Hardware Verification Batch Farm Simulator
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Solution Overview
Problem
Current hardware verification test cases in batch computing environments require extensive resources and processor overhead, with no effective method to predict the performance impact of changing batch processing parameters.
Innovation Solution
A computer-implemented method and system that calculates the expected behavior of hardware verification test cases by configuring batch simulation parameters, gathering historical performance data, generating performance statistics, and applying a weighting algorithm to dynamically adjust test case weights, allowing for capacity and coverage planning without actual resource expenditure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If every available test case is run to verify hardware design, then verification completeness is improved, but processor overhead and resource usage increase
Solution Approach 1:
The patent creates a simulation farm that replicates the batch processing environment virtually. Instead of running actual test cases on physical processors, the system uses historical performance data to simulate and predict outcomes, replacing expensive physical resource usage with computational modeling.
Solution Approach 2:
The system performs preliminary analysis by gathering historical performance data and creating performance models before actual batch processing. This allows the system to predict resource requirements and optimize test case selection in advance, avoiding unnecessary processor overhead during actual execution.
2Measurement precision
If batch simulation parameters are adjusted to improve performance prediction accuracy, then measurement precision is improved, but calculation complexity increases
Solution Approach 1:
The patent systematically varies batch simulation parameters such as test case weighting factors, simulation duration, and resource allocation settings to find optimal configurations. By changing these parameters methodically, the system improves prediction accuracy while managing computational complexity through focused experimentation.
Solution Approach 2:
The system uses historical performance data as feedback to continuously refine performance models and adjust simulation parameters. This iterative process improves prediction accuracy by learning from actual outcomes, while the feedback mechanism helps identify which parameter adjustments yield the most significant improvements, avoiding unnecessary computational complexity.
Data Source
AI summary
The exemplary embodiments provide a computer implemented method, apparatus, and computer usable program code for calculating the expected behavior of a group of hardware verification test cases. Batch simulation parameters are configured. A test case is submitted for evaluation. Historical performance data for test cases associated with the submitted test case is gathered. A set of performance statistics for the submitted test case is generated based on the historical performance data and the configured batch simulation parameters. A set of values for the submitted test is generated based on the generated performance statistics for the submitted test case and the historical performance data. The generated set of values and the generated set of performance statistics for the submitted test case are displayed to a user.