Shader Engine Store Bandwidth Compression
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Solution Overview
Problem
Graphics processing units (GPUs) face memory bandwidth limitations due to the high number of threads executing simultaneously, leading to unprocessed read/write requests accumulating at the frame buffer interface, which substantially limits memory-related performance.
Innovation Solution
A method is introduced to compress data for thread group STORE operations by determining if thread group data is compressible and reducing it into fewer subpackets, allowing it to be written to the frame buffer memory in one clock cycle, thereby improving memory performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multiple threads execute simultaneously in a thread group, then processing throughput is improved, but memory bandwidth requirements increase excessively
Solution Approach 1:
The patent merges the memory operations of multiple threads within a thread group by compressing their individual scalar write operations into a single compressed memory transaction. The compression engine combines data from multiple threads and transmits it as one memory operation, reducing the total number of memory transactions while maintaining the processing throughput of multiple simultaneous threads.
Solution Approach 2:
The patent changes the data representation parameter by compressing scalar data from multiple threads into a reduced representation that can be transmitted in fewer memory transactions. The compression algorithm transforms the raw scalar data into a compressed format that maintains semantic information while reducing bandwidth consumption and transaction count.
2Measurement precision
If scalar data is written to frame buffer memory individually by each thread, then data accuracy is maintained, but memory bandwidth is insufficient
Solution Approach 1:
The compression engine merges individual scalar write operations from multiple threads into a single compressed memory transaction. It collects scalar data from all threads in the thread group and transmits it as one memory operation, maintaining data accuracy while significantly reducing memory bandwidth requirements and transaction overhead.
Solution Approach 2:
The patent creates a compressed representation copy of the scalar data that preserves the essential information needed for frame buffer rendering. The compression algorithm generates a reduced representation that can be decompressed or directly interpreted by the memory system, maintaining data fidelity while reducing bandwidth consumption.
3Reliability
If thread group data is transmitted uncompressed, then data integrity is preserved, but memory performance is limited
Solution Approach 1:
The patent changes the data transmission parameter by compressing thread group data before memory transmission. The compression engine transforms the data into a compact representation that maintains integrity through deterministic decompression, thereby improving memory performance (fewer transactions, lower latency) while preserving data integrity for correct frame buffer rendering.
Data Source
AI summary
Embodiments of the present invention set forth systems and methods for compressing thread group data written to frame buffer memory to increase overall memory performance. A compression/decompression engine within the frame buffer memory interface includes logic configured to identify situations where the threads of a thread group are writing similar scalar values to memory. Upon recognizing such a situation, the engine is configured to compress the scalar data into a form that allows all of the scalar data to be written to or read from the frame buffer memory in fewer clock cycles than would be required to transmit the data in uncompressed form to or from memory. Consequently, the disclosed systems and methods are able to effectively increase memory performance when executing thread group STORE and LOAD operations.