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Estimating instruction-level throughput is critical for many applications:multimedia, low-latency networking, medical, automotive, avionic, andindustrial control systems all rely on tightly calculable and accurate timingbounds of their software. Unfortunately, how long a program may run - or if itmay indeed stop at all - cannot be answered in the general case. This is whystate-of-the-art throughput estimation tools usually focus on a subset ofoperations and make several simplifying assumptions. Correctly identifyingthese sets of constraints and regions of interest in the program typicallyrequires source code, specialized tools, and dedicated expert knowledge.Whenever a single instruction is modified, this process must be repeated,incurring high costs when iteratively developing timing sensitive code inpractice.  In this paper, we present MCAD, a novel and lightweight timing analysisframework that can identify the effects of code changes on themicroarchitectural level for binary programs. MCAD provides accuratedifferential throughput estimates by emulating whole program execution usingQEMU and forwarding traces to LLVM for instruction-level analysis. This allowsdevelopers to iterate quickly, with low overhead, using common tools:identifying execution paths that are less sensitive to changes overtiming-critical paths only takes minutes within MCAD. To the best of ourknowledge this represents an entirely new capability that reduces turnaroundtimes for differential throughput estimation by several orders of magnitudecompared to state-of-the-art tools. Our detailed evaluation shows that MCADscales to real-world applications like FFmpeg and Clang with millions ofinstructions, achieving < 3% geo mean error compared to ground truth timingsfrom hardware-performance counters on x86 and ARM machines.

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