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For example, creating a dense stoichiometric matrix with 4000 reactions takes approximately 100MB of computer memory and various matrix operations may increase the actual memory need by a factor of ten.
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Since memristive devices are especially tiny to be densely packed in crossbar-like structures and possess long time memory needed by neuromorphic synapses, this paper shows how to approximate the behavior of synapses in neural networks using this memristive device.
A first reason to explain the relatively low speedup is the amount of memory needed by the application.
Hence, LIDE with mixture model would require roughly one seventh of memory needed by integral image based AHE.
Thus, the amount of memory needed by the algorithm depends on the number of parents inferred by the network.
First, we estimate the memory needed by our data structure, under the assumption of an infinite number of bitmaps.
These restrictions can be approximately considered as using 1000th, 1%, 10% and 100% of memory needed by GA*.
The most critical parameter in selecting a candidate assembler tool is the memory needed by the tool during the assembly process, especially with the increase of data volumes in NGS platforms.
Their solution delivered a significant improvement in terms of execution time and memory needs, as by the definition it considers only local information in each iteration.
The 6.5%% of this needed RAM memory would be allocated for the buffers, concluding that in large neighbourhoods, the RAM memory needs are controlled by the algorithm rather than by the communication layer.
Moreover, storing the kernel can be another issue as the amount of memory needed is cursed by the dimensionality of the system.
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