ENHANCING PERFORMANCE OF AN HPC CLUSTER BY ADOPTING NONDEDICATED NODES

David C. Wyld et al. (Eds) : WiMo, ITCSE, ICAIT, NC - 2015
pp. 49–59, 2015. © CS & IT-CSCP 2015 DOI : 10.5121/csit.2015.51005
ENHANCING PERFORMANCE OF AN HPC
CLUSTER BY ADOPTING NON-
DEDICATED NODES
Pil Seong Park
Department of Computer Science, University of Suwon, Hwasung, Korea
pspark@suwon.ac.kr
ABSTRACT
Persona-sized HPC clusters are widely used in many small labs, because they are cost-effective
and easy to build. Instead of adding costly new nodes to old clusters, we may try to make use of
some servers’ idle times by including them working independently on the same LAN, especially
during the night. However such extension across a firewall raises not only some security
problem with NFS but also a load balancing problem caused by heterogeneity. In this paper, we
propose a method to solve such problems using only old techniques applicable to old systems as
is, without requiring any upgrade for hardware or software. Some experimental results dealing
with heterogeneity and load balancing are presented using a two-queue overflow queuing
network problem.
KEYWORDS
HPC clusters, Security, NFS, SSH tunnelling, load balancing
1. INTRODUCTION
The desire to get more computing power and higher reliability by orchestrating a number of low
cost commercial off-the-shelf computers has given rise to a variety of architectures and
configurations. A computer cluster is composed of a set of loosely or tightly coupled computers
that are usually connected to each other through a fast LAN and work together so that they can be
viewed as a single system in many respects.
Computer clusters may be configured for different purposes. Load-balancing (LB) clusters are
configurations in which nodes share computational workload like a web server cluster. High-
performance computing (HPC) clusters are used for computation-intensive purposes, rather than
handling IO-oriented operations. High-availability (HA) clusters improve the availability of the
cluster, by having redundant nodes, which are then used to provide service when system
components fail.
Many kinds of commercial clusters are on the market. However the technologies to build similar
systems using off-the-shelf components are widely known (e.g., see [1]), and it is easy to build a

50 Computer Science & Information Technology (CS & IT)
low-cost personal-sized cluster [2]. Many small labs first build their own personal-sized cluster,
and gradually grow it by adding some more dedicated nodes later. Instead, if there are some other
nodes that are being used for other purposes on the same LAN, we may try to utilize their idle
times as long as they are not always busy enough, especially during the night. The Berkeley
NOW (Network of Workstations) project is one of early attempts to make use of the power of
clustered machines on a building-wide scale [3]. However such extension gives rise to difficulties
in security, administering the cluster, and load forecasting for optimal performance.
In this paper, we deal with some technical issues in extending a personal-sized Linux cluster
across a LAN. We do not use state-of-the-art technologies, since the sole purpose is to achieve
our purpose using our old HPC clusters as is, with no hardware or software upgrade. Some results
of the experiments to evaluate the system are given at the end.
2. BACKGROUND
2.1. HPC Cluster Middlewares
An HPC cluster normally consists of the dedicated nodes that reside on a secure isolated private
network behind a firewall. Users normally access the master/login node (master, for short) only,
which sits in front of compute nodes (slave nodes, or slaves for short).
The activities of all compute nodes are orchestrated by a cluster middleware that allows treating
the whole cluster system via a single system image concept. Well-known HPC middlewares
based on message passing are the Message Passing Interface (MPI) [4] and the Parallel Virtual
Machine (PVM) [5], the former being the de facto standard. According to the standard, many
commercial or non-commercial implementation libraries have been developed. LAM/MPI, FT-
MPI, and LA-MPI are some of widely used non-commercial libraries, and their technologies and
resources have been combined into the on-going Open MPI project [6].
All the nodes in an HPC cluster share not only executable codes but also data via NFS (Network
File System), which is not encrypted in general. It is perfectly all right as long as the whole
cluster nodes are on a secure local area network behind a firewall.
To extend the cluster to include other nodes outside of the firewall, we will be confronted with
some problems with security and data sharing among nodes. Moreover, such growth results in a
heterogeneous cluster with nodes of different power, possibly running different Linux versions.
2.2. File Sharing Protocols
NFS, created by Sun Microsystems in 1984, is a protocol to allow file sharing between UNIX
systems residing on a LAN. Linux NFS clients support three versions of the NFS protocol:
NFSv2 (1989), NFSv3 (1995), and NFSv4 (2000). However the NFS protocol as is has many
problems to use in extending the cluster, since its packets are not encrypted and due to other
shortcomings to be discussed later.
Other alternatives to NFS include AFS (Andrew File System), DFS (Distributed File System),
RFS (Remote File System), Netware, etc. [7]. There are also various clustered file systems shared

