Pari-mutuel Pool Calculation Engine Parallel Processing
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Solution Overview
Problem
Current pari-mutuel pool calculation systems face challenges in efficiently handling large numbers of outcomes and wagers, leading to increased computation requirements and slower processing times, especially as events with numerous possible outcomes and participants grow in size.
Innovation Solution
The implementation of a system utilizing multiple parallel processing units, including graphics processing units (GPUs), which distribute and process wager data in a parallel manner, allowing for faster calculation of odds and payouts by dividing wagers into groups and processing them across multiple processors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If traditional sequential processing is used for pari-mutuel pool calculations, then system complexity remains manageable, but computation speed decreases and processing time increases
Solution Approach 1:
The patent divides the wager data into multiple groups and assigns each group to a separate parallel processing unit. The control processor segments the calculation tasks by creating distinct data groups that can be processed simultaneously, thereby increasing computation speed while managing system complexity through structured division of labor
Solution Approach 2:
The patent transitions from sequential one-dimensional processing to parallel multi-dimensional processing by utilizing multiple processing units that operate simultaneously. This dimensional change allows the system to handle larger datasets with more outcomes and wagers by distributing computation across multiple processors rather than sequentially
2Quantity of substance
If the number of outcomes and wagers increases, then the scope and accuracy of pari-mutuel pool calculations improve, but computation requirements increase and processing becomes slower
Solution Approach 1:
The system segments the large dataset of wagers into multiple manageable groups that can be distributed across parallel processing units. Each processing unit handles a specific group independently, allowing the system to scale processing capacity according to the quantity of data without proportionally increasing processing time
Solution Approach 2:
The parallel processing units are designed with universal functionality to handle different types of wager data and calculation requirements. Each processing unit can process various wager types and outcome combinations, making the system adaptable to increasing data quantities while maintaining consistent processing efficiency
3Productivity
If more parallel processing units are deployed, then computation speed and scalability improve, but data distribution and coordination complexity increase
Solution Approach 1:
The control processor serves as an intermediary that manages data distribution to parallel processing units and coordinates the aggregation of results. It receives wager data, divides it into appropriate groups, distributes them to processing units, and collects the computed results, thereby simplifying the complexity of direct peer-to-peer coordination between processing units
Solution Approach 2:
The control processor performs preliminary actions by pre-processing and organizing wager data into groups before distribution to parallel processing units. This preliminary organization of data ensures that each processing unit receives properly structured input, reducing the complexity of data handling during the parallel computation phase
Data Source
AI summary
The described technology relates to systems and techniques for improved utilization of a plurality of parallel processing units for processing a pari-mutuel pool. In one example, a control processor receives a plurality of wagers associated with an event associated with a pari-mutuel pool and a respective investment amount for each wager; divides the plurality of wagers to a plurality of groups, the number of groups in the plurality of groups being determined based on the number of parallel processing units in the plurality of parallel processing units; associates each group of wagers with a respective parallel processing unit of the plurality of parallel processing units; transmits each group of wagers and corresponding investment amounts to the respective parallel processing unit associated with said each group; and receives calculated odds data and/or payout amounts for each said group of wagers from the respective parallel processing units.