EXPLOITING RASPBERRY PI CLUSTERS AND CAMPUS LAB COMPUTERS FOR DISTRIBUTED COMPUTING

International Journal of Computer Science & Information Technology (IJCSIT) Vol 14, No 3, June 2022
DOI: 10.5121/ijcsit.2022.14304 41
EXPLOITING RASPBERRY PI CLUSTERS
AND CAMPUS LAB COMPUTERS FOR
DISTRIBUTED COMPUTING
Jacob Bushur and Chao Chen
Department of Electrical and Computer Engineering,
Purdue University Fort Wayne Fort Wayne, Indiana, USA
ABSTRACT
Distributed computing networks harness the power of existing computing resources and grant access to
significant computing power while averting the costs of a supercomputer. This work aims to configure
distributed computing networks using different computer devices and explore the benefits of the computing
power of such networks. First, an HTCondor pool consisting of sixteen Raspberry Pi single-board
computers and one laptop is created. The second distributed computing network is set up with Windows
computers in university campus labs. With the HTCondor setup, researchers inside the university can
utilize the lab computers as computing resources. In addition, the HTCondor pool is configured alongside
the BOINC installation on both computer clusters, allowing them to contribute to high-throughput
scientific computing projects in the research community when the computers would otherwise sit idle. The
scalability of these two distributed computing networks is investigated through a matrix multiplication
program and the performance of the HTCondor pool is also quantified using its built-in benchmark tool.
With such a setup, the limits of the distributed computing network architecture in computationally intensive
problems are explored.
KEYWORDS
Distributed Computing, Single-Board Computers, Raspberry Pi.
1. INTRODUCTION
Biology, chemistry, computer science, engineering, meteorology, and additional fields of study
share the need to process large amounts of data. From protein folding simulations to sub-atomic
chemical reaction simulations to analyzing large data sets, many fields of research require an
increasing amount of computational power. There are currently two ways to meet this rising
demand - supercomputers and distributed computing networks. Supercomputers are essentially
large computers composed of many processors, typically housed compactly within an array of
server cabinets. The compact arrangement of processors introduces challenges such as hardware,
cooling, space, and electricity costs. These costs can be illustrated using an existing
supercomputer, Summit, at the Oak Ridge National Laboratory. The Summit supercomputer is
composed of 4,608 compute servers, each with two IBM POWER9 22-core processors and six
NVIDIA Tesla V100 graphics processing unit (GPU) accelerators [1,2]. Albeit being a 200-
pertaflop machine, representing the capability to perform 200 quadrillion floating-point
operations per second, Summit comes with a total cost of $325 million, covering its processors,
memory, networking equipment, and other necessary hardware. The supercomputer also requires
approximately 13 MW of power to operate, including both the power consumption of the
hardware and the power usage of the cooling solution. Additionally, the supercomputer occupies

