Dynamic Data Compression for GPU Bandwidth Optimization
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Solution Overview
Problem
Existing systems that utilize different compression schemes for data, such as delta color compression (DCC) and planar compression, face challenges in efficiently transferring data between system levels, particularly in graphics processing units (GPUs), which can result in significant bandwidth usage.
Innovation Solution
A method is introduced for integrating data compression in a computer system by receiving data at a first system level and determining whether to compress it based on the number of planes exceeding a threshold. If the threshold is met, the data is compressed using a first scheme (e.g., planar compression) and transferred to a second system level. If not, the data is transferred uncompressed. Subsequently, if the data is compressed at the second system level, it is further compressed using a second scheme (e.g., DCC) before being transferred to a third system level.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If data is compressed using planar compression at the first system level, then bandwidth usage is reduced, but device complexity increases due to multiple compression schemes
Solution Approach 1:
The system dynamically selects compression schemes based on the number of planes in the incoming data. When data has more planes than a threshold, planar compression is applied; otherwise, delta color compression is used. This dynamic adaptation allows the system to optimize bandwidth usage while managing complexity through conditional logic rather than always employing the most complex scheme.
Solution Approach 2:
The compression process is segmented into two distinct paths: one for planar compression when the plane count exceeds the threshold, and another for delta color compression when it does not. This segmentation allows the system to handle different data types with appropriate compression methods, reducing overall bandwidth usage while avoiding unnecessary complexity for data types that don't benefit from planar compression.
2Device complexity
If data is transferred uncompressed between system levels, then device complexity is reduced, but bandwidth usage increases
Solution Approach 1:
The system dynamically determines whether to compress data based on the number of planes, transferring data uncompressed only when the plane count is below the threshold. This dynamic approach ensures that compression is applied selectively to data types that benefit from it, reducing bandwidth usage without unnecessarily increasing device complexity for all data transfers.
3Loss of energy
If multiple compression schemes are integrated in the system, then bandwidth usage is optimized, but device complexity increases
Solution Approach 1:
The system dynamically selects between planar compression and delta color compression based on the number of planes in the incoming data. This dynamic selection mechanism optimizes bandwidth usage by applying the appropriate compression scheme for each data type while avoiding the complexity of implementing both schemes simultaneously for all data paths.
Solution Approach 2:
The data processing path is segmented into two branches: one handling data with more planes than the threshold using planar compression, and another handling data with fewer planes using delta color compression. This segmentation allows the system to integrate multiple compression schemes effectively, optimizing bandwidth for each data type while managing overall system complexity through structured decision-making.
Data Source
AI summary
A method and apparatus for integrating data compression in a computer system includes receiving first data at a first system level. Based upon a number of planes of the first data being less than or equal to a threshold, the data is compressed with a first data compression scheme, and transferred to a second system level for processing. Based upon the number of planes of the first data exceeding the threshold, the first data is transferred uncompressed to the second system level for processing. Based upon the received data at the second system level being compressed with the first compression scheme, the data is transferred to a third system level, and based upon the received data at the second system level being uncompressed with the first compression scheme, compressing the data with a second compression scheme, and transferring the compressed data to the third system level.