Method for executing tasks parallelly on heterogeneous multiprocessor

Abstract

The invention provides a method for executing tasks parallelly on a heterogeneous multiprocessor, specifically a method for executing a plurality of executable programs parallelly on one or a plurality of physical computing device CPUs or GPUs. The dependency among the executable programs decides which one of the executable programs is chosen by the numerous threads running in a plurality of physical computing devices simultaneously to call and execute. If one thread is initialized by certain GPU in the physical computing device, but the GPU is busy in processing other image processing threads, then the thread can be initialized by another thread in the physical computing device. In order to execute a plurality of executable programs parallelly on the a plurality of physical computing devices, source codes and existing executable programs as API functions are all stored in an API library. The executed executable programs can be the existing executable programs as well as executable programs which are compiled on line from the source codes.

Description

technical field [0001] The invention relates to a computer application technology, in particular to a method for parallel execution of the same task on heterogeneous multiprocessors. Background technique [0002] As GPUs are gradually incorporated into high-performance parallel computing devices, GPUs are used as general-purpose computing devices, and more and more applications are developed to complete data parallel computing. Now we can use the professional interface provided by the GPU supplier and the professional GPU device to design these applications, so that the CPU and the GPU can be used together in the data processing system, and the CPU will not be overloaded. How to make applications run better on GPUs from different vendors. [0003] At present, more and more CPUs are embedded in multi-core to complete data parallel computing, and more and more data processing tasks can be completed by CPUs and GPUs together. However, there is still no good way to make better...

AI Technical Summary

Problems solved by technology

However, there is still no good way to make better use of CPU and GPU at the same time. Therefore, a new data processing system is needed to overcome the above difficulties, allowing applications to use multiple CPU combinations or GPU combinations or multiple CPU combinations to complete tasks. A combination of a CPU and multiple GPUs for large-scale data processing

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