42
5,600 square feet. Therefore, in order to build and operate a supercomputer, an institution must
invest in the hardware, find a space large enough to house the supercomputer, and pay the
recurring electricity costs. Thus, operating a supercomputer is cost-prohibitive for most
institutions. As an alternative, researchers may rent time on supercomputers, but the cost can also
be excessive. For example, one-month usage of Hewlett Packard Enterprise’s smallest GreenLake
cloud supercomputer platform costs $35,883 [3].
Distributed computing networks offer a potentially cheaper alternative without sacrificing
computing power. Distributed computing is based on the premise that many smaller computers
can be networked together, forming a virtual supercomputer. To run a program on a distributed
computing network, the network manager must first split the program into a collection of jobs.
Each job is sent to a computer connected to the network. The results are then returned to the
central manager and presented to the user. These networks are typically configured using
specially developed middleware such as BOINC [4], Folding@Home [5], or HTCondor [6], and
are much cheaper to maintain because of their distributed architecture. Since the middleware
takes care of connecting and managing computers over a network, operators almost entirely
circumvent the hardware, electricity, space, and cooling costs. Additionally, computers on the
network can perform valuable work while otherwise sitting idle. Although they may be cheaper
to operate, distributed computing networks are not necessarily less powerful than
supercomputers. For example, the statistics from Folding@Home project shows ever growing
collective computing power from contributors around the world, with a total processing power of
about 2.4 exaFLOPS in 2020, making it more powerful than the top 500 supercomputers
combined [7]. Thus, distributed computing networks harness the power of existing computing
resources and grant access to significant computing power while averting the costs of a
supercomputer.
Both supercomputers and distributed computing networks offer substantial computing power to
researchers in various fields. However, for many organizations and researchers, the costs
associated with building and maintaining or renting a supercomputer make distributed computing
networks a more attractive option. An important goal of this work is to explore the benefits of
distributed computing in two different computer clusters. First, an HTCondor pool consisting of
sixteen Raspberry Pi single-board computers and one laptop is created. Distributed computing
networks are well suited to problems that can be divided into a collection of smaller, independent
computations. However, not all problems can be separated in such a fashion. Therefore, the
scalability of distributed computing systems in problems like matrix computation is explored in
the Raspberry Pi clusters.
The Raspberry Pi is a low-cost single board computer with convenient sensors and controller
interfaces, as well as connectors to support various peripherals and high network connectivity.
Despite its small physical size, the Raspberry Pi’s computational performance, especially of the
newer models equipped with more powerful ARM processors, can reach the gigaFLOPS level.
The Raspberry Pi has been a popular device to support edge computing in internet-of-things
(IoT). The computing power can be further boosted by creating clusters with multiple Raspberry
Pi systems. In recent years, the CPU and energy consumption of various Raspberry Pi clusters
was evaluated using the HPL benchmark suite [8], a portable version of the LINPACK linear
algebra benchmark for distributed-memory computers. Example Raspberry Pi clusters include a
cluster with 25 Raspberry Pi Model 2B boards in [9] and clusters with 16 Raspberry Pi Model 3B
boards in [10]. In addition, a cluster with 20 Raspberry Pi Model B+ boards was built with the
evaluation focused on cryptography libraries [11].
In our case, we use a matrix multiplication program to evaluate the calculating performance of
the Raspberry Pi cluster. Matrix multiplication is widely used in various computing problems

43
such as linear systems solutions, coordinate transformation, and network theory. It is also one of
the most fundamental and computing intensive operations in machine learning. Evaluating matrix
multiplication can shed light on the capability and scalability of edge devices in artificial
intelligence applications such as federated learning in IoT networks [12].
The second distributed computing network is set up with Windows computers in university
campus labs, where each computer is configured to only execute HTCondor jobs after sitting idle
for a set time. The power of distributed computing networks in computationally intensive
problems is explored in both networks. The same matrix multiplication test was done on the
desktop computer clusters.
In addition to evaluating the computing power of the distributed computing networks, another
goal of this project is to configure distributed computer clusters to contribute to scientific
computing both locally and around the world. For this purpose, a guideline is created, allowing
campus users to securely access to the HTCondor central manager, submit computing jobs, and
retrieve results once done by the HTCondor pool. Moreover, the HTCondor pool is configured
alongside the BOINC installation on both computer clusters, allowing them to contribute to
community research projects such as the IBM World Community Grid [13], when the computers
would otherwise sit idle.
The remainder of this paper is structured as follows. Section 2 provides an overview of
HTCondor and BOINC, the middleware used to set up the distributed computing clusters. Section
3 explains the steps of setting up and configuring Raspberry Pi single-board computers as well as
campus lab computers as distributed computing networks. Section 4 evaluates the computing
performance of the two distributed computing clusters through a matrix multiplication program.
Finally, Section 5 summarizes our effort and discusses future work.
2. DISTRIBUTED COMPUTING MIDDLEWARE
To form independent computers together as a distributed network, middleware is needed to
provide services to enable the various components of a distributed system to communicate and
manage data, so that the computing power of the connected machines can be effectively
harnessed to create a high-throughput computing environment. It is software that lies in between
the operating system and applications, providing programming language and platform
independence, as well as code reuse. This section introduces two types of middleware, HTCondor
and BOINC, that are used to set up the distributed computing networks in this project.
2.1. HTCondor
HTCondor is developed by a team at the University of Wisconsin–Madison and is freely
available for use [14]. Users and administrators may tailor the program’s behaviour to unique
systems, networks, and environments with great flexibility.
HTCondor is a workload management system that provides scheduling policy and priority, as
well as resource monitoring and management [15]. The major processes of an HTCondor system
are depicted in Figure 1. The user submits jobs to an agent. This agent keeps the jobs in persistent
storage while looking for resources willing to run them. Agents and resources advertise
themselves to a matchmaker, which then introduces potential compatible agents and resources.
Once introduced, an agent will contact a resource and verify if the match is still valid. To execute
a job, both the agent and resource must start a new process. At the agent, a shadow provides all of

