Hardware Emulator Binary Instrumentation for Performance Estimation
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Solution Overview
Problem
Existing methods for evaluating software performance require expensive and time-consuming hardware testing, which is resource-intensive and limited to specific hardware configurations, making it inefficient for robust software development and evaluation.
Innovation Solution
A software testing framework utilizing a hardware emulator that instruments binaries to generate a trace of selected operations, which is then mapped to a representative profile of operations benchmarked on a corresponding hardware system, allowing for estimates of performance metrics without actual hardware execution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If real physical hardware is used to execute benchmarks, then performance measurement accuracy is improved, but hardware cost and resource consumption increase
Solution Approach 1:
The patent creates a virtual copy of the hardware system through hardware emulation. The emulator replicates the hardware's operational characteristics, allowing software benchmarks to be executed in a virtual environment that mirrors the target hardware's behavior. This copying approach enables accurate performance measurement without requiring physical hardware resources for each test configuration.
Solution Approach 2:
The hardware emulator serves as an intermediary between the software being tested and the physical hardware. It translates software benchmark executions into emulated hardware operations, providing a mediator that preserves measurement accuracy while eliminating the need for direct physical hardware interaction. The emulator absorbs the complexity of hardware variation and presents a standardized testing interface.
2Adaptability or versatility
If multiple hardware configurations are tested, then software adaptability is improved, but testing time and resource consumption increase
Solution Approach 1:
The hardware emulator is designed to emulate multiple different hardware configurations through a single universal platform. By configuring the emulator to represent different target hardware architectures and specifications, the same testing infrastructure can evaluate software across diverse hardware scenarios without requiring separate physical test beds for each configuration type.
Solution Approach 2:
The emulator is pre-configured with virtual hardware models that represent various target platforms. Before actual testing begins, the emulation environment is prepared with the appropriate hardware virtualization settings, allowing rapid switching between different hardware configurations through software configuration rather than physical reconfiguration. This preliminary setup eliminates time-consuming hardware provisioning and setup steps.
3Reliability
If statistical analysis is performed on timing data, then measurement reliability is improved, but data processing complexity increases
Solution Approach 1:
The patent extracts and isolates timing measurement data from the complex emulated hardware environment. By capturing only the critical timing information at specific instrumentation points within the emulated software execution, the system separates the essential measurement data from the complexity of the emulation process. This extraction enables statistical analysis to be performed on cleaned, pre-processed data rather than raw emulated output, reducing processing complexity while maintaining reliability.
Data Source
AI summary
Aspects of the technology provide a software testing framework that can significantly reduce hardware resources needed to validate code modules. This may include employing a hardware emulator capable of instrumenting binaries to produce a trace of operations performed by a given program. The trace of operations performed by the given program may be mapped to representative profiles of operations benchmarked on a hardware system corresponding to the hardware emulated by the hardware emulator. The representative profile may contain sets of representative operations previously performed on the hardware. The mapping may allow for estimates on performance metrics of the given program (e.g., efficacy and/or speed) when run on the hardware. Such estimates may allow for the identification of operations that cause the given program to run inefficiently or slowly on the hardware.