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Indexable Type Conversion

A type and type of index technology, applied in the direction of instrumentation, calculation, electrical digital data processing, etc., can solve problems such as inefficiency

Active Publication Date: 2016-08-17
MICROSOFT TECH LICENSING LLC
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

As a result, general-purpose processors are much less efficient at executing data-parallel algorithms than data-parallel compute nodes

Method used

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  • Indexable Type Conversion
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Embodiment Construction

[0011] In the following detailed description, reference is made to the accompanying drawings, which form a part hereof, and in which are shown by way of example specific embodiments in which the invention may be practiced. In this regard, directional terms such as "top", "bottom", "front", "rear", "leading", "trailing", etc. are used with reference to the orientation of the figures being described. Because various components of various embodiments may be positioned in a number of different orientations, directional terms are used for purposes of illustration and not limitation. It is to be understood that other embodiments may be utilized and structural or logical changes may be made without departing from the scope of the present invention. Accordingly, the following detailed description is not intended to be limiting, and the scope of the invention is defined by the appended claims. It should be understood that the features of the various exemplary embodiments described her...

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Abstract

A high-level programming language provides an extensible set of transforms on indexable types for use in a data parallel processing environment. Compilers for the language implement each transformation as a map from an indexable type, allowing each transformation to be applied to other transformations. At compile time, the compiler identifies sequences of transformations on each indexable type in the data-parallel source code and generates data-parallel execution code to implement the sequences as compositional operations using transformation maps at runtime. The compiler also includes optimizations based on the sequence of transformations to the data-parallel execution code.

Description

Background technique [0001] A computer system typically includes one or more general-purpose processors, such as a central processing unit (CPU), and one or more dedicated data-parallel computing nodes, such as a graphics processing unit (GPU) or single instruction multiple data (SIMD ) execution unit). A general-purpose processor typically performs general-purpose processing on a computer system, and a data-parallel compute node typically performs data-parallel processing (eg, graphics processing) on ​​a computer system. General-purpose processors often have the capability to implement data-parallel algorithms, but do so without the optimized hardware resources found in data-parallel compute nodes. As a result, general-purpose processors are much less efficient at executing data-parallel algorithms than data-parallel compute nodes. [0002] Data-parallel computing nodes have traditionally played an auxiliary role for general-purpose processors in executing programs on compu...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F9/45
CPCG06F8/453
Inventor P·F·林塞斯W·朱R·莫洛伊C·D·卡拉罕二世Y·莱瓦诺尼L·张
Owner MICROSOFT TECH LICENSING LLC
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