44
the necessary details to execute a job. At the resource, a sandbox creates a safe environment to
execute the job.
Figure 2 shows the state machine for the HTCondor program. The following description gives a
broad overview of HTCondor’s behaviour [15].
Figure 1. HTCondor kernel [6]
Figure 2. HTCondor program state machine [15]
When HTCondor starts, it immediately enters the Owner state. In this state, a user is controlling
the computer, or, more generally, the machine is unavailable to run any HTCondor jobs. There
are only two transitions out of the Owner state: HTCondor can initiate draining (N) to transition
to the Drained state, or the START expression evaluates to TRUE (A) allowing a transition to the
Unclaimed state.
One feature of HTCondor includes partitioning system resources into slots. A job may then be
written to request resource slots from a machine such that only a fraction of the system’s
resources is occupied. However, draining, the process of pooling resource slots, is required if a
job requires multiple resource slots. This is the purpose of the Drained state. Once HTCondor has
finished pooling system resources, it transitions to the Owner state (O).
On the other hand, if the START expression evaluates to TRUE (A), that is, the machine is
available to run HTCondor jobs, HTCondor enters the Unclaimed state where it sits idle. There
are five transitions from the Unclaimed state: 1) the user can control the computer causing the
START expression to evaluate to FALSE (B), 2) HTCondor can initiate draining (P), 3)

45
HTCondor can match an available job with the system’s resources entering the Matched state (C),
4) HTCondor can match an available job with the system’s resources entering the Claimed state
(D), and 5) the START_BACKFILL expression evaluates to TRUE (E).
While in the Matched state, HTCondor begins the process for officially claiming a job and
assigning it the machine’s resources. There are only two transitions from the Matched state.
HTCondor can complete the claiming protocol (G), or the START expression evaluates to
FALSE (F) indicating that a user is now controlling the computer. In rare cases, HTCondor may
immediately enter the Claimed state, bypassing the Matched state when certain background
processes (condor_startd and condor_schedd) do not receive the match notification in the correct
order.
In the Claimed state, HTCondor executes the job assigned by the central manager server. The
only transition from the Claimed state is to Preempting state (H), typically meaning either that
the job has finished or that a user is controlling the computer.
In the Preempting state, HTCondor will decide how to assign the system’s resources after
executing a job and exiting the Claimed state. If a job with a higher priority is available,
HTCondor will reassign the system’s resources and reenter the Claimed state (I). If a user is
controlling the computer again, HTCondor will halt execution of all jobs and enter the Owner
state (J).
Finally, in the Backfill state, HTCondor will utilize otherwise idle system resources to execute
some pre-configured tasks. There are three transitions out of the Backfill state. HTCondor can
match an available job with the system’s resources and enter the Matched state (L) or Claimed
state (M), or it will enter the Owner state after the START expression evaluates to FALSE (K)
indicating that a user is now controlling the computer.
In our work, HTCondor is set up and configured on both the computer that serves as the central
manager and the clients (i.e., Raspberry Pis and lab computers) that serve as computing
resources. Users can submit computing jobs to the central manager, and if a client is not under
control by any user for a set time, they are available to run HTCondor jobs.
2.2. BOINC
BOINC is an open-source middleware system for volunteer computing by a team at University of
California, Berkeley [4]. Figure 3 shows the components of the BOINC system.
Volunteers install the BOINC client application on a computing device, choose to support one or
more projects listed on the BOINC website [16], then attach the BOINC client to projects through
the account managers. Each project has a server that distributes jobs to volunteer devices. Once
attached to a project, the BOINC client periodically issues remote procedure calls (RPCs) to the
project’s server to report completed jobs and retrieve new job assignments. The client then
downloads application and input files, execute jobs, and uploads output files. The BOINC client
runs jobs at the lowest process priority and limits the total memory footprint, with the purpose of
computing invisibly to the volunteer.

