Model Batch Sizing with Precomputed Workload Profiles
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Solution Overview
Problem
Neural networks and machine learning models lack information about optimal batch sizes for data processing, leading to unclear configurations and inefficient runtime performance due to variations in data amounts and computing environments.
Innovation Solution
A system that analyzes models to determine whether to process data as a single batch or multiple smaller batches, and optionally supplements the data with additional data to achieve desired performance metrics, such as reduced runtime, by evaluating various workload and batch sizes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multiple batch sizes are evaluated to find optimal configuration, then processing efficiency is improved, but configuration time increases significantly
Solution Approach 1:
The system performs preliminary evaluation of different batch sizes and workload configurations during a configuration phase, storing the results for later use. This preliminary action captures performance data before actual data processing begins, so that during runtime the system can directly apply pre-determined optimal settings without re-evaluating configurations.
Solution Approach 2:
The system creates a configuration profile that captures the performance characteristics of the neural network model across different batch sizes. This configuration profile acts as a copy or representation of the model's behavior patterns, allowing the system to reference pre-analyzed performance data instead of重新 conducting full evaluations during actual processing.
2Productivity
If comprehensive model analysis is performed to determine optimal batch sizes, then processing performance is improved, but system complexity increases
Solution Approach 1:
The system segments the configuration process into distinct phases: an initial evaluation phase where different batch sizes are tested and stored, and a runtime phase where pre-stored configuration data is applied. This segmentation separates the complex analysis work from the actual processing workflow, making the system more manageable and easier to implement.
Solution Approach 2:
The system introduces a configuration profile or performance database as an intermediary between the neural network model and the data processing workflow. This intermediary stores pre-analyzed performance characteristics, allowing the system to reference optimized settings without directly re-running complex analyses during processing.
3Speed
If batch size is increased to improve throughput, then processing speed is improved, but memory requirements and computational load increase
Solution Approach 1:
The system dynamically selects batch sizes based on the specific characteristics of the input data and the performance profile of the model. Rather than using a fixed batch size, the system adjusts the batch size dynamically by referencing pre-evaluated configuration data that identifies optimal batch sizes for different scenarios, balancing throughput with resource constraints.
Solution Approach 2:
The system changes the batch size parameter based on pre-analyzed performance data. During the configuration phase, the system evaluates multiple batch size parameters and stores the results. During actual processing, it selects and applies the appropriate batch size parameter from this stored data, optimizing the balance between processing speed and memory requirements.
Data Source
AI summary
A system is disclosed that has at least one input to receive at least one model and data to be processed with the at least one model, and at least one circuit configured to perform: in response to receiving, via the input, a first amount of data to be processed with the at least one model, based on configuration data indicating that when input data of the first amount is to be processed with the at least one model the input data should be supplemented with additional data to yield a second amount of data to be processed, generating additional data to supplement the first amount of data and yield aggregated data that has the second amount of data, the second amount of data being larger than the first amount of data, processing the aggregated data with the at least one model. Various other methods and systems are also disclosed.