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Furthermore, the introduction of the 80% sequence length overlap threshold to the computation of UniRef90 and UniRef50 reduced the compute time more than 5-fold, further improving the scalability of UniRef.
The memory-bound interaction vector computation, accounting for almost two thirds of the compute time, features pre-allocation and a specific data ordering to reduce the costs of memory access.
We demonstrate that the compute time, t, scales inversely with the number of computational nodes as t ∝ (Nnodes)−0.95 ± 0.04.
On this basis, the size of problem and the compute time are reduced.
However, the compute time is also smaller as less particles will have to integrated.
The two stages of our approach would still apply to this scenario, although we would need to consider the compute time required to perform the conversion.
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In most cases, the compute times with sensitivities are substantial (see also Additional file 1: Figure S3 for other tolerances).
To contrast the computational cost, Table 4 gives the computing time for the Reserving Place Algorithm and the average computing time for the Genetic Algorithm for the 2-gene network under various values of T.
The computing time for the conventional computer increased exponentially with the number of qubits.
An algorithm is devised to reduce the computing time by using a vector computer.
In addition, the GPU-based parallel computation is shown to significantly reduce the computing time.
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