46
Figure 3. The components and RPC interfaces of the BOINC system [4]
The BOINC client software consists of three programs that communicate via RPCs over a TCP
connection: 1) The core client manages job execution and file transfers. The BOINC installer
initiates the client to run when the computer starts up. It exports a set of RPCs to the GUI and the
screensaver. 2) A GUI (the BOINC Manager) assists volunteers in controlling and monitoring
computation of individual jobs. 3) An optional screensaver shows application graphics for
running jobs when the computer is idle.
In our work, the BOINC client software is installed on the Raspberry Pis and lab computers,
making them as volunteering computing devices. BOINC client is set up as a pre-configured
Backfill task to contribute to the IBM World Community Grid project. Therefore, if the
computers are not under control by a user for a set time, and there is no current HTCondor jobs to
run, they will contribute a promised portion of computing resources to the World Community
Grid project.
3. COMPUTER CLUSTER CONFIGURATION
In this section, the procedures of configuring the middleware on the Raspberry Pi single-board
computers as well as the campus lab computers to form distributed computing networks are
explained.
3.1. Configuring the Raspberry Pi Cluster
3.1.1. HTCondor Installation and Verification
Figure 4. Raspberry Pi HTCondor pool

47
Our first distributed computing cluster consists of sixteen Raspberry Pi single board computers
and an HP Pavilion laptop, interconnected with a TP-Link Gigabit Ethernet switch. This cluster is
depicted in Figure 4. Among the single board computers, there are eleven Raspberry Pi 2B, four
Raspberry Pi 3B, and one Raspberry Pi 3B+ systems, each with a quad-core ARM processor and
Ubuntu Server 20.04 LTS flashed to the SD card. The laptop is installed with Ubuntu Desktop
20.04.
HTCondor is installed on both the laptop, configured as the central manager, and the Raspberry
Pis, as the clients. A central manager has the ability to submit jobs to the pool, allow jobs to be
executed using its resources, and add security configurations (e.g., password) to the pool. To run
any application using this distributed computing network, a user must have access to the laptop.
Each Raspberry Pi is configured as an HTCondor execute node, allowing jobs to be performed
using its resources. After configuring the Raspberry Pis and the laptop, the status of the pool was
queried using the condor_status command.
To test the HTCondor pool, a simple test program called sleep.sh was written and submitted. This
program made an executing machine sleep for six seconds and print its hostname. By checking
the hostname that was returned, the machine executing the job could be identified, and the pool's
functionality could be verified. To execute this program on the pool, the submit file in Figure 5
was written.
Figure 5. Test program submission file
According to the submission file, the sleep.sh program was run 100 times. Each time sleep.sh ran,
stdout was redirected to an output file. Similarly, stderr was redirected to the error file. The
program would only run on computers with an “armv7l” architecture, in our case the Raspberry
Pis. Additionally, any necessary files would be transferred to the executing machine. The job was
then submitted to the pool for execution. The status of the pool during the job was monitored
using condor_status and condor_q commands. The output of running these commands is shown
in Figure 6. The output files were verified to contain the hostname of every Raspberry Pi in the
HTCondor pool. Note that the status of the processor cores of the laptop and only one Raspberry
Pi is included; the rest are omitted due to space.
$ condor_submit sleep.sub
Submitting
job(s)...................................................................................
.................
100 job(s) submitted to cluster 19.
$ condor_status
Name OpSys Arch State Activity LoadAv Mem ActvtyTime
slot1@htcondorcm.Home LINUX X86_64 Unclaimed Idle 0.020 956 0+22:44:43
slot1@pfwpi2.Home LINUX armv7l Claimed Busy 0.000 213 0+00:00:00

