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What is Embarrassingly Parallel Workload

Handbook of Research on Big Data Storage and Visualization Techniques
Workload where little or no effort is needed to divide a task into a number of parallel tasks.
Published in Chapter:
Big Data in Massive Parallel Processing: A Multi-Core Processors Perspective
Vijayalakshmi Saravanan (The State University of New York at Buffalo, USA), Anpalagan Alagan (Ryerson University, Canada), and Isaac Woungang (Ryerson University, Canada)
DOI: 10.4018/978-1-5225-3142-5.ch011
Abstract
With the advent of novel wireless technologies and Cloud Computing, large volumes of data are being produced from various heterogeneous devices such as mobile phones, credit cards, and computers. Managing this data has become the de-facto challenge in the current Information Systems. According to Moore's law, processor speeds are no longer doubling, the processing power also continuing to grow rapidly which leads to a new scientific data intensive problem in every field, especially Big Data domain. The revolution of Big Data lies in the improved statistical analysis and computational power depend on its processing speed. Hence, the need to put massively multi-core systems on the job is vital in order to overcome the physical limits of complexity and speed. It also arises with many challenges such as difficulties in capturing massive applications, data storage, and analysis. This chapter discusses some of the Big Data architectural challenges in the perspective of multi-core processors.
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