Parallel SSD Decoding for Low-Queue-Depth QoS
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Enterprise SSDs face strict quality of service (QoS) requirements, particularly in low queue depth scenarios, where existing resources are not fully utilized, leading to suboptimal latency and performance.
Innovation Solution
Implementing a method where data is decoded in parallel by multiple decoders operating with different parameters, with the decoded data from the first completed decoder being delivered to the host while discarding the results from other decoders, thereby maximizing the utilization of decoding capabilities without increasing latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data is decoded by a single decoder in low queue depth scenarios, then device complexity is reduced, but quality of service and latency performance deteriorate
Solution Approach 1:
The patent segments the decoding task by dividing it across multiple decoders (first decoder, second decoder, etc.) that operate simultaneously in parallel. Each decoder processes the same data independently, and the first decoder to complete decoding delivers the result. This segmentation allows the system to meet strict QoS requirements by distributing the decoding workload across multiple processing units.
Solution Approach 2:
The patent merges multiple decoding operations into a single coordinated process. Multiple decoders are activated simultaneously to decode the same data, and their results are merged through a selection mechanism that chooses the first successful decoding result. This merging of parallel decoding paths enables the system to achieve both low latency and high reliability.
2Speed
If multiple decoders operate in parallel to decode the same data, then decoding speed and QoS improve, but resource utilization complexity increases
Solution Approach 1:
The patent applies preliminary action by pre-configuring multiple decoders with different parameters (such as different error correction code types or decoding algorithms) before the decoding task begins. When data needs to be decoded, the system can immediately activate the appropriate decoders without configuration delays, enabling parallel processing to start instantly and improve decoding speed.
Solution Approach 2:
The patent utilizes parameter changes by configuring different decoders with varying operational parameters (such as different LDPC code rates, different decoding algorithms, or different power levels). This allows the system to explore multiple decoding approaches simultaneously, increasing the likelihood of finding a successful decoding solution quickly while managing complexity through parameter variation rather than structural complexity.
3Productivity
If existing decoding resources are fully utilized in low queue depth scenarios, then productivity improves, but device complexity increases
Solution Approach 1:
The patent implements self-service through an automatic decoder selection mechanism that autonomously determines which decoder results to use based on completion timing. The system automatically activates multiple decoders, monitors their completion status, and selects the first successful result without requiring complex external management. This self-service approach maximizes resource utilization while keeping the management logic relatively simple.
Solution Approach 2:
The patent ensures continuity of useful action by keeping multiple decoders actively processing data simultaneously rather than sequentially. In low queue depth scenarios, instead of leaving decoders idle or processing data sequentially, the system maintains continuous useful action by having multiple decoders work in parallel on the same data, maximizing productivity of existing resources.
Data Source
AI summary
The present disclosure generally relates to a method and device for simultaneously decoding data. Rather than sending data to be decoded to a single decoder, the data can be sent to multiple, available decoders so that the data can be decode in parallel. The data decoded from the first decoder that completes decoding of the data will be delivered to the host device. All remaining decoded data that was decoded in parallel will be discarded. The decoders operating simultaneously in parallel can operate using different parameters such as different calculation precision (power levels). By utilizing multiple decoders simultaneously in parallel, the full functionality of the data storage device's decoding capabilities are utilized without increasing latency.