A Highly Scalable, Hybrid, Cross-Platform Timing Analysis Framework Providing Accurate Differential Throughput Estimation via Instruction-Level Tracing

Differential throughput estimation, i.e., predicting the performance impact of software changes, is critical when developing applications that rely on accurate timing bounds, such as automotive, avionic, or industrial control systems. However, developers often lack access to the target hardware to perform on-device measurements, and hence rely on instruction throughput estimation tools to evaluate performance impacts.

State-of-the-art techniques broadly fall into two categories: dynamic and static. Dynamic approaches emulate program execution using cycle-accurate microarchitectural simulators resulting in high precision at the cost of long turnaround times and convoluted setups. Static approaches reduce overhead by predicting cycle counts outside of a concrete runtime environment. However, they are limited by the lack of dynamic runtime information and mostly focus on predictions over single basic blocks which requires developers to manually construct critical instruction sequences.

We present MCAD 1 , a hybrid timing analysis framework that combines the advantages of dynamic and static approaches. Instead of relying on heavyweight cycle-accurate emulation, MCAD collects instruction traces along with dynamic runtime information from QEMU and streams them to a static throughput estimator. This allows developers to accurately estimate the performance impact of software changes for complete programs within minutes, reducing turnaround times by orders of magnitude compared to existing approaches with similar accuracy. Our evaluation shows that MCAD scales to real-world applications such as FFmpeg and Clang with millions of instructions, achieving < 3% geo. mean error * Both authors contributed equally to this research. † Now affiliated with Cerebras 1 The actual name has been redacted for anonymous review.