Synchronous deployment system and method for multi-stream parallelism

Abstract

The present disclosure relates to a multi-stream parallel synchronous deployment system and method thereof. The system includes: an initial calculation graph acquisition component, which is used to acquire an initial calculation graph including multiple task flows belonging to a calculation task, each task flow includes a plurality of sequentially executed operation logic nodes, each operation logic node contains Task flow mark; node relationship analysis component, used to traverse all tensors to be targeted in the calculation graph, and determine the task flow to which the first node for the first tensor and the second node for the first tensor belong Whether it is the same; and the synchronous node deployment component is used to deploy the marker before the second node when the node relationship analysis component determines that the first node belongs to the first task flow and the second node belongs to a second task flow different from the first task flow The first synchronization dependent node of the first task flow marks the first synchronization dependent node with the second node, thereby determining that the operation of the first node is prior to the operation of the second node through the first synchronization dependent node.

Description

technical field [0001] The present disclosure relates to a data processing technique. More specifically, the present disclosure relates to a multi-stream parallel synchronous deployment system and method thereof. Background technique [0002] Now that deep learning is popular, in order to increase the speed of data processing, it is usually necessary to divide a task into multiple slice tasks to form multiple task flows, so that the task flows can be processed in parallel, thereby saving data processing time or speeding up tasks. Processing efficiency, this data processing method is multi-stream parallel processing. [0003] However, in the multi-stream parallel data processing method, there are often parameter exchanges between different streams and data dependencies between different streams. Therefore, if parameters are not synchronized in parallel, errors will occur when the results of each task flow are merged. This asynchrony is often caused by different task volume...

AI Technical Summary

Problems solved by technology

Due to two Kernels, S1-n1 and S2-n2, in two different task flows S1 and S2, the execution of S2-n2 may be earlier than S1-n1, in this case, S2-n2 reads data may be wrong

On the other hand, for Kernels that are logically undependent on multiple parallel task flows, it is possible that the tensor life cycle between different Kernels is uncontrollable, resulting in memory data errors

For example, three Kernels, such as S3-n3, S2-n2, and S1-n2, are in different task flows S3, S2, and S1 respectively. S3-n3 and S2-n2 need to read the tensor T2 in the memory M, In order to achieve synchronization, the conventional method will unconditionally instruct S3-n3 and S2-n2 to wait for S1-n2 or to synchronize with S1-n2. These synchronizations are adjusted manually, which is very labor-intensive

Moreover, when there are multiple parallel task flows that need to read the tensor T2 in the memory M, there will be multiple waiting or synchronization processes, resulting in redundant synchronization operations, resulting in complicated data processing

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