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In computing, a compute kernel is a routine compiled for high throughput accelerators (such as graphics processing units (GPUs), digital signal processors (DSPs) or field-programmable gate arrays (FPGAs)), separate from but used by a main program (typically running on a central processing unit).
At the end, the form of the kernel is examined, and if it matches a known distribution, the normalization factor can be reinstated. Otherwise, it may be unnecessary (for example, if the distribution only needs to be sampled from). For many distributions, the kernel can be written in closed form, but not the normalization constant.
In linear algebra, linear transformations can be represented by matrices.If is a linear transformation mapping to and is a column vector with entries, then there exists an matrix , called the transformation matrix of , [1] such that: = Note that has rows and columns, whereas the transformation is from to .
For example, every integral transform is a linear operator, since the integral is a linear operator, and in fact if the kernel is allowed to be a generalized function then all linear operators are integral transforms (a properly formulated version of this statement is the Schwartz kernel theorem).
The kernel architecture's main benefit is that an application can be developed independently from a particular system configuration. This allows new modules to be added or replaced in the same application, so that specific scheduling policies can be evaluated for predictability, overhead and performance.
Kernel average smoother example. The idea of the kernel average smoother is the following. For each data point X 0, choose a constant distance size λ (kernel radius, or window width for p = 1 dimension), and compute a weighted average for all data points that are closer than to X 0 (the closer to X 0 points get higher weights).
Then an edge of the alpha-shape is drawn between two members of the finite point set whenever there exists a generalized disk of radius 1/α that has the two points on its boundary and that contains none of the point set in its interior. If α = 0, then the alpha-shape associated with the finite point set is its ordinary convex hull.
Autodesk ShapeManager is a 3D geometric modeling kernel used by Autodesk Inventor and other Autodesk products that is developed inside the company. It was originally forked from ACIS 7.0 in November 2001, [1] and the first version became available in Inventor 5.3 in February 2002.