Unified GPU Programming Interface for Graphics and Compute Integration
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Solution Overview
Problem
Existing programming interfaces for graphics and data-parallel tasks on GPUs require separate APIs, leading to performance degradation and inconvenience in combining graphics and computing tasks.
Innovation Solution
A unified programming interface and language that allows developers to write efficient multithreaded programs for both graphics and data-parallel compute processing on GPUs, using the same data structures and resources, and integrating graphics and computation tasks without the need for multiple frameworks or specific execution order encoding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If separate APIs are used for graphics and data-parallel tasks, then each task type can be programmed with dedicated interfaces, but performance degrades and combining tasks becomes inconvenient
Solution Approach 1:
The patent merges graphics and compute APIs into a single unified API that can handle both graphics data-parallel tasks and non-graphics data-parallel tasks. This consolidation eliminates the need for separate API contexts and allows seamless integration of different task types, thereby improving processing performance while maintaining versatility across task domains.
Solution Approach 2:
The unified API is designed to be universal, supporting multiple task types including graphics rendering and general-purpose computing through the same interface. This multi-functionality allows developers to use a single API for diverse workloads, eliminating performance penalties associated with context switching between separate APIs while preserving adaptability to different computational requirements.
2Adaptability or versatility
If separate APIs are used for graphics and data-parallel tasks, then each task type has specialized interfaces, but combining tasks becomes less convenient
Solution Approach 1:
By merging graphics and compute functionalities into a single unified API, the patent enables convenient combination of different task types within the same programming context. Developers can issue graphics and compute commands through the same interface without needing to switch between separate API contexts, significantly improving ease of operation for integrated workloads.
Solution Approach 2:
The unified API provides universal access to both graphics and compute operations, allowing developers to work with a single set of interfaces regardless of task type. This universality simplifies task integration by eliminating the complexity of managing multiple specialized APIs, while still providing the adaptability needed for diverse computational tasks.
3Adaptability or versatility
If different APIs are used for graphics and computing tasks, then each task type can be optimized independently, but the system requires multiple frameworks
Solution Approach 1:
The patent combines multiple separate API frameworks into a single unified framework that handles both graphics and compute operations. This consolidation reduces device complexity by eliminating the need for multiple framework implementations while maintaining the ability to optimize different task types through a common, streamlined interface.
Solution Approach 2:
The unified API framework provides universal support for both graphics and computing tasks, eliminating the need for multiple specialized frameworks. This single framework approach reduces system complexity while preserving the optimization capabilities needed for different task types through a unified code path and consistent interface design.
Data Source
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AI summary
Systems, computer readable media, and methods for a unified programming interface and language are disclosed. In one embodiment, the unified programming interface and language assists program developers write multi-threaded programs that can perform both graphics and data-parallel compute processing on GPUs. The same GPU programming language model can be used to describe both graphics shaders and compute kernels, and the same data structures and resources may be used for both graphics and compute operations. Developers can use multithreading efficiently to create and submit command buffers in parallel.