Threadmeasured

Threadmeasured is a software framework designed to collect and analyze per-thread performance data in multi-threaded applications. It provides a common API to measure execution time, CPU usage, context switches, and memory-affinity metrics on a per-thread basis, across different runtimes and programming languages.

The project aims to help developers identify thread imbalance, synchronization bottlenecks, and scheduling overhead in parallel workloads. It supports instrumentation at user-defined scopes as well as sampling-based collection, allowing lightweight profiling or detailed tracing depending on configuration.

Core components include a timer, a thread-local data store, a data collector, and an exporter. Timers can be high-resolution, with nanosecond precision where available. The exporter can emit data in JSON, CSV, or Prometheus formats, and can integrate with visualization dashboards. The design emphasizes minimal intrusion by using language-specific constructs to start and stop measurements automatically, such as guards or RAII patterns where appropriate. The system aggregates results per thread and can correlate them with hardware metrics like cache misses and branch mispredictions.

History and usage: Tools with similar goals emerged as multi-core architectures became prevalent, and threadmeasured-like approaches are used in both academic and industry settings. It is applicable in high-performance computing, real-time analytics, and server applications to improve load balancing and scheduling decisions.

Limitations: Instrumentation incurs overhead and can perturb the very metrics it aims to measure. Platform-specific inaccuracies can also affect results. Users should calibrate sampling rates, validate measurements under representative workloads, and interpret data within the measurement context.