New challenges that emerge from our increased ability to utilize computational resources and hardware infrastructure include the need to assure reliability and reproducibility of identically analyzed data, and the desire to continually lower the costs of employing and sharing data, tools and services.
Our framework slices a statistical ADS׳ feature space into multiple disjoint subspaces and then performs anomaly detection separately on each subspace by utilizing more computational resources.
Hydrodynamic models generally deal with large sets of data and utilize substantial computational resources.
Because our kernels under-utilize available computational resources to some extent, the kernels are memory bounded (at the L1 cache).
Evolutionary algorithms that break fragments and make crossovers allow for an exhaustive exploration of the chemical space [60, 61], however, using these techniques also requires applying chemical stability and synthetic feasibility rules, which, in turn, utilizes extra computational resources.
The positioning, i.e., localization, of the sensor motes is performed offline after the extraction of the agents by a central unit, which can utilize significant computational resources for this purpose.
To address these challenges, pairwise alignment methods were developed to utilize less computational resources [ 6- 8].
This enables highly task-parallel investigations to utilize distributed computational resources such as clusters, grids, and clouds to greatly increase productivity for the end-user.
In this process, the optimal computing budget allocation technique is applied so as to fully utilize the computational resource and potentially save the computational time.
Recent studies have also utilized computational modeling based approaches to build predictive models based on structural information and activity data8.
