Nvidia corporation (20240095083). PARALLEL WORKLOAD SCHEDULING BASED ON WORKLOAD DATA COHERENCE simplified abstract
Contents
- 1 PARALLEL WORKLOAD SCHEDULING BASED ON WORKLOAD DATA COHERENCE
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 PARALLEL WORKLOAD SCHEDULING BASED ON WORKLOAD DATA COHERENCE - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
PARALLEL WORKLOAD SCHEDULING BASED ON WORKLOAD DATA COHERENCE
Organization Name
Inventor(s)
Martin Stich of Memmingen (DE)
Rasmus Barringer of Helsingborg (SE)
PARALLEL WORKLOAD SCHEDULING BASED ON WORKLOAD DATA COHERENCE - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240095083 titled 'PARALLEL WORKLOAD SCHEDULING BASED ON WORKLOAD DATA COHERENCE
Simplified Explanation
The patent application provides approaches for addressing issues associated with processing workloads that exhibit high divergence in execution and data access. Here is a simplified explanation of the abstract:
- Identification of a plurality of workload items to be processed partially in parallel.
- Determination of coherence information associated with the workload items.
- Enqueuing the workload items in a segmented queue.
- Sorting the workload items based on the similarity of coherence information.
- Storing the sorted workload items in the queue.
- Processing the workload items in parallel using a set of processing units according to the sorting order.
Potential Applications
This technology could be applied in high-performance computing, data processing systems, and distributed computing environments.
Problems Solved
This technology addresses issues related to processing workloads with high divergence in execution and data access, improving efficiency and performance.
Benefits
The benefits of this technology include increased processing speed, optimized resource utilization, and enhanced scalability for handling diverse workloads.
Potential Commercial Applications
The potential commercial applications of this technology could be in cloud computing services, big data analytics platforms, and scientific research computing.
Possible Prior Art
One possible prior art could be the use of task scheduling algorithms in parallel computing systems to optimize workload processing efficiency.
Unanswered Questions
How does this technology compare to existing workload processing methods in terms of scalability and performance?
This article does not provide a direct comparison with existing methods in terms of scalability and performance.
What specific industries or sectors could benefit the most from implementing this technology?
The article does not specify which industries or sectors could benefit the most from implementing this technology.
Original Abstract Submitted
approaches for addressing issues associated with processing workloads that exhibit high divergence in execution and data access are provided. a plurality of workload items to be processed at least partially in parallel may be identified. coherence information associated with the plurality of workload items may be determined. the plurality of workload items may be enqueued in a segmented queue. the plurality of workload items may be sorted based at least on a similarity of the coherence information. the sorted plurality of workload items may be stored to the queue. using a set of processing units, the workload items in the queue may be processed at least partially in parallel according to an order of the sorting.
