cpp/src/arrow/tensor_benchmark.cc

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// to you under the Apache License, Version 2.0 (the
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// with the License.  You may obtain a copy of the License at
//
//   http://www.apache.org/licenses/LICENSE-2.0
//
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#include "benchmark/benchmark.h"

#include "arrow/record_batch.h"
#include "arrow/testing/gtest_util.h"
#include "arrow/testing/random.h"
#include "arrow/type.h"
#include "arrow/util/benchmark_util.h"

namespace arrow {

template <typename ValueType>
static void BatchToTensorSimple(benchmark::State& state) {
  using CType = typename ValueType::c_type;
  std::shared_ptr<DataType> ty = TypeTraits<ValueType>::type_singleton();

  const int64_t num_cols = state.range(1);
  const int64_t num_rows = state.range(0) / num_cols / sizeof(CType);
  arrow::random::RandomArrayGenerator gen_{42};

  std::vector<std::shared_ptr<Field>> fields = {};
  std::vector<std::shared_ptr<Array>> columns = {};

  for (int64_t i = 0; i < num_cols; ++i) {
    fields.push_back(field("f" + std::to_string(i), ty));
    columns.push_back(gen_.ArrayOf(ty, num_rows));
  }
  auto schema = std::make_shared<Schema>(std::move(fields));
  auto batch = RecordBatch::Make(schema, num_rows, columns);

  for (auto _ : state) {
    ASSERT_OK_AND_ASSIGN(auto tensor, batch->ToTensor());
  }
  state.SetItemsProcessed(state.iterations() * num_rows * num_cols);
  state.SetBytesProcessed(state.iterations() * ty->byte_width() * num_rows * num_cols);
}

void SetArgs(benchmark::internal::Benchmark* bench) {
  for (int64_t size : {kL1Size, kL2Size}) {
    for (int64_t num_columns : {3, 30, 300}) {
      bench->Args({size, num_columns});
      bench->ArgNames({"size", "num_columns"});
    }
  }
}

BENCHMARK_TEMPLATE(BatchToTensorSimple, Int8Type)->Apply(SetArgs);
BENCHMARK_TEMPLATE(BatchToTensorSimple, Int16Type)->Apply(SetArgs);
BENCHMARK_TEMPLATE(BatchToTensorSimple, Int32Type)->Apply(SetArgs);
BENCHMARK_TEMPLATE(BatchToTensorSimple, Int64Type)->Apply(SetArgs);

}  // namespace arrow