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... ... ... ... ... ...
Total Owner Claimed Unclaimed Matched Preempting Backfill Drain
X86_64/LINUX 4 0 0 4 0 0 0 0
armv7l/LINUX 64 0 64 0 0 0 0 0
Total 68 0 64 4 0 0 0 0
$ condor_q
-- Schedd: htcondorcm.Home : <192.168.0.98:9618?... @ 10/04/21 23:12:31
OWNER BATCH_NAME SUBMITTED DONE RUN IDLE TOTAL JOB_IDS
htcondor CMD: sleep.sh 10/4 23:11 59 41 _ 100 19.0-99
41 jobs; 0 completed, 0 removed, 0 idle, 41 running, 0 held, 0 suspended
Figure 6. Pool configuration verification and testing result on the Raspberry Pi cluster
In addition to verifying and testing the HTCondor pool, the pool's performance was also
quantified. Using the condor_status command, the MIPS (millions of instructions per second)
rating for each CPU core was calculated. In total, the HTCondor pool is capable of 232200 MIPS.
For reference, this score is approximately double that of an AMD 5950x (101163 MIPS), one of
the most powerful consumer processors available today [17].
3.1.2. BOINC Installation and Verification
In addition to installing HTCondor, the Raspberry Pis were set up with BOINC running
simultaneously as a pre-configured Backfill task to contribute to the IBM World Community Grid
project. After installing the BOINC package, several configuration files in the /etc/boinc-client
were edited. First, the remote hosts file (remote_hosts.cfg) was edited to allow a separate
computer (i.e., the laptop) with a specified IP address to control the BOINC client remotely. After
saving the remote hosts file, the core client configuration file (cc_config.xml) was edited to
enable remote connections. Then, the password file (gui_rpc_auth.cfg) was edited with a
password to authenticate remote BOINC connections. Finally, BOINCTasks was installed on the
laptop specified in the remote hosts file to view the BOINC client installed on each Raspberry Pi.
Figure 7 depicts all the Raspberry Pis (circled in red) are listed in the BOINCTasks program.
Figure 7. BOINCTasks computer view
The Raspberry Pis were also visible from the World Community Grid website [13] depicted in
Figure 8 providing additional verification that they were contributing to research projects. The
device names are circled in red – please note that not all devices are included due to space.
Additionally, through the World Community Grid website, the behaviour of the devices can be

49
altered by editing device profiles, and new devices can be added by copying the account key and
project URL.
Figure 8. World Community Grid device management page
3.2. Configuring Campus Lab Computers
After successful installation of HTCondor and BOINC on the Raspberry Pi cluster, the next step is
to configure the lab computers on campus as a distributed computing cluster. We obtained the
permission to install HTCondor and BOINC on the Windows computers in the computer labs
limited to one campus building. In these labs, there are 252 desktop computers with 1,140 physical
CPU cores, contributing to a total computing capability of 19.872 teraFLOPS. This aggregated
computing power is about 0.937% performance of the 500th supercomputer on the current Top500
most powerful supercomputer systems list [18]. In addition, a campus cloud server was reserved as
the HTCondor central manager server.
Unlike the Linux version of the software, the Windows version of HTCondor comes bundled with
an installer. This installer automatically configures the computer using a configuration file.
However, some manual configuration (the same process used for the Raspberry Pi cluster) is still
required to specify the security settings. After configuring HTCondor, the installation was verified
by entering the condor_status command in the command line.
Additionally, a simple test program called sleep.bat was written and submitted to the HTCondor
central manager. Similar to the sleep program in Section 3.1.1, this program made the executing
machine sleep for six seconds and print its hostname. While the batch of jobs was running, the
status of each CPU core on lab computers were monitored by entering the condor_status
command again, with similar running result as in Figure 6. By observing each CPU core status, we
verified that the software was installed and configured properly.
In addition to exploring the computing power of the distributed computing networks, another goal
of this project is to configure distributed computer clusters to contribute to scientific computing
both locally and around the world. For this purpose, a guideline is created to allow campus users to
securely access to the HTCondor manager, submit computing jobs there, and retrieve results once
the jobs are completed by the HTCondor pool. This provides a convenient way for campus users to
utilize lab computers as powerful computing resources.

