libtrilinos-tpetra-dev 12.12.1-5 / usr / include / trilinos / Tsqr_TbbTest.hpp

//@HEADER
// ************************************************************************
//
//          Kokkos: Node API and Parallel Node Kernels
//              Copyright (2008) Sandia Corporation
//
// Under the terms of Contract DE-AC04-94AL85000 with Sandia Corporation,
// the U.S. Government retains certain rights in this software.
//
// Redistribution and use in source and binary forms, with or without
// modification, are permitted provided that the following conditions are
// met:
//
// 1. Redistributions of source code must retain the above copyright
// notice, this list of conditions and the following disclaimer.
//
// 2. Redistributions in binary form must reproduce the above copyright
// notice, this list of conditions and the following disclaimer in the
// documentation and/or other materials provided with the distribution.
//
// 3. Neither the name of the Corporation nor the names of the
// contributors may be used to endorse or promote products derived from
// this software without specific prior written permission.
//
// THIS SOFTWARE IS PROVIDED BY SANDIA CORPORATION "AS IS" AND ANY
// EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
// PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL SANDIA CORPORATION OR THE
// CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
// EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
// PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
// PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
// LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
// NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
// SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
//
// Questions? Contact Michael A. Heroux (maherou@sandia.gov)
//
// ************************************************************************
//@HEADER

#ifndef __TSQR_Test_TbbTest_hpp
#define __TSQR_Test_TbbTest_hpp

#include <Tsqr_nodeTestProblem.hpp>
#include <Tsqr_verifyTimerConcept.hpp>
#include <Tsqr_Random_NormalGenerator.hpp>


#include <Tsqr_LocalVerify.hpp>
#include <Tsqr_Matrix.hpp>
#include <Tsqr_Util.hpp>
#include <TbbTsqr.hpp>

#include <Teuchos_LAPACK.hpp>
#include <Teuchos_Time.hpp>

#include <algorithm>
#include <cstring> // size_t definition
//#include <iomanip>
#include <iostream>
#include <limits>
#include <stdexcept>
#include <vector>

using std::make_pair;
using std::pair;
using std::vector;

using std::cerr;
using std::cout;
using std::endl;

////////////////////////////////////////////////////////////////////////////////
////////////////////////////////////////////////////////////////////////////////

namespace TSQR {
  namespace Test {

    /// Test the accuracy of Intel TBB TSQR on an nrows by ncols
    /// matrix (using the given number of cores and the given cache
    /// block size (in bytes)), and print the results to stdout.
    template< class Ordinal, class Scalar >
    void
    verifyTbbTsqr (const std::string& scalarTypeName,
                   TSQR::Random::NormalGenerator< Ordinal, Scalar >& generator,
                   const Ordinal nrows,
                   const Ordinal ncols,
                   const int num_cores,
                   const size_t cache_size_hint,
                   const bool contiguous_cache_blocks,
                   const bool printFieldNames,
                   const bool human_readable,
                   const bool b_debug = false)
    {
      typedef Teuchos::Time timer_type;
      typedef TSQR::TBB::TbbTsqr< Ordinal, Scalar, timer_type > node_tsqr_type;
      typedef typename node_tsqr_type::FactorOutput factor_output_type;
      typedef Teuchos::ScalarTraits<Scalar> STS;
      typedef typename STS::magnitudeType magnitude_type;
      using std::cerr;
      using std::cout;
      using std::endl;

      node_tsqr_type actor (num_cores, cache_size_hint);

      if (b_debug) {
        cerr << "Intel TBB TSQR test problem:" << endl
             << "* " << nrows << " x " << ncols << endl
             << "* # cores: " << num_cores << endl
             << "* Cache size hint in bytes: " << actor.cache_size_hint() << endl;
        if (contiguous_cache_blocks) {
          cerr << "* Contiguous cache blocks" << endl;
        }
      }

