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To reduce the extensive computational workload due to massive fragments, a grid-clustering algorithm for fragment modeling is also proposed.
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Large-scale urban systems simulations are complex and with a large number of active simulation entities the computational workload is extensive.
Metagenomic data analysis is both data- and computation- intensive, which requires extensive computational power.
Furthermore, the computational speedup increased when the computational problem size increased, which means that the high-performance heterogeneous embedded computing platform was more efficient when the computational workload was larger.
Extensive computational results are presented.
The MDFT filter bank uses double computational workload for filtering.
The DPR has eight constants and requires less computational workload to obtain the z-factor.
For large-sized circuits,7 most of the computational workload is in the linear solver.
This is with only two workloads used; as the number of workloads increases, the benefit of evaluating workloads singly instead of jointly becomes larger and the computational workload of the joint optimization rapidly becomes infeasible.
Extensive computational experiments confirm the efficiency of the proposed methodology.
Extensive computational studies have demonstrated the effectiveness of the proposal.
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