Sparse Compute Kernel With Pattern Tracking for Matrix Multiply
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current parallel graphics processing systems face inefficiencies in handling sparse matrix operations due to the processing of zero-value operands and the lack of efficient mechanisms for identifying and utilizing sparse data patterns, leading to suboptimal performance in matrix multiplication and deep learning applications.
Innovation Solution
The implementation of a sparse computation mechanism that includes a scheduler to identify and prevent the processing of zero-value operands, a pattern tracker to detect sparse segments, and compression logic to store frequently used sparse matrices in a buffer for efficient execution, optimizing the processing of neural networks and deep learning operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional parallel graphics processing systems process all operands including zero-value operands in matrix multiplication, then complete computational coverage is achieved, but processing efficiency deteriorates due to unnecessary computations
Solution Approach 1:
The patent extracts and identifies zero-value operands from the input matrices using pattern tracking logic before processing. By detecting sparse segments and recording addresses of zero-value elements, the system removes unnecessary computational operations from the processing pipeline, allowing parallel processors to skip these elements and focus only on non-zero operands, thereby improving processing efficiency and reducing computational waste
Solution Approach 2:
The patent implements preliminary action by performing pattern tracking and zero-value identification before the actual matrix multiplication processing. The system pre-processes the input matrices to identify sparse segments and store addresses of zero-value operands in buffers, so that when the parallel processing stage occurs, the processors can immediately skip these pre-identified elements without performing unnecessary computations
2Productivity
If parallel processors execute all program instructions synchronously in SIMT architecture, then processing throughput is maximized, but performance deteriorates when handling sparse data with many zero-value operands
Solution Approach 1:
The patent applies dynamics by enabling processing threads to dynamically adjust their execution behavior based on detected sparse patterns. Instead of rigid synchronous execution, the system allows threads to diverge when encountering zero-value operands identified through pattern tracking, with some threads skipping computations while others continue, thereby adapting the execution flow to the actual data characteristics and reducing wasted processing time
Solution Approach 2:
The patent introduces an intermediary mechanism in the form of pattern tracking logic and buffer structures that mediate between the input matrices and the parallel processing units. This intermediary layer pre-processes the data to identify zero-value operands and provides this information to the processors, enabling them to optimize their execution without breaking the overall parallel architecture
3Speed
If sparse matrices are stored in standard memory format, then storage simplicity is maintained, but processing speed deteriorates due to lack of sparse data pattern utilization
Solution Approach 1:
The patent applies segmentation by dividing the sparse matrix storage into distinct components: the original matrix data and a separate pattern track buffer that stores addresses of zero-value operands. This segmentation allows the system to maintain the simplicity of standard memory storage while adding a complementary structure that enables fast identification of sparse elements, thereby improving processing speed without completely restructuring the storage format
Data Source
AI summary
Techniques to improve performance of matrix multiply operations are described in which a compute kernel can specify one or more element-wise operations to perform on output of the compute kernel before the output is transferred to higher levels of a processor memory hierarchy.