      Matrix< Ordinal, Scalar > A (nrows, ncols);
      Matrix< Ordinal, Scalar > A_copy (nrows, ncols);
      Matrix< Ordinal, Scalar > Q (nrows, ncols);
      Matrix< Ordinal, Scalar > R (ncols, ncols);
      if (std::numeric_limits< Scalar >::has_quiet_NaN) {
        A.fill (std::numeric_limits< Scalar>::quiet_NaN());
        A_copy.fill (std::numeric_limits< Scalar >::quiet_NaN());
        Q.fill (std::numeric_limits< Scalar >::quiet_NaN());
        R.fill (std::numeric_limits< Scalar >::quiet_NaN());
      }
      const Ordinal lda = nrows;
      const Ordinal ldq = nrows;
      const Ordinal ldr = ncols;

      // Create a test problem
      nodeTestProblem (generator, nrows, ncols, A.get(), A.lda(), true);

      if (b_debug) {
        cerr << "-- Generated test problem" << endl;
      }

      // Copy A into A_copy, since TSQR overwrites the input.  If
      // specified, rearrange the data in A_copy so that the data in
      // each cache block is contiguously stored.
      if (! contiguous_cache_blocks) {
        deep_copy (A_copy, A);
        if (b_debug) {
          cerr << "-- Copied test problem from A into A_copy" << endl;
        }
      }
      else {
        actor.cache_block (nrows, ncols, A_copy.get(), A.get(), A.lda());
        if (b_debug) {
          cerr << "-- Reorganized test matrix to have contiguous "
            "cache blocks" << endl;
        }
        // Verify cache blocking, when in debug mode.
        if (b_debug) {
          Matrix< Ordinal, Scalar > A2 (nrows, ncols);
          if (std::numeric_limits< Scalar >::has_quiet_NaN) {
            A2.fill (std::numeric_limits< Scalar >::quiet_NaN());
          }
          actor.un_cache_block (nrows, ncols, A2.get(), A2.lda(), A_copy.get());
          if (matrix_equal (A, A2)) {
            if (b_debug) {
              cerr << "-- Cache blocking test succeeded!" << endl;
            }
          }
          else {
            throw std::logic_error ("Cache blocking failed");
          }
        }
      }

      // Fill R with zeros, since the factorization may not overwrite
      // the strict lower triangle of R.
      R.fill (Scalar(0));

      // Factor the matrix and compute the explicit Q factor
      factor_output_type factor_output =
        actor.factor (nrows, ncols, A_copy.get(), A_copy.lda(), R.get(),
                      R.lda(), contiguous_cache_blocks);
      if (b_debug) {
        cerr << "-- Finished TbbTsqr::factor" << endl;
      }
      actor.explicit_Q (nrows, ncols, A_copy.get(), A_copy.lda(), factor_output,
                        ncols, Q.get(), Q.lda(), contiguous_cache_blocks);
      if (b_debug) {
        cerr << "-- Finished TbbTsqr::explicit_Q" << endl;
      }

      // "Un"-cache-block the output Q (the explicit Q factor), if
      // contiguous cache blocks were used.  This is only necessary
      // because local_verify() doesn't currently support contiguous
      // cache blocks.
      if (contiguous_cache_blocks) {
        // Use A_copy as temporary storage for un-cache-blocking Q.
        actor.un_cache_block (nrows, ncols, A_copy.get(), A_copy.lda(), Q.get());
        deep_copy (Q, A_copy);
        if (b_debug) {
          cerr << "-- Un-cache-blocked output Q factor" << endl;
        }
      }

      // Print out the R factor
      if (b_debug) {
        cerr << endl << "-- R factor:" << endl;
        print_local_matrix (cerr, ncols, ncols, R.get(), R.lda());
        cerr << endl;
      }

      // Validate the factorization
      std::vector< magnitude_type > results =
        local_verify (nrows, ncols, A.get(), lda, Q.get(), ldq, R.get(), ldr);
      if (b_debug) {
        cerr << "-- Finished local_verify" << endl;
      }

