View source for 17955613. Quality-of-Service Partition Configuration simplified abstract (ATI TECHNOLOGIES ULC)

=Quality-of-Service Partition Configuration=


==Organization Name==

[[:Category:ATI TECHNOLOGIES ULC|ATI TECHNOLOGIES ULC]]


[[Category:ATI TECHNOLOGIES ULC]]

==Inventor(s)==

[[:Category:Tung Chuen Kwong of Richmond Hill (CA)|Tung Chuen Kwong of Richmond Hill (CA)]][[Category:Tung Chuen Kwong of Richmond Hill (CA)]]

[[:Category:King Chiu Tam of Vaughan (CA)|King Chiu Tam of Vaughan (CA)]][[Category:King Chiu Tam of Vaughan (CA)]]

[[:Category:Akila Subramaniam of Allen TX (US)|Akila Subramaniam of Allen TX (US)]][[Category:Akila Subramaniam of Allen TX (US)]]

==Quality-of-Service Partition Configuration - A simplified explanation of the abstract==


This abstract first appeared for US patent application 17955613 titled 'Quality-of-Service Partition Configuration

==Simplified Explanation==

The abstract describes a scheduler for an apparatus that allows applications to specify quality-of-service parameters, such as latency and throughput, for processing workloads using a hardware compute unit. The scheduler configures a partition within the hardware compute unit based on the specified QoS parameters to ensure that processing resources meet the desired quality-of-service.

* The scheduler exposes an API for applications to specify quality-of-service parameters.
* Applications can define QoS parameters like latency and throughput for processing workloads.
* The scheduler configures a partition within the hardware compute unit based on the specified QoS parameters.
* The partition ensures that processing resources meet the specified quality-of-service requirements.

== Potential Applications ==
This technology can be applied in various fields such as cloud computing, edge computing, and real-time data processing where meeting specific quality-of-service requirements is crucial.

== Problems Solved ==
1. Ensuring that workloads are processed with the desired quality-of-service parameters.
2. Optimizing resource allocation within hardware compute units based on specified QoS requirements.

== Benefits ==
1. Improved performance and efficiency in processing workloads.
2. Enhanced user experience by meeting quality-of-service expectations.
3. Flexibility for applications to tailor QoS parameters based on their specific requirements.

== Potential Commercial Applications ==
Optimizing resource allocation in cloud computing environments, improving real-time data processing in edge devices, and enhancing performance in high-demand applications are potential commercial applications of this technology.

== Possible Prior Art ==
Prior art may include existing scheduling algorithms and techniques used in operating systems and cloud computing platforms to manage resource allocation and prioritize tasks based on quality-of-service requirements.

== Unanswered Questions ==

=== How does the scheduler handle dynamic changes in workload requirements? ===
The abstract does not specify how the scheduler adapts to changes in quality-of-service parameters during runtime.

=== What mechanisms are in place to prevent resource contention within the hardware compute unit? ===
The abstract does not detail how the scheduler manages resource allocation to avoid conflicts and ensure efficient processing of workloads.


==Original Abstract Submitted==

A scheduler of an apparatus exposes an application programming interface (API) usable to specify quality-of-service (QoS) parameters, e.g., latency, throughput, and so forth. An application, for instance, specifies the QoS parameters for a workload to be processed using a hardware compute unit. The QoS parameters are employed by the scheduler as a basis to configure a partition within a hardware compute unit. The partition is configured such that processing resources that are available via the partition to process the workload comply with the specified quality-of-service.



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