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Various types of cloud computing patterns exist [8], covering workload, but also offer types and application and management architectures.
Researchers seek unified simulation frameworks that can model heterogeneous architectures like CPU and GPU devices and their interactions as computing patterns move toward heterogeneous era.
In this paper, we propose high-performance GPU implementations for a set of important irregular EDA computing patterns including sparse matrix, graph algorithms and message-passing algorithms.
These types of repeated computing patterns are a common feature of computing systems, even occurring in man-made computing circuits.
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With the cost proposed in "Formulation", all rational companies make the decision on the flat of switch to the cloud computing pattern to minimize its estimated discount cost with respect to the effort cost.
We masked repeats and coding exons to obtain the Masked Coding-exons and Repeats (MCR) set, which was used for computing pattern-discovery-based cross-species conservation.
An originality of our method is that matching information of already computed patterns is materialized as bitmaps, which greatly reduces both memory consumption and computation costs.
An originality of our method is that matching information of already computed patterns is materialized as bitmaps.
Next, we develop two theorems to rapidly compute patterns of nodes and prune nodes without the loss of information in colossal patterns.
Matching information of already computed patterns is materialized as bitmaps, a technique that not only minimizes the memory consumption, but also the CPU time.
The validity of studies which compute patterns of shear therefore depends on the anatomical fidelity of the reconstructed vascular geometry.
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