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The 2D algorithm is found to improve the performance of digital filtering systems by segmenting the 2D input into smaller block sizes, which are shown to be efficient and lead to highly parallel implementation.
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The computation speed is achieved by means of making use of highly parallel implementations, as well as custom data storage mechanisms (i.e. registers, local memories, etc).
This standards-based parallelization scheme is significant in that it results in a highly portable parallel implementation of AD4 with user customizability in the balance between system-level and node-level parallel execution.
Processing of large 3D data sets (3.5 × 6 × 2 mm) is also computationally intensive but lends itself to optimization through highly parallel algorithm implementation.
A highly sophisticated parallel implementation of the scheme is described that is fourth-order accurate on two-dimensional dynamically-adaptive body-fitted structured grids.
As prototypes for this class of algorithms we consider the iterative Jacobi and Gauss-Seidel smoothers and aim at highly efficient parallel implementations for cache-based multicore architectures.
In this study, we present a highly scalable massively parallel implementation of the fast marching method using a domain decomposition approach.
In this paper, we present a highly parallel formulation and implementation of the MEME motif discovery algorithm using the CUDA programming model.
In this work, we analyze the scalability of inexact two-level balancing domain decomposition by constraints (BDDC) preconditioners for Krylov subspace iterative solvers, when using a highly scalable asynchronous parallel implementation where fine and coarse correction computations are overlapped in time.
The image-processing algorithm was redesigned to enable highly parallel, single-pass image-processing implementation.
The whole pipeline design is highly parallel, allowing for a real-time GPU-based implementation.
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