50
Besides HTCondor, BOINC was also installed on the lab computers. Like HTCondor, the Windows
version of BOINC is accompanied by an installer which automatically configures the behaviour of
the BOINC client. By checking the World Community Grid device management page, similar as in
Figure 8, the BOINC installation could be verified.
After successful installation and configuration of HTCondor and BOINC, each lab computer
connected to the distributed computing network is configured to always be in one of the following
three states with decreasing priority: owner - under control by a legitimate user; busy - working on a
submitted job to HTCondor pool; or backfill - contributing to World Community Grid.
Currently the office of information technology services on campus is in charge of managing and the
distributed computing network with the lab computers. Security audits will be done periodically to
make sure no intruders would access the computers and cause harm to the campus network.
Additionally, information regarding the distributed computing network will be spread to faculty to
raise awareness about the computing resource available for their research.
4. COMPUTING PERFORMANCE EVALUATION
To evaluate the computing power and the scalability of the two constructed distributed computing
clusters, a simple matrix multiplication program was written and run using the HTCondor
pool.This program computes the product of two square matrices of the same size. The program
required three arguments to specify the dimensions of the matrix, number of CPU cores involved
in calculation, and the portion of the product matrix to calculate. For example, if arguments 64, 1
and 0 were provided, a single CPU core would calculate the product of two 64x64 matrices. If
arguments 128, 64, and 0 were provided, the program would split the multiplication of two
128x128 matrices into 64 parts and compute the first portion of the matrix product. Tests were
conducted using 1, 2, 4, 8, 16, 32, and 64 cores to multiply two 1024x1024, 2048x2048, or
4096x4096 matrices on the two networks: Raspberry Pi cluster and the lab computer cluster. The
time required for each test was determined by examining the timestamps of the first and last log
file entries. For each combination of matrix size and number of cores, three trials were conducted
on the Raspberry Pi cluster, whereas ten trials were conducted on the lab computer cluster. The
tests were run when the clients are not under control of any users. The average execution time for
each test was calculated, with results listed in Table 1.
Table 1. Matrix multiplication execution times on both distributed computing networks.
Matrix Size Cores
Average Time (MM:SS)
Raspberry Pi Cluster Lab Computer Cluster
4096x4096
64 01:02 00:17
32 01:42 00:19
16 02:41 00:40
8 04:26 00:53
4 08:17 01:24
2 15:28 03:09
1 27:47 05:31
2048x2048
64 00:25 00:07
32 00:19 00:05
16 00:20 00:08
8 00:36 00:13
4 00:59 00:15
2 01:53 00:42
1 03:35 00:59

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1024x1024
64 00:22 00:04
32 00:12 00:03
16 00:04 00:07
8 00:06 00:06
4 00:09 00:07
2 00:16 00:08
1 00:29 00:12
In general, as the number of cores increases, the execution time decreases. However, as the
matrix size decreases and the number of cores increase, this relationship is no longer true. This
observation is best visualized in Figure 9.
For 1024x1024 and 2048x2048 matrices, the optimum number of cores was 16 or 32 in the two
distributed computing networks, not the largest number of cores (64). This counterintuitive
observation is most plausibly explained by HTCondor’s overhead. For HTCondor to complete a
job, it must match the job with a client, send the program to execute, wait for the client to execute
the program, and receive the computation results. The total time to complete a single job is
expressed using (1).
𝑇𝑡𝑜𝑡𝑎𝑙 = 𝑇𝑚𝑎𝑡𝑐ℎ + 𝑇𝑠𝑒𝑛𝑑 + 𝑇𝑒𝑥𝑒 + 𝑇𝑟𝑒𝑐𝑒𝑖𝑣𝑒 = 𝑇𝑜𝑣𝑒𝑟ℎ𝑒𝑎𝑑 + 𝑇𝑒𝑥𝑒 (1)
Where 𝑇𝑡𝑜𝑡𝑎𝑙 is the total job completion time, 𝑇𝑚𝑎𝑡𝑐ℎ is the time required to match a job with a
client, 𝑇𝑠𝑒𝑛𝑑 is the time required to send the client the required resources, 𝑇𝑒𝑥𝑒 is the time
required by the client to complete the job, and 𝑇𝑟𝑒𝑐𝑒𝑖𝑣𝑒 is the time required to send the results
back to the central manager server. 𝑇𝑜𝑣𝑒𝑟ℎ𝑒𝑎𝑑 = 𝑇𝑚𝑎𝑡𝑐ℎ + 𝑇𝑠𝑒𝑛𝑑 + 𝑇𝑟𝑒𝑐𝑒𝑖𝑣𝑒, referring to the total
overhead time involved but not actually in executing the job itself.
(a)
0:00:00
0:05:00
0:10:00
0:15:00
0:20:00
0:25:00
0:30:00
1 2 4 8 16 32 64
Average
Time
(H:MM:SS)
Core Count
Average Time vs Core Count (Raspberry Pis)
4096x4096
2048x2048
1024x1024