Computer Science & Information Technology (CS & IT) 51
by multiple servers [8]. However we do not adopt such new technologies since they are not
supported by our old cluster.
2.3. SSH Tunnelling
Encryption of NFS traffic is necessary for secure extension across a firewall. One of the common
techniques that are ordinarily used is known as cryptographically protected tunnelling. An IP-
level or TCP-level stream of packets is used to tunnel application-layer segments [9]. A TCP
tunnel is a technology that aggregates and transfers packets between two hosts as a single TCP
connection. By using a TCP tunnel, several protocols can be transparently transmitted through a
firewall. Under certain conditions, it is known that the use of a TCP tunnel severely degrades the
end-to-end TCP performance, which is called TCP meltdown problem [10].
The SSH protocol allows any client and server programs to communicate securely over an
insecure network. Furthermore, it allows tunnelling (port forwarding) of any TCP connection on
top of SSH, so as to cryptographically protect any application that uses clear-text protocols.
3. EXTENSION OF AN HPC CLUSTER
We would like to extend our old cluster to include some other nodes across the firewall, as shown
in Figure 1. In general, the master node has attached storage that is accessible by diskless slave
nodes using insecure NFS. Since NFS relies on the inherently insecure unencrypted UDP
protocol (up to NFSv3), IP spoofing is possible. Moreover, firewall configuration is difficult
because of the way NFS daemons work.
Figure 1. Cluster extension to include the non-dedicated node EXT across a firewall.
3.1. Fixing NFS Ports for SSH Tunnelling
SSH tunnelling, which makes use of SSH port forwarding, is widely used to encrypt some
unencrypted packets or to bypass firewalls, e.g., see [9,11]. SSH tunnels support only TCP
protocols of fixed ports, but NFS uses UDP protocols by default, and the ports of some daemons
essential for the operation of NFS are variable.
Fortunately NFS over TCP protocols are also supported from the Linux kernel 2.4 and later on
the NFS client side, and from the kernel 2.4.19 on the server side [12]. Since all the nodes of our
cluster satisfy this condition, we can make NFS work over TCP by specifying the option "-o tcp"
in the mounting command. The following is an example of mounting server’s /nfs_dir directory
on the client’s mount_pt.

# mount -t nfs -o tcp server:/nfs_dir mount_pt
The daemons essential for NFS operation are 2 kinds. Portmapper (port 111) and rpc.nfsd (port
2049) use fixed ports, but rpc.statd, rpc.lockd, rpc.mountd, and rpc.rquotad use ports that are
randomly assigned by the operating system. However the ports of the latter can be fixed by
specifying port numbers in appropriate configuration files [13].
The ports of the first three can be fixed by adding the following lines in the NFS configuration
file /etc/sysconfig/nfs, for example,
STATD_PORT=4001
LOCKD_TCPPORT=4002
LOCKD_UDPPORT=4002
MOUNTD_PORT=4003
The rest ports can be fixed by defining new port numbers in /etc/services, for example
rquotad 4004/tcp
rquotad 4004/u에
3.2. Setting up an SSH Tunnel
For tunnelling of NFS, it is necessary for the server to mount its own NFS directory to be
exported to clients. Hence on the server side, the configuration file /etc/exports has to be
modified, for example, for exporting /nfs_dir,
/nfs_dir localhost (sync,rw,insecure,root_squash)
where “insecure” means it allows connection from ports higher than 1023, and “root_squash”
means it squashes the root permissions for the client and denies root access to access/create files
on the NFS server for security.
On the client side, to forward the ports 10000 and 20000, for example, to the fixed ports 2049
(rpc.nfsd) and 4003 (rpc.mountd), respectively, we can use the command
# ssh nfssvr -L 10000:localhost:2049 -L 20000:localhost:4003 -f sleep 600m
where "nfssvr" is the IP or the name of the NFS server registered in /etc/hosts, and "-f sleep
600m" means that port forwarding is to last for 600 minutes in the background.
Once connected to the NFS server, an SSH tunnel will be open if the correct password is entered.
Then the NFS server’s export directory can be mounted on the client’s side. The following
command is an example to mount the /nfs_dir directory of the NFS server on the /client_dir
directory of the client.
# mount -t nfs -o tcp,hard,intr,port=10000, mountport=20000 localhost:/client_dir

