GPU Matrix-Fused Min-Add Instructions for Lower-Latency Parallel Compute
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Solution Overview
Problem
Conventional matrix operations on GPUs require significant computational resources and time due to the two-step process of addition and multiplication, leading to inefficient use of processing resources and power consumption.
Innovation Solution
The implementation of matrix-fused min-add (MFMA) instructions that replace the multiplication step with a minimum operation and the addition step with an addition operation, allowing for simultaneous execution of min-plus and plus-min functions across multiple logic units of a processing device.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional two-step matrix operations (addition and multiplication) are used on GPUs, then computational accuracy is maintained, but processing time and power consumption increase significantly
Solution Approach 1:
The patent merges the multiplication operation with the addition operation into a single fused min-add instruction. Instead of executing multiplication and addition as separate steps, the fused instruction performs both operations simultaneously, where the minimum operation replaces multiplication and addition replaces addition, reducing the operation count and execution time while maintaining computational accuracy for the specific应用场景 of shortest path algorithms
2Adaptability or versatility
If conventional two-step matrix operations are used, then comprehensive computational coverage is achieved, but device complexity and resource utilization increase
Solution Approach 1:
The patent changes the operational parameters by replacing the multiplication operation with a minimum operation in the matrix computation formula. This parameter substitution transforms the conventional GEMM (General Matrix Multiply) operation into a min-add operation, which is better suited for shortest path algorithms and reduces the complexity of the computational workflow while maintaining adaptability for specific application domains
3Device complexity
If conventional matrix operations are executed sequentially, then resource allocation is simplified, but parallelism and productivity are reduced
Solution Approach 1:
The patent segments the matrix computation into independent min-add operations that can be executed in parallel across multiple logic units. Each element of the resulting matrix can be computed independently using the fused min-add instruction, enabling fine-grained parallelism and significantly improving computational throughput compared to sequential execution of conventional two-step operations
4Adaptability or versatility
If conventional two-step operations are performed, then operational flexibility is maintained, but energy efficiency and power consumption worsen
Solution Approach 1:
The patent combines multiple operational steps into a single fused min-add instruction, reducing the total number of operational cycles required. This merging eliminates the need for separate multiplication and addition steps, thereby reducing the energy consumed per matrix operation and improving overall power efficiency while maintaining the flexibility to handle various matrix dimensions and data types
Data Source
AI summary
Matrix-fused min-add (MFMA) instructions are described. The MFMA instructions cause a processing device to execute at least one of a min-plus function or a plus-min function. The MFMA instructions cause the processor device to execute min-plus and plus-min functions in response to a single instruction and without performing a multiplication operation as required by conventional systems. In accordance with the described techniques, a MFMA instruction causes multiple logic units (e.g., threads or wavefronts) of a processing device to execute a min-plus function, a plus-min function, or combinations thereof, as part of completing a computational task. To optimize system efficiency, the MFMA instruction causes the processing device to execute the min-plus function, the plus-min function, or combinations thereof using data stored in local registers of the processor device.