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/prebuilts/go/darwin-x86/src/cmd/compile/internal/ssa/
sparsemap.go	9 // from http://research.swtch.com/sparse
sparseset.go	7 // from http://research.swtch.com/sparse
/prebuilts/go/linux-x86/src/cmd/compile/internal/ssa/
sparsemap.go	9 // from http://research.swtch.com/sparse
sparseset.go	7 // from http://research.swtch.com/sparse
/prebuilts/go/darwin-x86/src/regexp/
exec_test.go	330 // at http://www2.research.att.com/~astopen/testregex/testregex.html. 365 // http://www2.research.att.com/~astopen/man/man1/testregex.html
exec.go	13 // See http://research.swtch.com/2008/03/using-uninitialized-memory-for-fun-and.html
/prebuilts/go/linux-x86/src/regexp/
exec_test.go	330 // at http://www2.research.att.com/~astopen/testregex/testregex.html. 365 // http://www2.research.att.com/~astopen/man/man1/testregex.html
exec.go	13 // See http://research.swtch.com/2008/03/using-uninitialized-memory-for-fun-and.html
/external/tensorflow/tensorflow/contrib/verbs/
README.md	30 3. Following HKUST research on the use of GPU direct, and their [GDR implementation](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/gdr/README.md), there is a smart way to benefit from the TensorFlow allocation theme which is mostly pool based, i.e allocators pre-allocate a large memory block, and allocate the tensors from there. By attaching a custom Visitor to relevant allocators, we can do a single registration of the entire memory block, which zeros the registration overhead. Once the block is registered, each new tensor allocated will be at a registered address, which will allow us to do direct RDMA writes to it.
patch_notes_verbs_with_0_copies.md	5 Following HKUST research on the use of GPU direct, and their [GDR implementation](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/gdr/README.md), we wish to adopt the 0 copies approach and apply it to the current verbs implementation, while keeping the current implementation advantages, such as configurability and the use of RDMA for control messages.
/external/ImageMagick/www/api/
morphology.php	254 <dd> Peak:radius1,radius2 Find any peak larger than the pixels the fall between the two radii. The default ring of pixels is as per "Ring". Edges Find flat orthogonal edges of a binary shape Corners Find 90 degree corners of a binary shape Diagonals:type A special kernel to thin the 'outside' of diagonals LineEnds:type Find end points of lines (for pruning a skeletion) Two types of lines ends (default to both) can be searched for Type 0: All line ends Type 1: single kernel for 4-conneected line ends Type 2: single kernel for simple line ends LineJunctions Find three line junctions (within a skeletion) Type 0: all line junctions Type 1: Y Junction kernel Type 2: Diagonal T Junction kernel Type 3: Orthogonal T Junction kernel Type 4: Diagonal X Junction kernel Type 5: Orthogonal + Junction kernel Ridges:type Find single pixel ridges or thin lines Type 1: Fine single pixel thick lines and ridges Type 2: Find two pixel thick lines and ridges ConvexHull Octagonal Thickening Kernel, to generate convex hulls of 45 degrees Skeleton:type Traditional skeleton generating kernels. Type 1: Tradional Skeleton kernel (4 connected skeleton) Type 2: HIPR2 Skeleton kernel (8 connected skeleton) Type 3: Thinning skeleton based on a ressearch paper by Dan S. Bloomberg (Default Type) ThinSE:type A huge variety of Thinning Kernels designed to preserve conectivity. many other kernel sets use these kernels as source definitions. Type numbers are 41-49, 81-89, 481, and 482 which are based on the super and sub notations used in the source research paper. </dd>     [all...]
/prebuilts/go/darwin-x86/src/math/big/
float.go	1204 // http://www.vinc17.net/research/papers/rnc6.pdf     [all...]
/prebuilts/go/linux-x86/src/math/big/
float.go	1204 // http://www.vinc17.net/research/papers/rnc6.pdf     [all...]
/build/make/tools/droiddoc/templates-ndk/assets/js/
android_3p-bundle.js	[all...]
/external/doclava/res/assets/templates-sdk/assets/js/
android_3p-bundle.js	[all...]

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