Since all of the stochastic analysis methods require to perform an analysis multiple times, it is possible to decrease the overall analysis time by exploiting the inherent parallelism showed by the stochastic analysis algorithms on parallel computers.
We design an efficient computational engine that is capable of conducting MSAs by exploiting the underlying parallelism embedded in the computational steps of multiple sequence algorithms.
This proposed work is about a trainable neural chip using Field Programmable Gate Array (FPGA) as this helps in learning capability by exploiting the inherent parallelism of neural network.
Implementations of NFA with hardwired circuits can overcome the speed deficiency by exploiting the massive parallelism offered by dedicated hardware circuitries, but this approach does not support efficient dynamic updates.
In April 2015, Nils Voss and Maxeler developed a dataflow implementation of the algorithm that performs Fast Fourier transform (FFT) by exploiting the dataflow parallelism (application icon is given in Fig. 20).
Moreover, the use of multiple levels of parallelism reports multiplicative accelerations: first, the speedups achieved in the multi-GPU system, which can be up to ×3.71 with 4 GPUs enabled; and second, the accelerations up to ×32 the can be achieved by exploiting the data parallelism on a GPU.
Although time complexity of calculating a heuristic function has been improved from O(n m t) to O(n m2 + n mt) in Section 2.2.1, the computation of heuristic functions for new expanded nodes in A* search can be further sped up by exploiting the inherent parallelism capacity of a GPU.
When more precision is needed a conversion to floating point is done once the image is stored in the GPU global memory, exploiting the parallelism provided by the GPU.
