(runif(1000,1,10)), 10)
# returns the workload, delay and total cpus used 
# for a cluster with 10 CPUs and random exponential times

hpcwld documentation built on May 1, 2019, 9:07 p.m.