A data flow compilation optimization method for multi-core clusters

Abstract

The invention discloses a data flow compiling optimization method for a multi-core cluster system, comprising: determining calculation tasks and processing core mapping task division and scheduling steps; constructing pipelines between cluster nodes and cluster node cores according to the task division and scheduling results The hierarchical pipeline scheduling step of the scheduling table; the caching optimization step based on the cache is performed according to the structural characteristics of the multi-core processor, the communication situation between the cluster nodes and the execution situation of the data flow program on the multi-core processor. The method of the present invention combines the optimization technology related to the data flow program and the system structure, fully exerts the high load balance and the high parallelism of the synchronous and asynchronous mixed pipeline code on the multi-core cluster, and aims at the cache and communication mode on the multi-core cluster, The cache access and communication transmission of the program are optimized, which further improves the execution performance of the program and has a shorter execution time.

Description

technical field [0001] The invention belongs to the technical field of computer compilation, and more specifically, relates to a multi-core cluster-oriented data flow compilation optimization method. Background technique [0002] With the development of semiconductor technology, multi-core processors have been verified as a viable platform for exploiting parallelism. Multi-core cluster parallel system has become an important parallel computing platform design due to its powerful parallel computing capability and good scalability. The multi-core cluster system provides powerful computing and processing capabilities, but also puts more burdens on compilers and programmers to effectively develop coarse-grained parallelism between cores. Dataflow programming provides a viable approach to exploit the parallelism of multicore architectures. In this model, each node represents a computing task, and each edge represents the data flow between computing tasks. Each computing task i...

AI Technical Summary

Problems solved by technology

However, there are the following defects: (1) The calculation and communication scheduled to the processing core are separated, and an independent communication time is allocated for it in the pipeline, thus increasing the communication overhead; (2) The processing is not considered The underlying storage allocation optimization problem and communication optimization problem of the core; (3) The compilation optimization method is not optimized for the underlying architecture characteristics of the multi-core cluster system

The existing stream compilation optimization method does not take into account the underlying architecture, and does not make full use of system hardware resources such as storage resources to improve program execution efficiency

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