Double Accumulator Access for Vector Multiply Add in Graphics
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Solution Overview
Problem
Current graphics processing units (GPUs) face limitations in efficiently processing large matrices and performing deep learning operations due to the lack of specialized hardware for matrix multiplication and tensor operations, which hampers their performance in machine learning applications.
Innovation Solution
The integration of tensor cores and ray tracing cores within the GPU architecture, specifically designed for simultaneous matrix multiplication and deep learning operations, enables efficient processing of large matrices and neural networks, enhancing performance in machine learning tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional fixed function computational units are used in GPUs, then the architecture is simpler and more reliable, but the ability to perform specialized matrix multiplication and deep learning operations is limited
Solution Approach 1:
The GPU architecture is segmented into distinct specialized units: traditional fixed function computational units for general graphics processing, and new tensor cores for specialized matrix multiplication and deep learning operations. This segmentation allows each unit to be optimized for its specific function, improving overall adaptability without excessively complicating the entire system.
Solution Approach 2:
The tensor cores are designed as multi-functional units that can perform various deep learning operations including matrix multiplication, convolution, and activation functions. This universality allows a single specialized unit to handle multiple deep learning tasks, improving adaptability while controlling the increase in device complexity.
2Productivity
If specialized tensor cores are integrated into GPU architecture, then deep learning operation performance is improved, but the device complexity increases
Solution Approach 1:
The processing architecture is segmented into traditional fixed function units and specialized tensor cores, allowing deep learning operations to be handled by dedicated hardware rather than general-purpose units. This segmentation dramatically improves productivity for deep learning tasks while isolating the complexity increase to specific regions of the GPU.
Solution Approach 2:
Memory units serve as intermediaries between the tensor cores and external memory systems, buffering and managing data flow for matrix operations. This intermediary layer simplifies the interface between specialized computing units and the rest of the system, helping to manage device complexity while maintaining high productivity.
3Productivity
If single accumulator access is used in vector multiply add operations, then the hardware is simpler, but the processing throughput and performance are limited
Solution Approach 1:
The accumulator structure is segmented into multiple independent accumulators (first and second accumulators) that can be accessed simultaneously. This segmentation allows parallel accumulation of partial results during vector multiply add operations, doubling the effective throughput without requiring a complete redesign of the computational units.
Solution Approach 2:
The accumulator access structure transitions from a single-dimensional sequential access model to a multi-dimensional parallel access model. By organizing accumulators in a structure that allows simultaneous access from multiple computational units, the system achieves higher throughput while adding only moderate complexity to the memory access architecture.
Data Source
AI summary
An apparatus to facilitate supporting vector multiply add with double accumulator access in a graphics environment is disclosed. The apparatus includes a processor comprising processing resources, the processing resources comprising multiplier circuitry to: receive operands for a matrix multiplication operation, wherein the operands comprising two source matrices to be multiplied as part of the matrix multiplication operation; and issue a multiply and add vector (MADV) instruction for the multiplication operation utilizing a double accumulator access output, wherein the MADV instruction to multiply two vectors of the two source matrices in a single floating point (FP) pipeline of the processor.