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A Speculative Multithreading Partitioning Method Based on Machine Learning

A machine learning and multi-threading technology, applied in the computer field, can solve the problem of not guaranteeing the optimal division of irregular programs, and achieve the effect of good adaptability

Inactive Publication Date: 2018-10-30
XI AN JIAOTONG UNIV
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Problems solved by technology

Its limitation is that different programs usually have different structural characteristics, and the method based on heuristic rules optimizes all programs with a single optimization scheme, so it cannot guarantee that all non-rule programs can obtain the optimal partition

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  • A Speculative Multithreading Partitioning Method Based on Machine Learning
  • A Speculative Multithreading Partitioning Method Based on Machine Learning
  • A Speculative Multithreading Partitioning Method Based on Machine Learning

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Embodiment Construction

[0032] The present invention will be described in further detail below. Said is by way of explanation rather than limitation.

[0033] The present invention provides a speculative multi-thread partitioning method based on machine learning. The method first extracts program features from irregular programs and annotates them into the program's Control Flow Graph (CFG). The annotated CFG graph is combined with the key path to represent the program features; secondly, the SUIF compiler is used to construct the control flow graph of the program, and the program analysis information and structural analysis method are used to convert it into a weighted control flow graph (Weighted ControlFlow Graph, WCFG) and the super block control flow graph (Super Control Flow Graph, SCFG), so that the program set is divided into different threads for the cyclic part and the non-cyclic part to obtain a training sample set composed of program features and optimal partition schemes; finally, throug...

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Abstract

The invention discloses a speculative multithreading division method based on machine learning. The speculative multithreading division method comprises the following steps: extracting program characteristics from an irregular program set, and combining a CFG (Control Flow Graph) with comments with a key path to show the program characteristics; then, constructing a program CFG by a SUIF compiler, converting the program CFG into a weighted CFG and a super block CFG, carrying out threading division, which aims at a cyclic part and an acyclic part, on the program set to obtain a training sample set formed by the program characteristics and an optimal division scheme; and finally, extracting the characteristics of an irregular program to be divided, calculating similarity between the characteristics of the irregular program to be divided and the program characteristics in the training samples, and carrying out weighted calculation on the division threshold values of a plurality of most similar sample programs to obtain an optimal division scheme suitable for the irregular program. The similarity between the program to be divided and the sample program is compared on the basis of the program characteristics, a similar sample division scheme is applied to the program to be divided, and therefore, the speculative multithreading division method exhibits better adaptability on each class of parallel irregular programs.

Description

technical field [0001] The invention belongs to the technical field of computers and relates to a speculative multithreading technology, in particular to a machine learning-based speculative multithreading division method. Background technique [0002] As instruction-level parallelism encounters more and more bottlenecks and the rapid development of on-chip multiprocessors, how to use core resources more effectively has become a current research hotspot. It is speculated that multi-threading, as a thread-level parallel technology, has developed rapidly. Especially for non-regular programs that use pointer-based data structures such as graphs and trees, there are a large number of fuzzy data dependencies that can only be determined at execution time, and thread-level speculative parallelism allows control and data dependencies. In this case, the non-regular serial program is decomposed into multiple thread units by the parallel compiler, which are respectively assigned to the...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F9/48G06F9/50
CPCG06F9/4843G06F9/5061G06F2209/5018
Inventor 赵银亮吉烁李玉祥侍加强刘延昭吕挫挫
Owner XI AN JIAOTONG UNIV
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