Adaptive Parallel Processing for Flow Cytometry Data Analysis
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Solution Overview
Problem
In parallel computing architectures, existing methods fail to effectively partition tasks between processors, leading to underutilization of resources as some processors remain idle while others handle more work, particularly in data analysis applications where efficient use of resources is crucial.
Innovation Solution
An adaptive semi-synchronous parallel processing system that identifies memory and relationship dependencies between tasks, representing them as a directed graph to optimize task assignment and execution, using threading models like parallel, strip-mine, and do not thread to maximize processor utilization and reduce processing time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If tasks are executed in parallel without intelligent partitioning, then computational speed may increase, but processor utilization becomes unbalanced with some processors idle while others are overloaded
Solution Approach 1:
The patent segments tasks into discrete units that can be independently assigned to different processors. By breaking down the computational workload into manageable task segments, the system can intelligently distribute these segments across multiple processors based on current processor availability and task dependencies, thereby balancing the load and improving overall processor utilization while maintaining parallel execution speed.
2Power
If more processors are added to the system, then computational capacity increases, but task management complexity increases
Solution Approach 1:
The patent implements a self-service task management system where processors automatically monitor their own workload and availability, and the system automatically assigns tasks based on predefined criteria and real-time processor status. This automated self-management approach allows the system to scale to more processors without proportionally increasing management complexity, as the system handles task distribution dynamically without requiring manual intervention for each processor addition.
3Ease of operation
If tasks are assigned based on simple load balancing, then implementation is straightforward, but processing time increases due to idle processors
Solution Approach 1:
The patent incorporates feedback mechanisms where the system continuously monitors processor performance, task completion status, and inter-task dependencies. This feedback information is used to dynamically adjust task assignments, ensuring that tasks are routed to processors that can execute them most efficiently. The feedback-driven approach maintains relative implementation simplicity while significantly reducing processing time by preventing idle processor periods and optimizing task distribution based on real-time system state.
Data Source
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AI summary
There is provided an adaptive semi- synchronous parallel processing system and method, which may be adapted to various data analysis applications such as flow cytometry systems. By identifying the relationship and memory dependencies between tasks that are necessary to complete an analysis, it is possible to significantly reduce the analysis processing time by selectively executing tasks after careful assignment of tasks to one or more processor queues, where the queue assignment is based on an optimal execution strategy. Further strategies are disclosed to address optimal processing once a task undergoes computation by a computational element in a multiprocessor system. Also disclosed is a technique to perform fluorescence compensation to correct spectral overlap between different detectors in a flow cytometry system due to emission characteristics of various fluorescent dyes.