GPU Context Loading API for Lower-Latency Contextual Processing
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Solution Overview
Problem
Performing computational operations that utilize contextual information is time-consuming, power-intensive, and inefficient in terms of computing resources.
Innovation Solution
The implementation of a load and unload API for GPUs that allows for context-less loading and unloading of contextual information, enabling efficient allocation and management of GPU resources by automatically tracking and managing contexts without explicit user intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If contextual information is loaded into GPU contexts, then computational operations can be performed, but processing time and power consumption increase significantly
Solution Approach 1:
The system performs preliminary actions by pre-loading contextual information into a context-less GPU context before it is actually needed for computation. This allows the computational kernels to be compiled and prepared in advance, so when the actual computation is required, the context is already ready to use, significantly reducing the processing time during actual computational operations.
Solution Approach 2:
The patent introduces an intermediary context-less GPU context that acts as a mediator between the CPU and the actual GPU contexts. This intermediary holds the contextual information and manages the loading/unloading of contexts, allowing efficient resource management and reducing the time overhead associated with context switching and memory operations.
2Reliability
If contextual information is loaded into GPU contexts, then computational operations can be performed, but power consumption increases
Solution Approach 1:
The patent extracts the contextual information management from the traditional GPU context model and separates it into a context-less GPU context. This extraction allows the system to load only the necessary contextual information into GPU memory when needed and unload it when not needed, rather than maintaining all contextual information permanently in GPU memory, thus reducing power consumption while maintaining computational capability.
Solution Approach 2:
The system implements a mechanism to discard contextual information from GPU memory when it is no longer needed and recover it when needed again. The context-less GPU context allows for efficient loading and unloading of contextual information, enabling the system to discard unused contexts and recover them when required, optimizing power consumption by keeping GPU memory utilization dynamic rather than static.
3Ease of operation
If explicit user intervention is required to manage GPU contexts, then fine-grained control is achieved, but application code complexity increases
Solution Approach 1:
The patent implements self-service by enabling the context-less GPU context to automatically track and manage the loading and unloading of contextual information without requiring explicit user intervention. The system autonomously handles context management tasks such as determining when to load contexts into GPU memory and when to unload them, simplifying application code while maintaining fine-grained control over resource allocation.
Solution Approach 2:
The system incorporates feedback mechanisms where the context-less GPU context continuously monitors the state of GPU resources and computational needs, and automatically adjusts context loading and unloading decisions based on this feedback. This allows the system to maintain optimal performance without requiring complex user-managed context tracking logic in the application code.
Data Source
AI summary
Apparatuses, systems, and techniques to indicate contextual information to be used by available logical processors. In at least one embodiment, one or more circuits are to perform an application programming interface (API) to indicate a first set of contextual information to be used by a first subset of available processors.