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The online processes are distributed over a large set of processors in this network.
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It is made up of a large set of fully encapsulated 360 32-bit RISC processors and 360 distributed memories.
Since CNVhac is a single-array based strategy, the running time could be reduced by executing CNVhac on multiple processors in parallel when analyzing a large set of samples.
It consists of a large set of differential and algebraic equations, which is computational intensive and challenging to solve using single-processor based dynamic simulation solution.
Our current implementation runs on a single processor and it would need days or months to run a larger set of proteins on a larger set of genomes.
Performances on recent processor architectures heavily rely on the ability of applications and compilers to exploit a more and more diverse and large set of parallel features.
From this large set of various BG methods, we have conducted a relevant experimental analysis to evaluate both their robustness and their practical performance in terms of processor/memory requirements.
The code is parallelized using MPI and scales well to a large number of processors.
Their approach is computationally expensive, requiring extended simulation times and a large number of processors to complete.
This happens by feeding large sets of data through layers of interconnected processors.
To simulate a large-scale agent based model, the most traditional way is to distribute it across a set of processors.
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CEO of Professional Science Editing for Scientists @ prosciediting.com