Task Distribution Protocol for Parallel Read Alignment
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Solution Overview
Problem
Existing methods for distributing computational tasks across multiple machines are inefficient due to task inter-dependencies and varying resource utilization, leading to uneven resource usage and potential delays from conflicting results.
Innovation Solution
A computer-implemented method that aligns reads with a reference dataset, identifies sparse indicators, and distributes tasks based on predicted resource expenditure and time profiles, using a task-distribution protocol to balance machine loads and process reads in parallel, allowing for real-time and stream processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If computational tasks are distributed among multiple computing machines, then processing efficiency is improved, but resource utilization becomes uneven due to task inter-dependencies and varying resource requirements
Solution Approach 1:
The system performs preliminary actions by predicting resource expenditure and time profiles for each task before distribution. A task-distribution protocol uses these predictions to proactively balance the load across computing machines, preventing idle time and uneven resource usage before they occur.
Solution Approach 2:
The task distribution system is dynamic, continuously monitoring and adjusting task allocation based on varying resource requirements and machine availability. The protocol adapts to changing conditions in real-time, optimizing resource utilization as tasks complete and new tasks become available.
2Speed
If tasks with inter-dependencies are distributed in parallel, then processing speed is improved, but delays occur due to conflicting results requiring verification
Solution Approach 1:
The system implements feedback mechanisms to detect conflicting results from parallel task execution. When conflicts are identified, the system triggers verification tasks and automatically manages re-execution of affected tasks, ensuring data integrity while minimizing overall processing delays.
Solution Approach 2:
The system performs preliminary verification actions by predicting which tasks may produce conflicting results based on their dependencies. Verification tasks are prepared and queued in advance, allowing for faster conflict resolution when parallel execution produces contradictory outcomes.
3Ease of operation
If tasks are distributed without considering resource variation, then distribution simplicity is improved, but resource utilization efficiency deteriorates
Solution Approach 1:
The task-distribution protocol changes key parameters including predicted resource expenditure and time profiles for each task. These parameter adjustments enable intelligent task allocation that considers varying resource requirements of different task types and data characteristics, optimizing overall resource utilization efficiency.
Data Source
AI summary
Embodiments in the disclosure are directed to the use of distributed computing to align reads against multiple portions of a reference dataset. Aligned portions of the reference dataset that correspond with an above-threshold alignment score can be assessed for the presence of sparse indicators that can be categorized and used to influence a determination of a state transition likelihood. Various tasks associated with the processing of reads (e.g., alignment, sparse indicator detection, and/or determination of a state transition likelihood) may be able to take advantage of parallel processing and can be distributed among the machines while considering the resource utilization of those machines. Different load-balancing mechanisms can be employed in order to achieve even resource utilization across the machines, and in some cases may involve assessing various processing characteristics that reflect a predicted resource expenditure and/or time profile for each task to be processed by a machine.