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On the other hand, with a virtual GPU on which all blocks are active simultaneously (totally utilized), the order of the algorithm would be O(R) as the threads are independent from each other, not counting the synchronization points.
Since higher values for N increase the time needed to compute the algorithm, the order of the algorithm must be chosen so that it will have good results while maintaining acceptable completion times.
A mapping f Δ t on R 2 n is an algorithm with time step-size Δ t for (f t H ) if we have f Δ t H (z ) = f Δ t (z ) + O (Δ t k ) ; the number k (usually an integer ⩾1) is called the order of the algorithm.
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Variation of the accuracy of numerical solution with the time step and the order of the predictor-corrector algorithm is studied on three different partially fixed models of the oligopeptide (Ala 9.
To decrease the order of the dynamic programming algorithm to O(L), we consider imposing the additional constraint, which we call C1(A, τ, O), that the alignments between the three nodes connected to O remain fixed.
The running time of the algorithm has the order of the product of m and the number of leaves in S0.
It is found that the partition algorithm and the order of the physical variables are central to parallelization performance.
Our algorithm takes the order of the sequencing flows into account.
Due to the quadratic order of the algorithm, this division into subsequences implies a substantial efficiency gain.
Reversing the order of evaluation in the algorithm for i and k would eliminate this overhead.
As stated before, reversing the order of evaluation in the algorithm for i and k would eliminate this overhead.
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