Skip to content

Latest commit

 

History

History
55 lines (43 loc) · 3.46 KB

benchmark.rst

File metadata and controls

55 lines (43 loc) · 3.46 KB
Raw

Benchmark

The openPMD API provides utilities to quickly configure and run benchmarks in a flexible fashion. The starting point for configuring and running benchmarks is the class template Benchmark<DatasetFillerProvider>.

#include "openPMD/benchmark/mpi/Benchmark.hpp"

An object of this class template allows to preconfigure a number of benchmark runs to execute, each run specified by:

  • The compression configuration, consisting itself of the compression string and the compression level.
  • The backend to use, specified by the filename extension (e.g. "h5", "bp", "json", ...).
  • The type of data to write, specified by the openPMD datatype.
  • The number of ranks to use, not greater than the MPI size. An overloaded version of addConfiguration() exists that picks the MPI size.
  • The number n of iterations. The benchmark will effectively be repeated n times.

The benchmark object is globally (i.e. by its constructor) specified by:

  • The base path to use. This will be extended with the chosen backend's filename extension. Benchmarks might overwrite each others' files.
  • The total extent of the dataset across all MPI ranks.
  • The BlockSlicer, i.e. an object telling each rank which portion of the dataset to write to and read from. Most users will be content with the implementation provided by OneDimensionalBlockSlicer that will simply divide the dataset into hyperslabs along one dimension, default = 0. This implementation can also deal with odd dimensions that are not divisible by the MPI size.
  • A DatasetFillerProvider. DatasetFiller<T> is an abstract class template whose job is to create the write data of type T for one run of the benchmark. Since one Benchmark object allows to use several datatypes, a DatasetFillerProvider is needed to create such objects. DatasetFillerProvider is a template parameter of the benchmark class template and should be a templated functor whose operator()<T>() returns a shared_ptr<DatasetFiller<T>> (or a value that can be dynamically casted to it). For users seeking to only run the benchmark with one datatype, the class template SimpleDatasetFillerProvider<DF> will lift a DatasetFiller<T> to a DatasetFillerProvider whose operator()<T'>() will only successfully return if T and T' are the same type.
  • The MPI Communicator.

The class template RandomDatasetFiller<Distr, T> (where by default T = typename Distr::result_type) provides an implementation of the DatasetFiller<T> that lifts a random distribution to a DatasetFiller. The general interface of a DatasetFiller<T> is kept simple, but an implementation should make sure that every call to DatasetFiller<T>::produceData() takes roughly the same amount of time, thus allowing to deduct from the benchmark results the time needed for producing data.

The configured benchmarks are run one after another by calling the method Benchmark<...>::runBenchmark<Clock>(int rootThread). The Clock template parameter should meet the requirements of a trivial clock. Although every rank will return a BenchmarkReport<typename Clock::rep>, only the report of the previously specified root rank will be populated with data, i.e. all ranks' data will be collected into one report.

Example Usage

8_benchmark_parallel.cpp