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

Abstract

Estimating instruction-level throughput is critical for many applications: multimedia, low-latency networking, medical, automotive, avionic, and industrial control systems all rely on tightly calculable and accurate timing bounds of their software. Unfortunately, how long a program may run - or if it may indeed stop at all - cannot be answered in the general case. This is why state-of-the-art throughput estimation tools usually focus on a subset of operations and make several simplifying assumptions. Correctly identifying these sets of constraints and regions of interest in the program typically requires 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 in practice. In this paper, we present MCAD, a novel and lightweight timing analysis framework that can identify the effects of code changes on the microarchitectural level for binary programs. MCAD provides accurate differential throughput estimates by emulating whole program execution using QEMU and forwarding traces to LLVM for instruction-level analysis. This allows developers to iterate quickly, with low overhead, using common tools: identifying execution paths that are less sensitive to changes over timing-critical paths only takes minutes within MCAD. To the best of our knowledge this represents an entirely new capability that reduces turnaround times for differential throughput estimation by several orders of magnitude compared to state-of-the-art tools. Our detailed evaluation shows that MCAD scales to real-world applications like FFmpeg and Clang with millions of instructions, achieving < 3% geo mean error compared to ground truth timings from hardware-performance counters on x86 and ARM machines.