Cluster Mode Overview. If you’d like to send requests to the cluster remotely, it’s better to open an RPC to the driver and have it submit operations from nearby than to run a driver far away from the worker nodes. A jar containing the user's Spark application. In some cases users will want to create an 'uber jar' containing their. Fastcluster: Fast Hierarchical, Agglomerative Clustering Routines for R and Python Daniel Mullner Stanford University Abstract The fastcluster package is a C library for hierarchical, agglomerative clustering. It provides a fast implementation of the most e cient, current algorithms when the input is a dissimilarity index. %%% -.-BibTeX-.-%%% %%% BibTeX-file%%% author = 'Nelson H. Beebe',%%% version = '2.160. %%% -.-BibTeX-.-%%% %%% BibTeX-file%%% author = 'Nelson H. Beebe',%%% version = '2.160. Jul 25, 2018  Linux Cluster User Guide. This is a user guide with some of the basic commands to use the Cluster. Accessing the Cluster. To login to the sirius cluster: ssh -l -p Port-Number user-name@sirius.bc.edu. Where user-name is your BC user name. Enter your password. The fastcluster package implements the seven common hierarchical clustering schemes efficiently. The package is made with two interfaces to standard software: R and Python, which should cover a big part of the scientific community. A full User's Manual is available on CRAN.

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fastcluster: Fast hierarchical clustering routines for R and Python

Copyright:

* Until package version 1.1.23: © 2011 Daniel Müllner <http://danifold.net>

* All changes from version 1.1.24 on: © Google Inc. <http://google.com>

The fastcluster package is a C++ library for hierarchical, agglomerative

clustering. It efficiently implements the seven most widely used clustering

schemes: single, complete, average, weighted/McQuitty, Ward, centroid and

median linkage. The library currently has interfaces to two languages: R and

Python/NumPy. Part of the functionality is designed as drop-in replacement for

existing routines: “linkage” in the SciPy package “scipy.cluster.hierarchy”,

“hclust” in R's “stats” package, and the “flashClust” package. Once the

fastcluster library is loaded at the beginning of the code, every program that

uses hierarchical clustering can benefit immediately and effortlessly from the

performance gain. Moreover, there are memory-saving routines for clustering of

vector data, which go beyond what the existing packages provide.

See the author's home page <http://danifold.net> for more

information, in particular a performance comparison with other clustering

packages. The User's manual is the file docs/fastcluster.pdf in the

source distribution.

The fastcluster package is distributed under the BSD license. See the file

LICENSE in the source distribution or

<http://opensource.org/licenses/BSD-2-Clause>.

The distribution on pypi.python.org contains only the files which are necessary

for the Python interface. The full source distribution with both interfaces

is available on CRAN

https://CRAN.R-project.org/package=fastcluster

The Python package can be installed either from PyPI (conveniently with pip)

or manually from the source package at CRAN. Both distributions compile and

install identical libraries.

Christoph Dalitz wrote a pure C++ interface to fastcluster:

<http://informatik.hsnr.de/~dalitz/data/hclust>.

Installation

‾‾‾‾‾‾‾‾‾‾‾‾

See the file INSTALL.txt in the source distribution, which also explains how to

install the fastcluster package for R.

Usage

‾‾‾‾‾

1. R

‾‾‾‾

In R, load the package with the following command:

library('fastcluster')

The package overwrites the function hclust from the “stats” package (in the

same way as the flashClust package does). Please remove any references to the

flashClust package in your R files to not accidentally overwrite the hclust

function with the flashClust version.

The new hclust function has exactly the same calling conventions as the old

one. You may just load the package and immediately and effortlessly enjoy the

performance improvements. The function is also an improvement to the flashClust

function from the “flashClust” package. Just replace every call to flashClust

by hclust and expect your code to work as before, only faster. (If you are

using flashClust prior to version 1.01, update it! See the change log for

flashClust:

http://cran.r-project.org/web/packages/flashClust/ChangeLog )

If you need to access the old function or make sure that the right function is

called, specify the package as follows:

fastcluster::hclust(…)

flashClust::hclust(…)

stats::hclust(…)

Vector data can be clustered with a memory-saving algorithm with the command

hclust.vector(…)

See the User's manual docs/fastcluster.pdf for further details.

