GPU Hardware Compression for Sparse Matrix Throughput
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Solution Overview
Problem
Existing technologies face challenges in efficiently processing sparse matrices, leading to increased memory usage and processing overhead, which can limit computational throughput.
Innovation Solution
The implementation of a hardware-level compressor for sparse data within the graphics processing unit (GPU), allowing for the expansion of data contents only when necessary to achieve higher computational throughput.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If sparse matrix data is stored in uncompressed format, then memory bandwidth and processing throughput are maximized, but memory usage increases significantly
Solution Approach 1:
The patent applies parameter changes by dynamically adjusting the compression level based on the sparsity of the matrix data. When data sparsity exceeds a threshold, the system switches from uncompressed to compressed storage format, thereby reducing memory usage while maintaining computational throughput for sparse operations.
Solution Approach 2:
The system implements dynamic format switching between compressed and uncompressed representations based on real-time analysis of data characteristics. This allows the memory system to adapt its storage strategy to the actual sparsity pattern of the matrix, optimizing both memory efficiency and processing performance.
2Quantity of substance
If hardware compression is implemented for sparse data, then memory bandwidth requirements are reduced, but device complexity increases
Solution Approach 1:
The patent introduces a compression intermediary layer between the memory subsystem and the processing units. This intermediary handles the compression and decompression of sparse matrix data, allowing the rest of the system to operate with standard memory interfaces while benefiting from reduced bandwidth requirements through compression.
Solution Approach 2:
The compression functionality is segmented into separate hardware modules that can be selectively activated based on the sparsity of the data being processed. This modular approach allows the system to add compression capability without requiring a complete redesign of the memory subsystem, thereby limiting the increase in overall device complexity.
Data Source
AI summary
One embodiment provides a graphics processor comprising a base die including a plurality of chiplet sockets and a plurality of chiplets coupled with the plurality of chiplet sockets. At least one of the plurality of chiplets include a graphics core cluster including a plurality of processing elements, a shared local memory coupled with the plurality of processing elements, a plurality of matrix engines coupled with the shared local memory, and codec circuitry coupled with the shared local memory and the plurality of matrix engines. The codec circuitry is configured to decode matrix data stored in the shared local memory in a first format into a second format for consumption by the plurality of matrix engines.