Multi-task runtime collaborative scheduling system under heterogeneous environment

Abstract

The invention discloses a multi-task runtime collaborative scheduling system under a heterogeneous environment. The system comprises a system task preprocessing module, a runtime dynamic task scheduling module and a system resource monitoring and managing module, wherein the system task preprocessing module is used for performing static analysis and marking on a code and generating a task code for performing collaborative scheduling by taking a thread as a unit; the system resource monitoring and managing module is used for monitoring, tidying and recording the use condition of system resources, and providing to the runtime dynamic task scheduling module for performing system runtime feature analysis after processing; the runtime dynamic task scheduling module is used for receiving and managing the task code generated by the system task preprocessing module as well as loading and executing the corresponding task according to the system runtime information received from the system resource monitoring and managing module. By implementing the multi-task runtime collaborative scheduling system, the existing program can be fast transferred to the multi-core-many-core heterogeneous environment under the condition of ensuring higher speed-up ratio, higher energy efficiency ratio and higher system resource utilization ratio.

Description

technical field [0001] The present invention relates to the field of computer multi-core-many-core heterogeneous processing system structure and cooperative task scheduling. More specifically, the present invention relates to a multi-task runtime cooperative scheduling system in a heterogeneous environment. - Rapid porting of many-core heterogeneous systems. Background technique [0002] Due to the limitations of energy consumption walls and memory walls, heterogeneous processing systems represented by CPU-GPU are gradually emerging. The main advantage of this heterogeneous system structure is to provide powerful computing capabilities while ensuring high energy efficiency. When transplanting programs in such a heterogeneous system, programmers redesign and implement programs suitable for the corresponding processing system according to related programming models and specifications, thereby improving the speedup ratio, energy efficiency ratio and resource utilization of the ...

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Problems solved by technology

[0003] However, the inherent heterogeneity of this architecture also brings various inconveniences to the transplantation of programs.

First of all, the platform heterogeneity brought about by the heterogeneity of hardware structure makes the existing programs unable to run directly in the new heterogeneous system. Traditional transplantation solutions are generally redesigned and implemented according to the relevant programming models and specifications, which are suitable for the corresponding processing system. This solution not only requires program transplanters to master new programming techniques and frameworks, but also requires them to have a deep understanding of the structure and characteristics of the transplanted program. The transplantation is difficult, inefficient, and ineffective; secondly, in the traditional In the transplantation scheme, when the load of the entire heterogeneous system and the load fluctuate greatly, the newly developed program cannot dynamically adapt to the load situation between the heterogeneous units at runtime and make effective adjustments accordingly, resulting in system operating efficiency and resource loss. The utilization rate is relatively low

Therefore, limited by the heterogeneity of the system and the static nature of the task scheduling strategy, the solutions for traditional program transplantation have various problems such as low efficiency, high cost, poor flexibility, and unsatisfactory results.

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