Context APIs for GPU Resource Allocation and Deferred Execution
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Solution Overview
Problem
Existing computer programs face challenges in optimizing memory and resource usage, leading to inefficiencies in performance.
Innovation Solution
Implementing application programming interfaces (APIs) to manage resources by creating, destroying, and subdividing contexts and sub-contexts, as well as obtaining and generating resource descriptors, which allows for more efficient allocation and management of resources on graphics processing units (GPUs).
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If computer programs use significant memory and resources to perform operations, then computational tasks can be completed, but memory usage and resource consumption increase
Solution Approach 1:
The patent segments computational contexts into hierarchical levels (primary contexts and sub-contexts). Each context represents a isolated computational environment with its own resource descriptors, allowing memory and resources to be divided into manageable segments that can be independently allocated and managed, reducing overall memory consumption while maintaining productivity
Solution Approach 2:
The patent implements local quality by allowing different contexts to have specialized resource descriptors tailored to their specific computational needs. Each context can optimize its resource allocation independently, with sub-contexts inheriting and customizing resource descriptors from parent contexts, enabling efficient local resource management without uniform memory consumption across all operations
2Quantity of substance
If resource allocation is optimized through context management, then memory efficiency improves, but system complexity increases
Solution Approach 1:
The patent employs nested doll by creating a hierarchical structure where sub-contexts are nested within primary contexts. Sub-contexts inherit resource descriptors from parent contexts and can create further nested sub-contexts, forming a tree-like hierarchy. This nesting approach manages complexity by organizing resource allocation in hierarchical layers rather than requiring flat, monolithic management
Solution Approach 2:
The patent applies preliminary action by pre-allocating and configuring resource descriptors at the primary context level before creating sub-contexts. Resource descriptors are prepared in advance with inherited configurations, allowing sub-contexts to inherit pre-configured resource allocations rather than requiring complex runtime negotiation, thereby reducing system complexity while maintaining memory efficiency
Data Source
AI summary
Apparatuses, systems, and techniques to perform computing operations. In at least one embodiment, a processor performs an application programming interface (API) to cause one or more second instructions to wait to be performed until the one or more second instructions receives context corresponding to one or more first software instructions.