4. DEALING WITH HETEROGENEITY
Data partitioning and load balancing are important components in parallel computation. Since
earlier works (e.g., see [14]), many authors have studied load balancing using different strategies
on dedicated/non-dedicated heterogeneous systems [15,16,17,18], but it is nearly impossible to
find works on the security problems arising in cluster expansion, which is more technical rather
than academic.
Even though the original tightly-coupled HPC cluster may be homogeneous, the extended cluster
inevitably will behave like a heterogeneous system. First, the power of the newly added node
EXT may be different and its workload may change continually since it is not a dedicate node. In
addition, the communication speed between the node EXT and the original cluster is slower than
that among the original cluster nodes. Hence we need to use a dynamic run-time load balancing
strategy [19], while assigning equal amount of work to the nodes of the original cluster.
4.1. The Sample Problem
Strategies of dynamic load balancing depend on problems, and we need introduce the problem we
consider first. We deal with the old famous two-queue overflow queuing problem given by the
Kolmogorov balance equation
( ) ( )
( ) ( )
1 2 2
1
1 , 1 , 1 2 , 1 1 1 2 2 ,
1 ,0 1, 1 , 1 1 1,
[ 1 1 m in ( , ) m in ( , )]
= 1 1 m in ( 1, ) , , 1, 2,
i n j n j n i j
i i j i n i j
i s j s p
p i s p i j
λ δ δ λ δ µ µ
λ δ µ δ
− − −
− − +
− + − + +
− + − + =
where pi,j is the steady-state probability distribution giving the probability that there are ij
customers in the j-th queue. It is assumed that, in the i-th queue, there are si parallel servers and
ni-si-1 waiting spaces, and customers enter the i-th queue with mean arrival rate λi, and depart at
the mean rate µi. The model allows overflow from the first queue to the second queue if the first
queue is full. The total number of possible states is n1×n2.
It is known that no analytic solution exists, and the balance equation has to be solved explicitly.
We can write the discrete equation as a singular linear system, say 0A =x where x is the vector
consisting of all states pi,j’s. Even for systems with relatively small numbers of queues, waiting
spaces, and servers per queue, the size of the resulting matrix is huge. The numbers of waiting
spaces in our problem are 200 and 100 in each queue, respectively, and the resulting matrix size
is 20,000ⅹ20,000.
4.2. Job Assignment by Domain Decomposition
The graph of the resulting matrix of a k-queue problem is the same as the graph of the matrix
corresponding to the k-dimensional Laplacian operator. Thus the graph of the matrix A is a two-
dimensional rectangular grid.
As an example, Figure 1.a) shows the grid of a two-queue overflow model with 9ⅹ5 = 45 states,
where each dot corresponds to some state pi,j, i=1,2,…9 and j=1,2….5. If we use 3 computation
nodes, we may assign 15 grid points to each node. However to compute the values at boundary

points (in vertical dotted boxes), each node needs the values at the boundary points computed by
adjacent nodes. As a result, node 0 and node 2 should have enough memory to store 20 grid
points (or 4 vertical grid lines) and node 2 needs to store 25 grid points (or 5 vertical grid lines),
even though each node updates only 15 grid points (or 3 vertical grid lines). The extra grid points
are to store the values to be received from adjacent nodes.
Figure 1.b) shows partitioning of the corresponding matrix A of size 45ⅹ45, each submatrix Ai
being of size 5ⅹ45. For better load balancing, workload will be assigned in units of one vertical
line (that corresponds to the computation with one submatrix Ai). Since we deal with a huge
matrix problem of size 20,000ⅹ20,000, one unit workload is computation with 100ⅹ20,000
submatrix.
a) b)
Figure 2. Examples showing a) job assignment to 3 nodes, and b) matrix partitioning
for a 9ⅹ5 = 45 state two-queue overflow queuing model.
To solve the singular system 0A =x , we use a splitting method A=D-C where D is the diagonal
matrix that consists of all diagonal entries of A, and C=D-A. Then we can rewrite D C=x x
which leads to a convergent eigenvalue problem 1
D C−
=x x . For convergence test, we use the
size of the residual defined by
1
2 2 2|| r || || ( ) || where || || 1.I D C−
= − =x x
4.3. Communication for Asynchronous Iteration
In normal synchronous parallel computation, each node updates the values of the grid points
assigned to it in lockstep fashion, so that it can give the same solution as that of serial
computation. During computation, each node should communicate with its adjacent nodes to
exchange boundary point values, and all nodes should synchronize computation.
The workload of the added node EXT may change dynamically all the time, and it is
unpredictable and uncontrollable. If the node EXT is busy doing some other jobs, it cannot
communicate with its adjacent nodes in time, degrading overall performance by making all other
nodes wait.

