Task scheduling system of on-chip multi-core computing platform and method for task parallelization

Abstract

The invention discloses a task scheduling system of an on-chip multi-core computing platform and a method for task parallelization, wherein the system comprises user service modules for providing tasks which are needed to be executed, and computation service modules for executing a plurality of tasks on the on-chip multi-core computing platform, and the system is characterized in that core scheduling service modules are arranged between the user service modules and the computation service modules, the core scheduling service modules receive task requests of the user service modules as input, judge the data dependency relations among different tasks through records, and schedule the task requests in parallel to different computation service modules for being executed. The system enhances platform throughput and system performance by performing correlation monitoring and automatic parallelization on the tasks during running.

Description

technical field [0001] The invention belongs to the technical field of scheduling of multi-core computing platforms on a chip, and in particular relates to a task scheduling system of a multi-core computing platform on a chip and a task parallelization method. Background technique [0002] As the complexity of very large scale integration (VLSI) increases rapidly according to Moore's law, the performance improvement of a single processor has reached the limit, and multi-core processors have become the inevitable direction of the development of microprocessor architecture. Especially for single-chip heterogeneous multi-core systems, it integrates heterogeneous processing units such as general-purpose processors, DSPs, ASIPs, and even mixed-signal circuits on the same chip, giving full play to the respective advantages of heterogeneous processing units, and can meet the needs of embedded systems. The requirements for real-time performance and power consumption have become a re...

AI Technical Summary

Problems solved by technology

Among them, OpenMP provides a general thread-level programming model, which mainly relies on the mechanism of mutex to realize the synchronization between tasks between threads, but because the mutex is controlled by the programmer, it cannot realize the automatic parallelization of tasks implement

Other programming models such as MPI also require programmers to manually divide tasks, and even need to explicitly schedule tasks in parallel, so that the acceleration effect and performance improvement that tasks can achieve are greatly restricted by the programmers themselves.

[0005] In general, the task division and scheduling method in the task parallel scheduling method in the current parallel programming model requires manual intervention and configuration by the programmer, which limits the performance optimization effect that the platform can obtain

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