The phrase "accelerated computation" is correct and usable in written English.
It can be used in contexts related to computing, technology, or data processing where speed and efficiency are emphasized.
Example: "The new algorithm allows for accelerated computation, significantly reducing the time required to process large datasets."
Alternatives: "enhanced processing" or "speeded-up calculations."
We need to check whether the accelerated computation using reduced precision is still generating meaningful results.
We demonstrate a linear time mapping between these solutions that allows for accelerated computation of the solution of the fractional order problem.
Here we review and discuss these challenges in seven areas: first, experimental interrogation; second, data curation; third, model building and integration; fourth, accelerated computation; fifth, analysis and visualization; sixth, model validation; and seventh, collaboration and community development.
We discuss simulations of models with discrete and continuous variables, and using an array of algorithms ranging from single-spin flip Metropolis updates over cluster algorithms to multicanonical and Wang-Landau techniques to judge the scope and limitations of GPU accelerated computation in this field.
The accelerated computation of values of the form μ i, j is obtained by the application of fast matrix multiplication subroutines between sibling recursive calls of the algorithm.
We confirm the correctness of the contagion spread calculations when computed on the GPU by comparing results of the original CPU-only computation to the results of the GPU accelerated computation.
In a previous work, we presented a system which combines active solid state drives and reconfigurable FPGAs (which we called reconfigurable active SSD nodes or simply RASSD nodes) into a storage-compute node that can be used by a cloud datacenter to achieve accelerated computations while running data intensive applications.
We present GPU and APU accelerated computations of Finite-Time Lyapunov Exponent (FTLE) fields.
We use Bézier extraction to accelerate the computation of NURBS and B-spline terms and employ H-matrices to provide accelerated computations and memory reduction for the dense matrices that result from the boundary integral discretization.
The improved efficiency is attributed to the absence of the initial energy-minimization step required before applying NMA and accelerated computations for the force constant matrix (second derivatives of the potential) due to the simplified energy function (Tirion, 1996).
A description of the methods used will be presented, followed by a discussion of the advanced GPU (Graphics Processing Unit) computing technology applied to accelerate computation.