Asynchronous algorithms appear naturally in parallel systems and are heavily exploited
applications.Allowing nodes to operate in an asynchronous manner simplifies the implementation
of parallel algorithms and eliminates the overhead associated with synchronization. Since 1990’s,
some theories on asynchronous iteration has been developed, e.g., see [20]. Fortunately, the
matrix A in our problem satisfies the necessary condition for a matrix to satisfy for convergence.
Hence we adopt asynchronous iteration freely, but we need some more work for implementation
details.
We assume the master node is always alive and is not busy so that it can immediately handle
communication with other nodes. Data exchange between two adjacent nodes should be done
through the master node, and the master node should keep the most current values of all grid
points. But all communication should be initiated by compute nodes (including the added non-
dedicate nodes), and the master node should just respond according to compute nodes’ request.
Figure 3 is the master’s algorithm using MPI functions to handle other nodes’ request. The
master continually checks if any message has arrived from any other nodes by calling the
MPI_Iprobe( ) function. If there is any message arrived, it then identifies the sender, tag, and
sometimes the number of data received. Based on the tag received, the master takes some
appropriate action. For example, if the received tag is 1000, it means “Receive the boundary data
I send, and send the boundary data I need.”, etc. Then any compute node can continue its work
without delay.
Figure 3. A master’s algorithm to handle compute nodes’ request.
We also adopt a dynamic load balancing strategy by measuring the time interval between two
consecutive data requests by the same node. It is necessary only for non-dedicated nodes, since
the others are dedicated and have the same computational power. The workload of the non-
dedicated node is reduced if
N t
n
T
α<
where N is the average workload (in units of the number of vertical grid lines), T is the average
time interval between two consecutive data requests by the same dedicated node, t is the time
interval between two requests of EXT, and α is some tolerance parameter less than 1 (we used
0.8). Similarly, the workload of the non-dedicate node is increased if
N t
n
T
β>

for some β (we used 1.2).
In adjusting workloads, we used a simpler strategy. That is, when one of these conditions is
satisfied, we just decrease/increase two boundary grid lines of EXT and assigned them to/from
adjacent nodes. For this, to each non-dedicated node, we assigned the grid region which is
between the two regions assigned to dedicated nodes. It takes very little time to re-assign grid
lines to some other node, since our matrix is very sparse (just 5 nonzero entries in each row) and
can be generated by each node instantly whenever necessary.
5. PERFORMANCE TESTS
Our old cluster we want to extend has 2 nodes each with dual 1GHz Pentium 3 Xeon processors
running Fedora Core 4, with LAM/MPI v.7.1.2 installed. The nodes are interconnected via a
Gigabit LAN, and NFS is used for file sharing. The non-dedicated node EXT which sits on the
outside fast Ethernet has a 2.4 GHz Intel® Core™ i5 CPU with 8 GB memory, and runs on
Fedora 13.
The performance of the NFS through an SSH tunnel will inevitably drop due to encryption
overhead. Tests were performed between the master node and the node EXT, using UDP or TCP,
and with or without an SSH tunnel across the firewall. The times it took to read or write a file of
size 1GB from EXT were measured, at varying NFS block sizes. Since
NFSSVC_MAXBLKSIZE (maximum block size) of the NFS in Fedora Core 4 is 32*1024 (see
/usr/src/kernels/2.6.11-1.1369_FC4-i686/include/linux/nfsd/const.h), tests were performed at
block sizes of 4k, 8k, 16k, and 32k, respectively, 3 times for each and they are averaged. In
addition, to delete any remaining data in cache, NFS file system was manually unmounted and
mounted again between tests.
The following are example commands that first measure the time taken to create the file
/home/testfile of size 1GB on the NFS server and read it using block size 16KB.
# time dd if=/dev/zero of=/home/testfile bs=16k count=65536
# time dd if=/home/testfile of=/dev/null bs=16k
The results of the NFS performance test, with or without SSH tunnelling are given in Table 1. For
the common NFS without tunnelling, the figures in parentheses are the times it took when TCP is
used, and others are when UDP is used.
As we see, the larger the NFS block size, the faster in all cases. For the common NFS without
SSH tunnelling, write operation using UDP is slightly faster than TCP, but it is not the case for
read operation. Moreover the NFS with SSH tunnelling takes 3.9%-10.1% more time for write
and 1.4-4.3% more for read, than the common NFS using UDP.
As long as the NFS block size is taken as large as possible, the tunnelling overhead may not be
large even though NFS service is done through SSH tunnelling, since the non-dedicated nodes
outside of the firewall need not read or write so often through NFS, which is common in high
performance parallel computing.