WARNING

‾‾‾‾‾‾‾

R and Matlab/SciPy use different conventions for the “Ward”, “centroid” and

“median” methods. R assumes that the dissimilarity matrix consists of squared

Euclidean distances, while Matlab and SciPy expect non-squared Euclidean

distances. The fastcluster package respects these conventions and uses

different formulas in the two interfaces.

If you want the same results in both interfaces, then feed the hclust function

in R with the entry-wise square of the distance matrix, D^2, for the “Ward”,

“centroid” and “median” methods and later take the square root of the height

field in the dendrogram. For the “average” and “weighted” alias “mcquitty”

methods, you must still take the same distance matrix D as in the Python

interface for the same results. The “single” and “complete” methods only depend

on the relative order of the distances, hence it does not make a difference

whether the method operates on the distances or the squared distances.

The code example in the R documentation (enter ?hclust or example(hclust) in R)

contains an instance where the squared distance matrix is generated from

Euclidean data.

2. Python

‾‾‾‾‾‾‾‾‾

The fastcluster package is imported as usual by

import fastcluster

It provides the following functions:

linkage(X, method='single', metric='euclidean', preserve_input=True)

single(X)

complete(X)

average(X)

weighted(X)

ward(X)

centroid(X)

median(X)

linkage_vector(X, method='single', metric='euclidean', extraarg=None)

The argument X is either a compressed distance matrix or a collection of n

observation vectors in d dimensions as an (n×d) array. Apart from the argument

preserve_input, the methods have the same input and output as the functions of

the same name in the package scipy.cluster.hierarchy.

The additional, optional argument preserve_input specifies whether the

fastcluster package first copies the distance matrix or writes into the

existing array. If the dissimilarities are generated for the clustering step

only and are not needed afterward, approximately half the memory can be saved

by specifying preserve_input=False. Note that the input array X contains

unspecified values after this procedure. You may want to write

linkage(X, method='…', preserve_input=False)

del X

to make sure that the matrix X is not accessed accidentally after it has been

used as scratch memory.

The method

linkage_vector(X, method='single', metric='euclidean', extraarg=None)

provides memory-saving clustering for vector data. It also accepts a collection

of n observation vectors in d dimensions as an (n×d) array as the first parameter.

The parameter 'method' is either 'single', 'ward', 'centroid' or 'median'. The

'ward', 'centroid' and 'median' methods require the Euclidean metric. In case

of single linkage, the 'metric' parameter can be chosen from all metrics which

are implemented in scipy.spatial.dist.pdist. There may be differences between

linkage(scipy.spatial.dist.pdist(X, metric='…'))

and

linkage_vector(X, metric='…')

since there have been made a few corrections compared to the pdist function.

Please consult the the User's manual docs/fastcluster.pdf for

comprehensive details.

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Linux Cluster User Guide

This is a user guide with some of the basic commands to use the Cluster.

Accessing the Cluster

To login to the sirius cluster:

ssh -l -p Port-Number user-name@sirius.bc.edu

where user-name is your BC user name. Enter your password.

From on campus, you do not need to specify the port number (i.e. you do not need the '-p Port-number' option). You must enter a port number from off-campus. Contact researchiveservices@bc.edu for the port number.

If you are using an X11 client,

ssy -Y -p Port-Number user-name@sirius.bc.edu

will allow you to run graphical applications on sirius from your workstation. The pleiades cluster is similar

You can also connect using NoMachine, which gives you a desktop on the cluster. Ask Research Services for instructions

Changing your password

Please change your intitial password after you login. To change your password, type:

passwd

then follow instructions.

User Environment

We use 'Environment Modules' to keep the environment clean. For application software, there will be a module to load before you can use the software. The module will set all paths and environment variables necessary to use the software. Environment Modules are simple to use. For example, to use the software called matlab, first load the matlab module by typing:

module load matlab Sony rx100 manual.

