Programmable Logic K-Mer Graph Generation for Faster Genomic Throughput
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Solution Overview
Problem
Existing methods for generating K-mer graphs are computationally intensive and time-consuming, occupying valuable software processor resources and hindering efficient genomic data processing.
Innovation Solution
Utilizing a programmable logic device with non-pipelined hardware logic units and a control machine to generate K-mer graphs, where each unit performs specific operations, and the control machine manages workflow and updates graph description data to enable parallel processing and accelerate the generation process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If software-based K-mer graph generation is used, then flexibility and ease of implementation are maintained, but processing time is excessive and computational resources are heavily occupied
Solution Approach 1:
The K-mer graph generation process is divided into multiple independent hardware logic units, each responsible for specific operations such as K-mer extraction, graph node creation, and edge formation. This segmentation enables parallel processing of different aspects of graph generation simultaneously, dramatically reducing overall processing time while maintaining manageable complexity through modular design
Solution Approach 2:
A control machine acts as an intermediary between the hardware logic units and the output, coordinating their operations and managing data flow. This intermediary component simplifies the overall system architecture by providing a single point of control that orchestrates multiple parallel hardware units without requiring complex inter-unit communication protocols
2Productivity
If hardware logic units are used for parallel processing, then throughput increases and multiple graphs can be processed simultaneously, but device complexity and initial resource requirements increase
Solution Approach 1:
The hardware logic units are designed with universal functionality to process multiple types of K-mer graphs using the same operational framework. Each unit can handle different graph configurations and parameters without requiring dedicated hardware for each specific task, thereby increasing throughput while controlling complexity through reusable modular components
Solution Approach 2:
The system allows dynamic adjustment of operational parameters such as K-mer size, graph depth, and processing batch size to optimize throughput for different workload requirements. By enabling parameter changes without hardware reconfiguration, the system achieves high productivity across diverse genomic analysis tasks while maintaining relatively simple fixed hardware architecture
Data Source
AI summary
Methods, systems, and apparatus for hardware-accelerated generation of a K-mer graph using a programmable logic device. In one aspect, a method includes actions of obtaining a first set of nucleic acid sequences, generating a K-mer graph using the obtained first set of nucleic acid sequences and using a plurality of non-pipelined hardware logic units of a programmable logic device, and periodically updating, with a control machine, graph description data for the K-mer graph after performance of the one or more operations by each hardware logic unit.