Summary:
Arm’s Statistical Profiling Extension (SPE) is a powerful tool for performance analysis and optimization. This hardware-assisted CPU profiling mechanism provides detailed profiling capabilities, capturing key execution data such as program counters, data addresses, and PMU events. By leveraging SPE, software developers, performance analysts, and silicon engineers can gain valuable insights into their code and improve performance.
Apache Arrow CSV Writer: Unlocking Performance Potential
One example of utilizing SPE is optimizing the Apache Arrow CSV writer code. By measuring Instructions Per Cycle (IPC), bandwidth, MPKI, and miss ratios, the performance bottlenecks were identified. Profiling L1D cache events and branch mispredictions revealed issues related to the memcpy function, which experienced frequent cache misses and branch mispredictions. Further analysis of the branches within memcpy exposed an inefficient buffer size as the source of branch mispredictions. Armed with this information, the code was optimized, resulting in a 40% performance improvement on a Neoverse N1 platform.
Memory Access Analysis: Identifying Bottlenecks
SPE-based profiling offers valuable insights into memory operations, including memory latency and execution latency. By analyzing SPE-profiled data, it is possible to identify bottlenecks and performance issues related to memory access. The hierarchical data source hits recorded by SPE help pinpoint where memory accesses hit within the cache hierarchy, aiding in the identification of performance problems such as TLB misses.
Estimating Memory Bandwidth and Sensitivity Studies
SPE can also be used for estimating memory bandwidth, especially for code with predictable and well-known memory access patterns. While not highly accurate, SPE provides relative measurements during optimization exercises and sensitivity studies. The SPE-parser tool, introduced in SPE monitoring tools, processes the raw SPE-profiled data collected with the Linux perf tool to estimate memory read bandwidth.
Data Sharing Analysis: Improving Multi-Threaded Workloads
SPE profiling can be beneficial for analyzing data sharing in multi-threaded workloads. Issues like false sharing, which can lead to cache invalidation and reduced performance, can be detected using tools like Linux perf c2c. By analyzing memory access data obtained from SPE, including data source information and addresses, potential cache line-related issues can be identified, helping improve performance in multi-threaded scenarios.
In conclusion, Arm’s SPE is a comprehensive tool for performance analysis and optimization. By leveraging its detailed profiling capabilities, software developers, performance analysts, and silicon engineers can gain valuable insights into their code, identify bottlenecks, and enhance overall performance. Whether optimizing code, analyzing memory access, estimating memory bandwidth, or addressing data sharing issues, Arm SPE proves to be an invaluable asset.
The source of the article is from the blog procarsrl.com.ar