Multicore Interferer Generation for Deterministic Processor Testing
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Solution Overview
Problem
Multicore processors often result in non-deterministic operations, which can lead to increased execution time and failure to meet real-time requirements, particularly in applications like aviation operations, where determinism is crucial.
Innovation Solution
An automated framework generates processor-specific interferers to test and characterize multicore determinism, ensuring deterministic and repeatable interference testing, with configurable parameters and post-processing analysis to identify worst-case latencies and deviations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multiple processor cores are used, then processing capacity and productivity are improved, but operational determinism deteriorates leading to non-deterministic behavior
Solution Approach 1:
The patent segments the testing process into distinct phases: generating interferer code for each core, executing tests with controlled interference, and analyzing results separately. This segmentation allows systematic characterization of each core's behavior under interference while maintaining overall test determinism.
Solution Approach 2:
The patent performs preliminary actions by generating interferer code and configuring test parameters before actual execution. The framework pre-defines interference scenarios, test cases, and evaluation criteria, enabling deterministic replay and consistent results across multiple test runs.
2Speed
If multiple processor cores are used, then processing speed is improved, but execution time variability increases causing real-time requirements to fail
Solution Approach 1:
The patent implements feedback mechanisms by measuring execution times under various interference conditions and using this data to characterize worst-case scenarios. The framework collects performance data, compares it against thresholds, and adjusts test parameters to comprehensively evaluate timing behavior.
Solution Approach 2:
The patent systematically changes test parameters including interference intensity, core activation patterns, and workload characteristics to characterize processor behavior across different operating conditions. This enables identification of worst-case execution times under controlled parameter variations.
3Measurement precision
If manual testing methods are used for processor characterization, then test accuracy is maintained, but time consumption and productivity are reduced
Solution Approach 1:
The patent creates copies of test code and interferer patterns that can be automatically generated and executed across multiple cores. The framework replicates test scenarios systematically, enabling comprehensive coverage without manual intervention for each test case.
Solution Approach 2:
The testing framework performs self-service by automatically generating test cases, configuring interferers, executing tests, and analyzing results without requiring manual setup for each test scenario. The system manages its own test workflow, significantly reducing engineering time while maintaining accuracy.
4Productivity
If processor-specific interferers are generated automatically, then testing efficiency is improved, but test complexity and device complexity increase
Solution Approach 1:
The patent creates a universal testing framework that can characterize multiple processor cores and different interferer types through a single unified system. The framework handles various processor architectures, interference patterns, and test scenarios using common infrastructure, reducing overall complexity despite increased capabilities.
Data Source
AI summary
A system for multi-core interferer generation includes a processor and a memory including instructions which, when executed by the processor, cause the system at least to perform generating an interference test for one or more processor cores based upon one or more configuration files; introducing interference to the one or more processor cores to generate one or more interference test results; and filtering the one or more test results by evaluating performance data against one or more expected result hypotheses, each defining benchmark execution behavior and tolerance thresholds.


