Dynamic Hardware Scaling for High-Frequency Trading Latency
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Solution Overview
Problem
High frequency trading systems face challenges in efficiently processing large amounts of data and managing computational complexity, leading to variable throughput and latency that can delay order data generation.
Innovation Solution
A method and system for dynamically adjusting hardware performance by receiving context information, determining processing performance changes, and adjusting resources such as clock speed and voltage based on computational complexity and data traffic to optimize hardware resources for high frequency trading.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning models are used to analyze large amounts of market data for high frequency trading, then prediction accuracy is improved, but computational complexity increases and processing speed decreases
Solution Approach 1:
The system dynamically adjusts hardware processing performance based on real-time context information including computational complexity of machine learning models and traffic of market data. The processor monitors these parameters and adjusts clock speed and voltage supplied to hardware components, transforming a static system into a dynamic one that adapts to varying computational demands, thereby resolving the contradiction between using complex models and maintaining processing speed.
Solution Approach 2:
The invention changes physical parameters of hardware (clock speed and voltage) based on computational complexity and data traffic conditions. When computational complexity increases due to larger data amounts or more complex machine learning models, the system adjusts these parameters to maintain optimal processing performance, directly addressing the contradiction between model complexity and processing speed.
2Measurement precision
If the amount of data used for machine learning analysis is increased, then prediction accuracy is improved, but computational speed decreases
Solution Approach 1:
The system dynamically adjusts hardware resources based on the amount of data being processed. When larger datasets are fed into machine learning models for improved prediction accuracy, the processor detects increased computational load and adjusts clock speed and voltage accordingly to maintain computational speed, resolving the contradiction between data amount and processing velocity.
3Measurement precision
If computational complexity of machine learning models is increased, then analysis accuracy is improved, but throughput decreases
Solution Approach 1:
The system changes hardware operating parameters (clock speed and voltage) in response to computational complexity variations. When more complex machine learning models are deployed for better prediction accuracy, the processor adjusts these parameters to maintain throughput, directly resolving the contradiction between model complexity and system throughput.
4Productivity
If hardware processing performance is increased to handle large data amounts, then computational speed is improved, but energy consumption increases
Solution Approach 1:
The system dynamically adjusts energy consumption by varying clock speed and voltage based on actual computational needs. When large data amounts require higher processing performance, the system temporarily increases energy consumption but only to the extent necessary, then reduces it when demand decreases. This dynamic adaptation resolves the contradiction between maintaining high computational speed and minimizing energy consumption.
Data Source
AI summary
A method for adjusting a hardware performance for high frequency trading is provided, which is performed by one or more processors, and includes receiving context information including at least one of a traffic of market data for one or more target items or a computational complexity of using a machine learning model performed based on the market data, determining whether or not to change a processing performance of the hardware based on the received context information, and changing a resource supplied to the hardware based on the determination result for changing the processing performance.