Pipeline Data Processing for GPU Throughput
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
In data processing systems, the sequential execution of transfer and calculation processes leads to increased processing time, making it challenging to meet time constraints, especially when handling large amounts of data in real-time systems.
Innovation Solution
A data processing device and method that divides data into multiple streams and performs transfer and calculation processing in parallel using pipeline processing, allowing the GPU to execute these processes independently, reducing overall processing time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data transfer and calculation are executed sequentially in the GPU, then the processing flow is simple and easy to control, but the total processing time increases and cannot meet real-time constraints
Solution Approach 1:
The patent segments the data processing task into multiple independent processing lines (first processing line for transfer, second processing line for calculation). Each processing line handles specific operations independently, allowing parallel execution. The data series is divided into divided data that can be processed simultaneously through different lines, thereby reducing total processing time while maintaining manageable complexity through modular design.
Solution Approach 2:
The patent implements dynamic pipeline processing where the GPU switches between different processing lines based on data availability and processing stage. The control unit dynamically manages the execution flow, enabling the system to adaptively optimize processing speed by activating appropriate processing lines at appropriate times, thus improving productivity without requiring a permanently complex static structure.
2Quantity of substance
If a large amount of data is transferred to the GPU for processing, then comprehensive calculation can be performed, but the transfer time becomes large and exceeds constraint time
Solution Approach 1:
The patent divides the large data series into multiple divided data units that can be processed in parallel through different processing lines. This segmentation allows the system to handle large volumes of data simultaneously rather than sequentially, reducing the effective transfer time required while maintaining comprehensive data processing capability.
Solution Approach 2:
The patent performs preliminary data division and prepares multiple processing lines in advance before the actual data processing begins. The control unit pre-configures the processing pipeline structure, so when data arrives, it can be immediately routed through multiple lines simultaneously, minimizing idle transfer time and maximizing data processing throughput.
Data Source
AI summary
A data process device includes a data input unit and a processor. The processor includes a division unit, a first storage unit and a second storage unit which have a plurality of storage areas, a write unit, a calculation unit, and a control unit. The division unit divides a data series input by the data input unit to generate a plurality of divided data. The write unit writes the divided data to the first storage unit according to writing order to the storage areas in the first storage unit. The calculation unit performs calculation processing on the divided data written to the first storage unit, and writes calculated data obtained by the calculation processing to the second storage unit according to writing order to the storage areas in the second storage unit. The control unit controls processing of the write unit and processing of the calculation unit, which are divided into different processing lines, to be executed in parallel by pipeline processing.