Measurement tools

From HPC Wiki
Jump to: navigation, search

Hardware Performance Counter Measurement Tools

Low Level

  • Perf: The main interface in the Linux kernel and a corresponding user-space tool to measure hardware counters
  • PAPI (Performance-API): A generic API for applications to measure different aspects of the system. For hardware performance counters it uses the perf backend for measurements. Other plugins for GPU and other components exist
  • PCM (Performance Counter Monitor): A higher level tool and API that provides common metrics like memory bandwidth and NUMA traffic. The API also provides access to any hardware counter event
  • PMU-Tools: A set of Python scripts that use the perf backend
  • LIKWID: Command line applications and API to measure hardware events which can use perf as backend but also provides other backends to be independent of the kernel version

High Level

  • ARM Performance Report: A tool that provides a simple one page HTML report that highlights processor, memory, communication and I/O issues and offers advice on how to improve the performance.
  • Vampir: A scalable framework for performance analysis using PAPI as backend
  • TAU: Utilities to sample or instrument code for hardware counters and other metrics
  • HPC-Toolkit: Toolkit to sample timers and hardware performance counters for serial and parallel applications
  • Intel Advisor: A vectorization and threading optimization tool
  • Intel VTune: A performance profiling tool to analyse algorithms and hardware usage for serial and parallel applications
  • Scalasca Trace Tools: A performance optimisation tool for runtime behaviour measurement and analysis of parallel programs
  • Score-P: A Scalable Performance Measurement Infrastructure for Parallel Codes
  • Intel Trace Collector/Analyzer: Powerful tools that acquire/display information on the communication behavior of MPI programs
  • Oracle Sampling Collector and Performance Analyzer: Pair of tools that can collect and analyze performance data for serial or parallel applications

Links and Further Information