      // Print the results
      if (human_readable) {
        cout << "Parallel (via Intel\'s Threading Building Blocks) / cache-blocked) TSQR:" << endl
             << "Scalar type: " << scalarTypeName << endl
             << "# rows: " << nrows << endl
             << "# columns: " << ncols << endl
             << "# cores: " << num_cores << endl
             << "Cache size hint in bytes: " << actor.cache_size_hint() << endl
             << "Contiguous cache blocks? " << contiguous_cache_blocks << endl
             << "Absolute residual $\\|A - Q*R\\|_2$: "
             << results[0] << endl
             << "Absolute orthogonality $\\|I - Q^T*Q\\|_2$: "
             << results[1] << endl
             << "Test matrix norm $\\| A \\|_F$: "
             << results[2] << endl
             << endl;
      }
      else {
        if (printFieldNames) {
          const char prefix[] = "%";
          cout << prefix
               << "method"
               << ",scalarType"
               << ",numRows"
               << ",numCols"
               << ",numThreads"
               << ",cacheSizeHint"
               << ",contiguousCacheBlocks"
               << ",absFrobResid"
               << ",absFrobOrthog"
               << ",frobA"
               << endl;
        }
        cout << "TbbTsqr"
             << "," << scalarTypeName
             << "," << nrows
             << "," << ncols
             << "," << num_cores
             << "," << actor.cache_size_hint()
             << "," << contiguous_cache_blocks
             << "," << results[0]
             << "," << results[1]
             << "," << results[2]
             << endl;
      }
    }

    /// \brief Benchmark Intel TBB TSQR vs. LAPACK's QR, and print the
    ///   results to stdout.
    ///
    /// \note c++0x support is need in order to have a default
    /// template parameter argument for a template function, otherwise
    /// we would have templated this function on TimerType and made
    /// Teuchos::Time the default.
    template< class Ordinal, class Scalar >
    void
    benchmarkTbbTsqr (const std::string& scalarTypeName,
                      const int ntrials,
                      const Ordinal nrows,
                      const Ordinal ncols,
                      const int num_cores,
                      const size_t cache_size_hint,
                      const bool contiguous_cache_blocks,
                      const bool printFieldNames,
                      const bool human_readable)
    {
      using TSQR::TBB::TbbTsqr;
      using std::cerr;
      using std::cout;
      using std::endl;

      typedef Teuchos::Time timer_type;
      typedef Ordinal ordinal_type;
      typedef Scalar scalar_type;
      typedef Matrix< ordinal_type, scalar_type > matrix_type;
      typedef TbbTsqr< ordinal_type, scalar_type, timer_type > node_tsqr_type;

      // Pseudorandom normal(0,1) generator.  Default seed is OK,
      // because this is a benchmark, not an accuracy test.
      TSQR::Random::NormalGenerator< ordinal_type, scalar_type > generator;

      // Set up TSQR implementation.
      node_tsqr_type actor (num_cores, cache_size_hint);

      matrix_type A (nrows, ncols);
      matrix_type A_copy (nrows, ncols);
      matrix_type Q (nrows, ncols);
      matrix_type R (ncols, ncols, scalar_type(0));

      // Fill R with zeros, since the factorization may not overwrite
      // the strict lower triangle of R.
      R.fill (scalar_type(0));

      // Create a test problem
      nodeTestProblem (generator, nrows, ncols, A.get(), A.lda(), false);

      // Copy A into A_copy, since TSQR overwrites the input.  If
      // specified, rearrange the data in A_copy so that the data in
      // each cache block is contiguously stored.
      if (contiguous_cache_blocks) {
        actor.cache_block (nrows, ncols, A_copy.get(), A.get(), A.lda());
      }
      else {
        deep_copy (A_copy, A);
      }

      // Do a few timing runs and throw away the results, just to warm
      // up any libraries that do autotuning.
      const int numWarmupRuns = 5;
      for (int warmupRun = 0; warmupRun < numWarmupRuns; ++warmupRun) {
        // Factor the matrix in-place in A_copy, and extract the
        // resulting R factor into R.
        typedef typename node_tsqr_type::FactorOutput factor_output_type;
        factor_output_type factor_output =
          actor.factor (nrows, ncols, A_copy.get(), A_copy.lda(),
                        R.get(), R.lda(), contiguous_cache_blocks);
        // Compute the explicit Q factor (which was stored
        // implicitly in A_copy and factor_output) and store in Q.
        // We don't need to un-cache-block the output, because we
        // aren't verifying it here.
        actor.explicit_Q (nrows, ncols, A_copy.get(), A_copy.lda(),
                          factor_output, ncols, Q.get(), Q.lda(),
                          contiguous_cache_blocks);
      }

