Super Workload Dispatch for Single Context Processor Utilization
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Solution Overview
Problem
Single context multicore processors are underutilized due to limited parallelism in certain applications like video encoding, leading to inefficiency and performance scalability issues, as they cannot fully utilize available execution units despite having more units than required for the task.
Innovation Solution
Creating a super workload by grouping independent workloads together and dispatching them as a single entity to a single context hardware system, allowing for efficient distribution across multiple execution units, thereby achieving multi-context processing without the need for hardware modifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single context multicore processor is used, then hardware complexity is reduced, but execution unit utilization is insufficient
Solution Approach 1:
The patent merges multiple independent workloads into a single assembled workload that is dispatched to a single context hardware processor. This combining approach allows the processor to handle multiple workloads sequentially within a single context, achieving high execution unit utilization without requiring complex multi-context hardware architecture.
2Productivity
If more execution units are added to the system, then performance scalability is improved, but hardware cost and complexity increase
Solution Approach 1:
The patent ensures continuous utilization of execution units by assembling multiple independent workloads into a single workload that keeps all execution units busy. This approach achieves performance scalability by maximizing the utilization of existing execution units rather than adding more hardware, thereby avoiding increased hardware cost and complexity.
3Productivity
If algorithm changes are made to increase parallelism, then execution unit utilization is improved, but result quality decreases
Solution Approach 1:
The patent segments the assembled workload into multiple independent workloads that can be processed sequentially. Each workload maintains its original algorithm integrity and spatial dependencies, ensuring result quality is not compromised while achieving high execution unit utilization through the assembly and dispatch mechanism.
Data Source
AI summary
Independent workloads may be grouped together into a single super workload. This super workload is dispatched to a single context hardware system that does not run an operating system. This effectively creates a multi-context system out of a single context hardware processor.


