A new computational model for real gains in big data processing power
Date
2017-02-01
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Lviv Politechnic Publishing House
Abstract
Big data and high performance computing are
seen by many as important tools that will be used to
advance science. However, the computational power needed
for this promise to materialize far exceeds what is currently
available. This paper argues that the von Neumann
computational model, the only model in everyday use, has
inherent weaknesses that will prevent computers from
achieving the envisaged performance levels. First, these
weaknesses are explored and the properties of a
computational model are identified that would be required
to overcome these weaknesses. The performance benefits of
implementing a model with these properties are discussed,
making a case that a computational model with these
properties has the potential to address the needs of high
performance computing. Next, the paper presents a
proposed computational model and argues that it is a viable
alternative to the von Neumann model. The paper gives a
simplified outline of an architecture and programming
language that express the proposed computational model.
The main feature of this computational model is that it
processes variables as they become defined. These variables
can be processed in any order and simultaneously, avoiding
bottlenecks and enabling high levels of parallelism. Finally,
the computational model is evaluated against the properties
identified as desirable, showing that it is possible to design
an architecture and programming language that do not
have the weaknesses of the currently dominant von
Neumann model. The paper concludes that the weaknesses
which limit the performance of current computers can be
overcome by exploring alternative computational models,
architectures and programming languages, rather than by
working towards incremental improvements to the existing
dominant model.
Description
Keywords
Big data, Computer architecture, Computational models, High-performance computing, Programming language
Citation
Conrad S. M. Mueller A new computational model for real gains in big data processing power / Conrad S. M. Mueller // Advances in Cyber-Physical Systems. — Lviv : Lviv Politechnic Publishing House, 2017. — Vol 2. — No 1. — P. 11–21.