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The advantage of this algorithm is that it is readily parallelized in modern high computing architecture.
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Due to the increasing gap between speed of CPU and speed of access to memory the latter is often the main bottleneck in modern high performance computing.
However, to make effective use of the produced data, the design of big data algorithms and their efficient implementation on modern high performance computing systems is required.
Due to increasing vector capabilities, efficient mapping of bit-parallel APM algorithms onto modern high performance computing architectures is an interesting research topic.
Energy efficiency is a major concern in modern high performance computing (HPC) systems and a power-aware scheduling approach is a promising way to achieve that.
Modern high performance computing is dominated by parallelization.
Our work seeks to highlight the scope of modern high-performance computing systems and computational optimization tools.
The computer codes were written to exploit the efficiencies engineered into modern, high-performance computing software.
Issues related to the implementation of efficient algorithms on modern high-performance computing systems will be discussed.
However, developing and maintaining software for such systems is fraught with challenges, especially for modern high-performance computing (HPC) applications that can benefit the most from leveraging accelerators.
Therefore, they do not take advantage of modern high-performance computing systems.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com