A division method of the parallel GPDT algorithm on a multi-core SOC

A parallel computing and multi-core technology, applied in computing, computer components, CAD circuit design, etc., can solve problems such as large amount of calculation

Active Publication Date: 2017-02-15
FUDAN UNIV
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Problems solved by technology

[0043] The advantage of the GPDT algorithm is that the number of working set elements solved by each iteration can reach 10. 3 The order of magnitude enables the algorithm to converge quickly, but due to the large number of matrix operations in a single iteration, the amount of calculation is very large

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  • A division method of the parallel GPDT algorithm on a multi-core SOC
  • A division method of the parallel GPDT algorithm on a multi-core SOC
  • A division method of the parallel GPDT algorithm on a multi-core SOC

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[0067] Below in conjunction with accompanying drawing, the present invention will be further described.

[0068] Such as figure 1 As shown, the present invention calculates the initial gradient in the algorithm middle The inner loop calculates the matrix z (k’) , the outer loop calculates the gradient increment The process is parallelized and distributed to multiple processors, which will greatly reduce the time of matrix operations in each iteration process. In addition, other parts of the algorithm are still serialized operations, including gradient projection and working set. update etc. According to Amdahl's law, the speedup ratio of the parallelized algorithm is not only related to the speedup ratio of the parallelizable part, but also related to the proportion of the parallelizable part. Therefore, as the training data increases, the proportion of computing time of the parallelizable part increases. , the overall speedup of the algorithm will gradually approach t...

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Abstract

The invention relates to the technical field of integrated circuit design and in particular provides a division method of the parallel GPDT algorithm on a multi-core SOC. The parallel GPDT algorithm includes two layers of iteration, wherein the inner-layer iteration is responsible for solving a working set and the outer-layer iteration is responsible for updating the working set. As for critical paths of computing speed, the critical path of outer-layer circulation is gradient update and the critical path of inner-layer circulation is vector computing following each projection, wherein both the matrix operations need multi-core parallel processing; the other operations can only be carried out in a serial manner on a main core, including a gradient projection operation realized by using the Dai-Fletcher algorithm and the working set update realized by introducing in the quick sort algorithm. Vectors obtained after the computing is over are support vectors of training data of the GPDT algorithm.

Description

technical field [0001] The invention belongs to the technical field of integrated circuit design, and specifically relates to a method for dividing a parallel GPDT algorithm on a multi-core SoC. Background technique [0002] The GPDT algorithm is a decomposition method for the original QP problem proposed by Zanni et al. The number of working set variables for each iteration is 10 2 to 10 3 Between the order of magnitude, the algorithm can reach convergence after a few iterations. Although the calculation amount of each iteration is relatively large, complex calculations can be distributed to multiple processors through parallelization, so that Get faster training speed. [0003] The original formulation of the SVM problem is: [0004] [0005] [0006] G is an l×l matrix, called the kernel matrix, where, is the kernel function. [0007] The decomposition of the problem is to divide the vector to be solved It is divided into two parts, one part is the working...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/50G06K9/62
CPCG06F30/39G06F18/2411
Inventor 韩军轩四中袁腾跃曾晓洋
Owner FUDAN UNIV
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