Statistical Profile Extension: extracting value from SPE for SoC Telemetry
The Arm Statistical Profiling Extension (SPE) is an architectural feature designed for enhanced instruction execution profiling within Arm CPUs. This feature has been available since the introduction of the Neoverse N1 CPU platform in 2019, along with performance monitor units (PMUs) generally available in Arm CPUs. An important step in extracting value from capabilities like SPE and PMUs is the tooling, documentation, and examples to form a top-down solution for SoC telemetry. Six engineers at Arm recently published a detailed white paper on the use of SPE for performance analysis. Their approach and findings are summarized here. This blog post aims to introduce the concept of using SPE for performance analysis and root cause analysis, targeting software developers, performance analysts, and silicon engineers.
Arm SPE is a hardware-assisted CPU profiling mechanism that offers detailed profiling capabilities. It records key execution data, including program counters, data addresses, and PMU events. SPE enhances performance analysis for branches, memory access, and more, making it useful for software optimization. SPE data can be applied for precise sampling in source code hotspot detection, memory access analysis, and data sharing analysis using tools like the Linux perf tool. SPE sampling involves four stages: statistical selection of operations, recording key execution information, post-filtering of sample records, and storing records in memory. It enables efficient profiling and data extraction using monitoring tools. SPE uses a down counter to periodically select micro-operations for profiling. SPE sample records capture the execution lifecycle of an operation, starting at the CPU backend.
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