GPU Systolic Array Output Sparsity for Matrix Throughput
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Solution Overview
Problem
Existing graphics processors face inefficiencies in performing high-throughput matrix operations, particularly in applications like deep learning and general-purpose numerical computing, due to limitations in processing resources and functional units.
Innovation Solution
Implementing a systolic array architecture within graphics processing units (GPUs) that supports output sparsity, enabling efficient execution of matrix multiplication and dot product operations through structured output sparsity techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional fixed function computational units are used in graphics processors, then the processor can perform specific graphics operations reliably, but it cannot efficiently handle general-purpose numerical computing applications like deep learning and matrix operations
Solution Approach 1:
The computational units are divided into multiple processing elements arranged in a grid structure, where each element can independently perform computations. This segmentation enables parallel processing of matrix operations while maintaining flexibility for different computational patterns used in deep learning and general-purpose numerical computing
Solution Approach 2:
The computational units are designed with universal functionality to perform both traditional graphics operations and general-purpose numerical computing tasks. The same processing elements can be configured for different operations including matrix multiplication, dot products, and other computational kernels required by deep learning applications
2Productivity
If more processing resources and functional units are added to handle matrix operations, then computational capability improves, but device complexity increases
Solution Approach 1:
Multiple processing elements are merged into a unified computational unit that shares common resources such as data pathways, control logic, and memory interfaces. This merging approach enables high-throughput matrix operations while reducing the overall device complexity compared to having completely separate functional units for each processing element
Solution Approach 2:
Processing elements are arranged in a two-dimensional grid structure, adding spatial dimensionality to the architecture. This dimensional organization enables efficient data flow patterns for matrix operations without requiring a proportional increase in interconnect complexity, as adjacent elements in the grid can communicate through shared local pathways
3Productivity
If conventional processing architectures are used without output sparsity support, then all computational results are processed uniformly, but resource utilization efficiency decreases when output matrices contain many zero values
Solution Approach 1:
The system performs computations only for non-zero output elements by tracking sparsity patterns, rather than uniformly processing all elements in the output matrix. This partial action approach reduces computational overhead and improves resource utilization efficiency when the output matrix contains many zero values, as processing resources are directed only where needed
Solution Approach 2:
Different processing strategies are applied to different regions of the output matrix based on their sparsity characteristics. Elements identified as non-zero receive full computational processing, while zero elements are skipped or handled with simplified logic, creating local quality variations in the processing approach that optimize overall resource utilization
Data Source
AI summary
A processing apparatus is described herein that includes a general-purpose parallel processing engine comprising a matrix accelerator including one or more systolic arrays, at least one of the one or more systolic arrays comprising multiple pipeline stages, each pipeline stage of the multiple pipeline stages including multiple processing elements, the multiple processing elements configured to perform processing operations on input matrix elements based on output sparsity metadata. The output sparsity metadata indicates to the multiple processing elements to bypass multiplication for a first row of elements of a second matrix and multiply a second row of elements of the second matrix with a column of matrix elements of a first matrix.