Table 1. Performance of NFS, with or without SSH tunnelling (sec)
Block
size
Common NFS SSH tunnelled
Read Write Read Write
4K
96.12
(95.31)
119.37
(120.95)
100.29 131.45
8K
95.07
(94.43)
115.74
(120.31)
98.12 122.13
16K
93.85
(92.21)
114.70
(118.32)
95.31 121.72
32K
92.51
(91.45)
112.35
(115.87)
93.89 116.83
Figure 4 shows the comparison results. The solid line marked with 1 is the synchronous
computation result using only the dedicated nodes of our old cluster which has 4 cores in total.
The dotted line marked with 2 is the result when we add the node EXT (we created just 1 process
on EXT). The residual and elapsed time were checked every time the first slave reports the
intermediate result. The running times to converge to the result with residual size less than 10-5
were 956.75 sec for curve 1 and 854.14 sec for curve 2, respectively. Hence the speedup by the
added EXT node is 1.12.
Figure 4. Comparison results.
The result is somewhat disappointing, since the speed of the CPU core on EXT is at least twice as
fast as that of the old cluster. Probably it is mainly due to much slower network speed between
the cluster and the node EXT and the extra workload for some other jobs.
6. CONCLUSIONS
Personal-sized HPC clusters are widely used in many small labs, because they are easy to build,
cost-effective, and easy to grow. Instead of adding costly new nodes, we can extend clusters to
include some other servers on the same LAN, so that we can make use of their idle times.

However, unlike a tightly-coupled HPC cluster behind a firewall, the resulting system suffers a
security problem with NFS which is vital for HPC clusters.
Of course there are many new good solutions using recent technologies. However we do adopt
such strategy, because they require upgrade of hardwares and/or softwares including an operating
system. Instead we devise a solution using SSH tunnelling, which can be applied to the old
system as is. Probably this approach may be helpful to many of small home-made cluster
systems. We also devised a dynamic load balancing method which is based on domain
decomposition and asynchronous iteration, using a two-queue overflow queuing network problem
of matrix size 20,000ⅹ20,000. The speedup obtained by using one extra process on a much
faster non-dedicated node is somewhat disappointing, mainly because of the slow fast Ethernet
speed between the cluster and the extra node. We expect much higher speedup if the outer
network is upgraded.
ACKNOWLEDGEMENTS
This work was supported by the GRRC program of Gyeonggi province [GRRC SUWON2014-
B1, Cooperative Recognition and Response System based on Tension Sensing].
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AUTHOR
Prof. Pil Seong Park received his B.S. degree in Physical Oceanography from Seoul
National University, Korea in 1977, and M.S. degree in Applied Mathematics from Old
Dominion University, U.S.A. in 1984. He received his Ph.D. degree in Interdisciplinary
Applied Mathematics (with emphasis on computer science) from University of Maryland
at College Park, U.S.A in 1991. He worked as the head of Computer Center at Korea
Ocean Research & Development Institute from 1991 to 1995. Currently, he is a professor
of the Department of Computer Science, University of Suwon in Korea. His research interest includes high
performance computing, Linux clusters, digital image processing, and knowledge-based information
systems. He is a member of several academic societies in Korea.

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