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Using the Cloud for computationally intense tasks has been seen in recent years as a convenient way to process data quickly.
To solve this issue, we are considering allocating some EMBL-EBI cluster's nodes to run computationally intense tasks (e.g. short read alignment) during our training courses as well as using cloud computing services to decentralize the execution of tasks.
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Embedding a forensic watermark is typically a computationally intense task.
Indeed, to deal with the computationally intense task of calculating reads coverage at specific chromosomal positions (namely the pileup), which is fundamental in ASE studies, ASEQ combines the power of multi-threaded computation with samtools C APIs, a programming library that offers rapid random access functionalities to indexed alignment files [ 16].
These are identical, automatic, repetitive and computationally intensive tasks.
Therefore, the preprocessing operations are the most computationally intensive tasks and are the limiting factors.
Video compression is among the most computationally intensive tasks in current imaging technology [1][2].
Especially computationally intensive tasks are predestined to be put in a cloud computing environment.
We therefore developed a pilot grid architecture that can carry out such computationally intensive tasks.
Computationally intensive tasks are parallelized on the Vital-IT high-performance computing cluster [[ 17], Additional file 2].
Clearly, this is a time-consuming and computationally intensive task.
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