Processor Query Function for Tensor Data Layout Adaptation
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Solution Overview
Problem
Current computing environments, particularly those using artificial intelligence accelerators for deep learning, are limited by data bandwidth, leading to inefficiencies in tensor computations due to inadequate data locality and re-use, which hampers the performance of machine learning applications.
Innovation Solution
A computer program product that includes a query function to obtain model-dependent data attributes such as supported data types, layouts, and sizes, allowing applications to adapt and optimize processing, thereby improving execution efficiency and reducing errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data locality and data re-use are employed at the accelerator, then data bandwidth limitations are addressed, but processing efficiency and performance of machine learning applications are hampered
Solution Approach 1:
The system performs preliminary actions by obtaining model-dependent information about the processor capabilities (data types, layouts, sizes) before executing machine learning applications. This advance preparation allows the application to be configured optimally for the specific hardware, preventing performance hampering while maintaining data bandwidth benefits.
Solution Approach 2:
The invention changes parameters by dynamically adapting application behavior based on queried processor parameters (supported data types, data layout formats, data size limitations). This parameter adaptation resolves the contradiction by optimizing processing efficiency for the specific accelerator model while utilizing its data bandwidth capabilities.
2Loss of time
If applications are prepared with model-dependent information, then execution time is saved and errors are reduced, but system complexity increases due to additional query functions
Solution Approach 1:
The system implements self-service by automatically querying the processor for model-dependent information and using this information to configure applications without requiring manual intervention. This automation reduces execution time while the query function encapsulates the complexity, preventing system complexity from becoming a burden.
Solution Approach 2:
The invention uses feedback by having the application query the processor capabilities and then adapt its behavior accordingly. This feedback loop enables the system to optimize execution time for each specific hardware configuration while managing complexity through structured information retrieval and adaptation.
3Speed
If data bandwidth is increased to improve tensor computation performance, then processing speed increases, but data locality and re-use become insufficient
Solution Approach 1:
The system applies dynamics by making data layout and access patterns adaptable based on the specific processor model's capabilities. Rather than using fixed data locality strategies, the application dynamically adjusts its data handling approach according to queried processor parameters, enabling both high processing speed and effective data locality utilization.
Solution Approach 2:
The invention implements local quality by optimizing data layout and access patterns specifically for each processor model's characteristics. Different processor models receive tailored data organization strategies based on their supported data types, layouts, and size limitations, achieving both speed and data locality effectiveness.
Data Source
AI summary
An instruction is executed to perform a query function. The executing includes obtaining information relating to a selected model of a processor. The information includes at least one model-dependent data attribute of the selected model of the processor. The information is placed in a selected location for use by at least one application in performing one or more functions.