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(b)
Figure 9. Task completion time vs number of cores in (a) the Raspberry Pi cluster and (b) the lab computer
cluster
Assuming in the same distributed computing network, 𝑇𝑜𝑣𝑒𝑟ℎ𝑒𝑎𝑑 stay approximately constant for
different jobs, the time benefits of a distributed computing network may be predicted based on
the relationship between 𝑇𝑜𝑣𝑒𝑟ℎ𝑒𝑎𝑑 and 𝑇𝑒𝑥𝑒. For example, to execute the same job, assuming
highly parallelizable, if the total number of computing cores in a distributed computing network
is doubled, the job execute time 𝑇𝑒𝑥𝑒 will be halved. In the limit, if 𝑇𝑒𝑥𝑒 is significantly longer
than 𝑇𝑜𝑣𝑒𝑟ℎ𝑒𝑎𝑑, the total job completion time approaches ½ of before, which is the ideal result of
doubling the number of cores. On the other hand, if 𝑇𝑜𝑣𝑒𝑟ℎ𝑒𝑎𝑑 is comparable to or longer
than𝑇𝑒𝑥𝑒, the work will not benefit much from additional cores and further division into smaller
jobs. Additionally, if 𝑇𝑒𝑥𝑒 is too small, meaning the job is simple and can be done extremely fast,
the execute nodes could return results before the central manager has completed the match
process for the remaining jobs. In this scenario, the total completion time for a batch of jobs
could increase. Consequently, the work will only benefit from additional cores and further
division into smaller jobs if 𝑇𝑜𝑣𝑒𝑟ℎ𝑒𝑎𝑑 is significantly shorter than 𝑇𝑒𝑥𝑒.
5. CONCLUSIONS AND FUTURE WORK
In conclusion, an HTCondor pool was successfully created using a collection of sixteen
Raspberry Pi single-board computers and one laptop. The pool's functionality was verified using
a simple test job. Computing intensive programs such as matrix multiplication can be efficiently
split into smaller jobs to be distributed to the Raspberry Pis and executed independently. The
computing power of the Raspberry Pi cluster can be greatly boosted by forming a distributed
computing network, making it suitable to carry out compute-intensive applications such as
federated learning in power-constrained IoT devices.
A similar HTCondor pool was constructed to form a second distributed computing system
composed of Windows computers in campus labs. As a result, university faculty and students
have access to a significant amount of computing power by submitting computing jobs to the
HTCondor central manager server. In addition to installing and configuring HTCondor, BOINC
was also installed on the Raspberry Pi cluster and lab computer cluster as a Backfill task to
contribute to scientific computing projects such as the IBM World Community Grid. In this
configuration, researchers partnered with scientific computing projects associated with BOINC
0:00:00
0:00:30
0:01:00
0:01:30
0:02:00
0:02:30
0:03:00
0:03:30
0:04:00
0:04:30
0:05:00
0:05:30
0:06:00
1 2 4 8 16 32 64
Average
Time
(H:MM:SS)
Core Count
Average Time vs Core Count (Lab Computers)
4096x4096
2048x2048
1024x1024

53
will have more resources available from the community to help with high-demand computing
needed for research such as climate, cancer, and vaccine study.
Additionally, the scalability of distributed computing networks in matrix multiplication was
investigated on the Raspberry Pi cluster and lab computer cluster. In general, as the number of
cores increases, the execution time decreases. However, as the matrix size decreases and the
number of cores increase, this relationship is no longer valid. This counter-intuitive observation is
most plausibly explained by HTCondor’s overhead. Additionally, the relationship between
HTCondor’s overhead (primarily the time required to match jobs with resources) and the
execution time of a job may predict the benefit of further parallelization. Regardless, the power of
distributed computing systems is apparent: many small computers can create a body of
computing power on par with most powerful consumer hardware available on the market.
Future directions of the work include evaluating different types of programs in distributed
computing networks. Unlike matrix multiplication, not all compute-intensive programs can be
parallelized easily. The power of distributed computing, especially of resource-constrained IoT
devices like the Raspberry Pi should be evaluated for different types of computations (e.g.,
convolution and filtering) involved in edge computing. Furthermore, as security is always a
concern for any distributed network, mechanisms such as encryption, authentication, and load
balancing need to be carefully studied.
ACKNOWLEDGEMENTS
The authors would like to thank the Chapman Scholars Program, the Electrical and Computer
Engineering Department, and the Office of Information Technology Services at Purdue
University Fort Wayne for financial and technical support for this project.
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