Sparse Matrix Scheduler for Skipping Zero-Value Operands
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Solution Overview
Problem
Current data processing systems, particularly graphics processors, face inefficiencies in handling sparse matrix operations due to the processing of zero-value operands and the need for dense matrix computations, which can lead to increased processing loads and resource utilization.
Innovation Solution
The implementation of a sparse matrix processing mechanism that includes a scheduler to identify and prevent the scheduling of zero-value operands, a pattern tracker to detect sparse segments, and compression logic to store frequently used sparse matrices in a compressed buffer, allowing for efficient execution and reducing unnecessary computations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional dense matrix computation is used to process sparse matrices, then the processor can maintain a simple uniform architecture, but it performs unnecessary computations on zero-value operands increasing processing load
Solution Approach 1:
The patent extracts and identifies zero-value operands from the matrix data before processing. The scheduler component specifically detects zero values and excludes them from computation scheduling, thereby removing unnecessary computational operations and reducing processing load while maintaining accurate results.
Solution Approach 2:
The patent implements dynamic scheduling that adapts to the actual data characteristics. The scheduler dynamically identifies zero-value operands and adjusts the computation plan in real-time, switching from static dense matrix computation to dynamic sparse-aware computation, thereby optimizing processing efficiency and reducing energy consumption.
2Productivity
If sparse matrix operations are optimized by identifying and skipping zero-value operands, then processing efficiency improves, but the device complexity increases due to additional scheduler logic
Solution Approach 1:
The scheduler performs self-service by automatically identifying zero-value operands and adjusting its own scheduling decisions without requiring external intervention or complex control logic. This self-aware scheduling mechanism reduces the need for additional complex control structures while maintaining processing efficiency.
3Ease of operation
If zero-value operands are processed in traditional dense computation, then the computation is straightforward and uniform, but it increases processing time and resource utilization
Solution Approach 1:
The system performs preliminary identification of zero-value operands before the actual computation begins. By pre-processing the data to mark or identify zero values, the scheduler can then skip these operands during execution, significantly reducing processing time while maintaining computational accuracy.
Data Source
AI summary
An apparatus to facilitate processing of a sparse matrix is disclosed. The apparatus includes a plurality of processing units each comprising one or more processing elements, including logic to read operands, a multiplication unit to multiply two or more operands and a scheduler to identify operands having a zero value and prevent scheduling of the operands having the zero value at the multiplication unit.