      // Benchmark TBB-based TSQR for ntrials trials.
      //
      // Name of timer doesn't matter here; we only need the timing.
      timer_type timer("TbbTsqr");
      timer.start();
      for (int trial_num = 0; trial_num < ntrials; ++trial_num) {
        // Factor the matrix in-place in A_copy, and extract the
        // resulting R factor into R.
        typedef typename node_tsqr_type::FactorOutput factor_output_type;
        factor_output_type factor_output =
          actor.factor (nrows, ncols, A_copy.get(), A_copy.lda(),
                        R.get(), R.lda(), contiguous_cache_blocks);
        // Compute the explicit Q factor (which was stored
        // implicitly in A_copy and factor_output) and store in Q.
        // We don't need to un-cache-block the output, because we
        // aren't verifying it here.
        actor.explicit_Q (nrows, ncols, A_copy.get(), A_copy.lda(),
                          factor_output, ncols, Q.get(), Q.lda(),
                          contiguous_cache_blocks);
      }
      const double tbb_tsqr_timing = timer.stop();

      // Print the results
      if (human_readable) {
        cout << "(Intel TBB / cache-blocked) TSQR cumulative timings:" << endl
             << "Scalar type: " << scalarTypeName << endl
             << "# rows: " << nrows << endl
             << "# columns: " << ncols << endl
             << "# cores: " << num_cores << endl
             << "Cache size hint in bytes: " << actor.cache_size_hint() << endl
             << "Contiguous cache blocks? " << contiguous_cache_blocks << endl
             << "# trials: " << ntrials << endl
             << "Total time (s) = " << tbb_tsqr_timing << endl
             << "Total time (s) in factor() (min over all tasks): "
             << (ntrials * actor.min_seq_factor_timing()) << endl
             << "Total time (s) in factor() (max over all tasks): "
             << (ntrials * actor.max_seq_factor_timing()) << endl
             << "Total time (s) in apply() (min over all tasks): "
             << (ntrials * actor.min_seq_apply_timing()) << endl
             << "Total time (s) in apply() (max over all tasks): "
             << (ntrials * actor.max_seq_apply_timing()) << endl
             << endl << endl;
        cout << "(Intel TBB / cache-blocked) TSQR per-invocation timings:" << endl;

        std::vector<TimeStats> stats;
        actor.getStats (stats);
        std::vector<std::string> labels;
        actor.getStatsLabels (labels);

        const std::string labelLabel ("label");
        for (std::vector<std::string>::size_type k = 0; k < labels.size(); ++k) {
          const bool printHeaders = (k == 0);
          if (stats[k].count() > 0)
            stats[k].print (cout, human_readable, labels[k], labelLabel, printHeaders);
        }
      }
      else {
        if (printFieldNames) {
          const char prefix[] = "%";
          cout << prefix
               << "method"
               << ",scalarType"
               << ",numRows"
               << ",numCols"
               << ",numThreads"
               << ",cacheSizeHint"
               << ",contiguousCacheBlocks"
               << ",numTrials"
               << ",timing"
               << endl;
        }

        // We don't include {min,max}_seq_apply_timing() here, because
        // those times don't benefit from the accuracy of benchmarking
        // for ntrials > 1.  Thus, it's misleading to include them
        // with tbb_tsqr_timing, the total time over ntrials trials.
        cout << "TbbTsqr"
             << "," << scalarTypeName
             << "," << nrows
             << "," << ncols
             << "," << num_cores
             << "," << actor.cache_size_hint()
             << "," << contiguous_cache_blocks
             << "," << ntrials
             << "," << tbb_tsqr_timing
             << endl;
      }
    }
  } // namespace Test
} // namespace TSQR

#endif // __TSQR_Test_TbbTest_hpp