If you use a module frequently, you can add the module to the existing 'module load' command in your .tcshrc or your .bash_profile, depending on which shell you are using.The default shell is the tcsh.

There are some basic commands:
module avail - lists all currently available modules
module list - lists all modules currently loaded.
module load modulefile - add/load one or more modulefile
module unload modulefile - unload modulefile.
module switch mod1 mod2 - replace mod1 with mod2

There should be no need for you to set a path yourself to run application software that is available to all users of the system.

Compilers

Mullner D The Fastcluster Package User's Manual 2017 Download

The gnu, Intel and other compilers are installed on scorpio. For Intel , the C, C++, and FORTRAN 77/90/95 compilers are icc, icc, and ifort, respectively. To compile and link a C program, for example, you may type from your shell prompt:

gcc -o hello hello.c

The intell module needs to be loaded to use the pathscale compilers (module load pathscale)

File Systems

Each account has a home directory. Home directories are backed up nightly. If a file in your home directory exists, there will be a copy on the backup system. For files that exist and change, we save the current file, and, for 15 days, the previous version. We can restore either the current file or, if the requested is made within 15 days of the last change to the file, the previous version of the file. If a file is deleted, then we can recover the file for up to 30 days from the day it was deleted.

Home directories will have a quota. The default quota will be 10 TB. If you need more space, please keep in mind there is a file system called /scratch that can be used for temporary files. You may also request a larger quota by sending email to researchservices@bc.edu.

For temporary files, please create a directory for your work in /scratch and put your temporary files there. Files in /scratch are not backed up.

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Running a program (Queues)

Other than short test jobs on scorpio.bc.edu, all jobs must be submitted to the queuing system. For information on the queue structure, see the Cluster Queue Web page. We are using PBS(Torque), along with the Moab scheduler to dispatch jobs to the compute nodes. For more information and instructions see the Torque User Guide. The most common PBS and Moab commands are as follows:
qsub submit jobs
qdel delete a job(s) job from the queue.
showq show the jobs waiting to run, and the running
showstart displays an estimated start time of a job waiting to run

Parameters such as memory, the number of cores and wallclock time requested are specified in a command file. Here is an example of a command file.

#!/bin/tcsh
#PBS -l mem=500mb,nodes=1:ppn=1,walltime=1:00:00
#PBS -m abe -M your-email-address

cd work-directory
./a.out

This will request 500 MB of memory and one core for 1 hour

To submit the job via the script file sample.pbs, you may type

Mullner D The Fastcluster Package User's Manual 2017 Review

qsub sample.pbs

Specifing the maximum wall clock time (walltime=hh:mm:ss) helps schedule your job promptly. Wall clock time is the elapsed time from when your job starts running to the time it completes. We have one queue, the scheduler will determine where to run your job so that it gets started as soon as possible. We have reserved some nodes for short jobs. By having one queue and letting the scheduler determine where to run the job means that you won't submit your job to the wrong queue (meaning a queue that is full, when there are available processors in another). Likewise, we have nodes with different amounts of memory and the scheduler will guarantee that you get the memory you requested for yourself alone.

Unfortunately, both the memory and wall-clock time parameters require you to over estimate the amount. If you under estimate, your job may be killed. Use this to get better estimates on future job submissions. For assistance, contact researchservices (researchservices@bc.edu).

To view all jobs in the system, type:

Mullner D The Fastcluster Package User's Manual 2017 Specs

showq

You may want to kill your job with job id 901, you may type:

qdel 901

To view the estimated start time of job id 901, type:

showstart 901

This is only an estimate of the start time, and the start time may change as other jobs are submitted.

Optimization

The following options may generate more faster code:
-O3 -OPT:Ofast

OpenMP

Mullner D The Fastcluster Package User's Manual 2017 Pdf

In order to use OpenMP, your program must be compiled and linkied with
-mp option

Mullner D The Fastcluster Package User's Manual 2017 Model

Assistance

Mullner D The Fastcluster Package User's Manual 2017 Edition

For assistance please contact Research Services at: researchservices@bc.edu