High-performance systems are a vital tool for supporting the rapid developments being recorded in software technologies.
The recent innovations in software systems have changed the way we see and deal with our physical world. Many applications today, as
part of their functions, implement highly data-intensive algorithms such as machine learning, graphics processing, and scientific
calculations which require high processing power to deliver acceptable performance. The Central Processing Unit (CPU)-based
architectures, which have been used over the years, are not coping well with these classes of applications. This has led to the emergence
of a new set of architectures such as Graphics Processing Units (GPUs) and Field Programmable Gate Arrays (FPGAs). These
architectures are based on the computing paradigm referred to as dataflow computing in contrast to the popular control-flow computing.
In this research, we used the dataflow engines developed by Maxeler Technologies, to compare performance with conventional CPUbased
parallel systems by writing a program each for the two platforms, which solves a typical vector operation, and run it on the two
platforms. The results of the experiment show that even though the Dataflow Engines (DFEs) we used in our experiments run at the clock
frequency of 100MHz, its performance is at par with a quad core CPU which runs at 1.86GHz per core.
Published In:IJCSN Journal Volume 7, Issue 4
Date of Publication : August 2018
Pages : 263-271
Tables : 02
Abdulkadir Dauda :
was born in Lafia, Nasarawa State of Nigeria
on the 25th of October 1982. He obtained the Bachelor of Science
degree in Computer Science from the Usmanu Danfodiyo
University Sokoto, Nigeria in 2006. He worked with the Nigerian
Judiciary as a Programme Analyst from February 2009 to April
2014 when he joined the Federal University Lafia as a Graduate
Assistant. In 2015, he proceeded to the University of Bedfordshire,
United Kingdom for his Masters of Science Degree which he
completed in January 2017. He currently works as an Assistant
Lecturer in the department of Computer Science, Federal
University Lafia, Nigeria. His research interests are in the area of
High-Performance Computing and Distributed Systems.
Adoga, H. U. :
holds a Bachelor of Engineering (B.Eng.) in
Electrical & Electronics Engineering from the University of
Maiduguri, Nigeria, with specialization in data communications and
networks. He also holds a Master of Science (MSc.) degree in
Computer Science, from the University of Hertfordshire, England.
He is currently a lecturer with the Department of Computer
Science, Federal University Lafia, Nigeria. His research interests
are in the areas of Software Defined Networking (SDN), IOT and
Distributed Systems. He is a registered member of the Institute of
Electrical and Electronics Engineers (IEEE), the Nigeria Computer
Society (NCS), and the Nigeria Society of Engineers (NSE). As a
CCNP professional, Haruna is also fascinated by design and
configuration of computer networks.
Ogbonoko, J. F. :
holds a Bachelor of Science (BSc.) degree in
Computer Science from the Benue State University, Makurdi,
Nigeria. He also holds a Master of Science (MSc.) degree in
Software Systems and Internet Technology from the University of
Sheffield, United Kingdom. He currently lectures in the
Department of Computer Science, Federal University Lafia,
Nasarawa State, Nigeria. His research interests are in the area of
Software Engineering, Internet of Things, and Big Data.
In this work, we highlighted some of the important
advancements in the area of high performance computing.
We discussed the different architectures of the CPU-based
systems and the incredible performance that is gained over
single core processors by joining multiple cores together.
We also pointed out some of the disadvantages, such as
heat generation and power consumption and the associated
costs, of the continued increase in the number of
processing units in that manner. As an alternative to
control flow computing, we discussed dataflow computing
and how it can be used to accelerate certain classes of
applications which are characterized as SIMD. As the main
aim of our research is to compare performance, we
implemented a vector operation on both CPU-based
parallel systems and Maxeler dataflow engine and used it
for the experiment. The outcome of our experiments shows
that the DFEs perform better even though they run at a
lower clock frequency than the CPUs.
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