EIC code displayed by LXR master/​include/​arrow/​util/​benchmark_util.h	Source navigation Diff markup Identifier search General search
	Version: master test Revert   Change

File indexing completed on 2025-08-28 08:27:02

 // Licensed to the Apache Software Foundation (ASF) under one
 // or more contributor license agreements.  See the NOTICE file
 // distributed with this work for additional information
 // regarding copyright ownership.  The ASF licenses this file
 // to you under the Apache License, Version 2.0 (the
 // "License"); you may not use this file except in compliance
 // with the License.  You may obtain a copy of the License at
 //
 //   http://www.apache.org/licenses/LICENSE-2.0
 //
 // Unless required by applicable law or agreed to in writing,
 // software distributed under the License is distributed on an
 // "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
 // KIND, either express or implied.  See the License for the
 // specific language governing permissions and limitations
 // under the License.
 
 #include <algorithm>
 #include <cstdint>
 #include <string>
 
 #include "benchmark/benchmark.h"
 
 #include "arrow/memory_pool.h"
 #include "arrow/type_fwd.h"
 #include "arrow/util/cpu_info.h"
 #include "arrow/util/logging.h"  // IWYU pragma: keep
 
 namespace arrow {
 
 // Benchmark changed its parameter type between releases from
 // int to int64_t. As it doesn't have version macros, we need
 // to apply C++ template magic.
 
 template <typename Func>
 struct BenchmarkArgsType;
 
 // Pattern matching that extracts the vector element type of Benchmark::Args()
 template <typename Values>
 struct BenchmarkArgsType<benchmark::internal::Benchmark* (
     benchmark::internal::Benchmark::*)(const std::vector<Values>&)> {
   using type = Values;
 };
 
 using ArgsType =
     typename BenchmarkArgsType<decltype(&benchmark::internal::Benchmark::Args)>::type;
 
 using internal::CpuInfo;
 
 static const CpuInfo* cpu_info = CpuInfo::GetInstance();
 
 static const int64_t kL1Size = cpu_info->CacheSize(CpuInfo::CacheLevel::L1);
 static const int64_t kL2Size = cpu_info->CacheSize(CpuInfo::CacheLevel::L2);
 static const int64_t kL3Size = cpu_info->CacheSize(CpuInfo::CacheLevel::L3);
 static const int64_t kCantFitInL3Size = kL3Size * 4;
 static const std::vector<int64_t> kMemorySizes = {kL1Size, kL2Size, kL3Size,
                                                   kCantFitInL3Size};
 // 0 is treated as "no nulls"
 static const std::vector<ArgsType> kInverseNullProportions = {10000, 100, 10, 2, 1, 0};
 
 struct GenericItemsArgs {
   // number of items processed per iteration
   const int64_t size;
 
   // proportion of nulls in generated arrays
   double null_proportion;
 
   explicit GenericItemsArgs(benchmark::State& state)
       : size(state.range(0)), state_(state) {
     if (state.range(1) == 0) {
       this->null_proportion = 0.0;
     } else {
       this->null_proportion = std::min(1., 1. / static_cast<double>(state.range(1)));
     }
   }
 
   ~GenericItemsArgs() {
     state_.counters["size"] = static_cast<double>(size);
     state_.counters["null_percent"] = null_proportion * 100;
     state_.SetItemsProcessed(state_.iterations() * size);
   }
 
  private:
   benchmark::State& state_;
 };
 
 void BenchmarkSetArgsWithSizes(benchmark::internal::Benchmark* bench,
                                const std::vector<int64_t>& sizes = kMemorySizes) {
   bench->Unit(benchmark::kMicrosecond);
 
   for (const auto size : sizes) {
     for (const auto inverse_null_proportion : kInverseNullProportions) {
       bench->Args({static_cast<ArgsType>(size), inverse_null_proportion});
     }
   }
 }
 
 void BenchmarkSetArgs(benchmark::internal::Benchmark* bench) {
   BenchmarkSetArgsWithSizes(bench, kMemorySizes);
 }
 
 void RegressionSetArgs(benchmark::internal::Benchmark* bench) {
   // Regression do not need to account for cache hierarchy, thus optimize for
   // the best case.
   BenchmarkSetArgsWithSizes(bench, {kL1Size});
 }
 
 // RAII struct to handle some of the boilerplate in regression benchmarks
 struct RegressionArgs {
   // size of memory tested (per iteration) in bytes
   int64_t size;
 
   // proportion of nulls in generated arrays
   double null_proportion;
 
   // If size_is_bytes is true, then it's a number of bytes, otherwise it's the
   // number of items processed (for reporting)
   explicit RegressionArgs(benchmark::State& state, bool size_is_bytes = true)
       : size(state.range(0)), state_(state), size_is_bytes_(size_is_bytes) {
     if (state.range(1) == 0) {
       this->null_proportion = 0.0;
     } else {
       this->null_proportion = std::min(1., 1. / static_cast<double>(state.range(1)));
     }
   }
 
   ~RegressionArgs() {
     state_.counters["size"] = static_cast<double>(size);
     state_.counters["null_percent"] = null_proportion * 100;
     if (size_is_bytes_) {
       state_.SetBytesProcessed(state_.iterations() * size);
     } else {
       state_.SetItemsProcessed(state_.iterations() * size);
     }
   }
 
  private:
   benchmark::State& state_;
   bool size_is_bytes_;
 };
 
 class MemoryPoolMemoryManager : public benchmark::MemoryManager {
   void Start() override {
     memory_pool = std::make_shared<ProxyMemoryPool>(default_memory_pool());
 
     MemoryPool* default_pool = default_memory_pool();
     global_allocations_start = default_pool->num_allocations();
   }
 
 // BENCHMARK_DONT_OPTIMIZE is used here to detect Google Benchmark
 // 1.8.0. We can remove this Stop(Result*) when we require Google
 // Benchmark 1.8.0 or later.
 #ifndef BENCHMARK_DONT_OPTIMIZE
   void Stop(Result* result) override { Stop(*result); }
 #endif
 
   void Stop(benchmark::MemoryManager::Result& result) override {
     // If num_allocations is still zero, we assume that the memory pool wasn't passed down
     // so we should record them.
     MemoryPool* default_pool = default_memory_pool();
     int64_t new_default_allocations =
         default_pool->num_allocations() - global_allocations_start;
 
     // Only record metrics if (1) there were allocations and (2) we
     // recorded at least one.
     if (new_default_allocations > 0 && memory_pool->num_allocations() > 0) {
       if (new_default_allocations > memory_pool->num_allocations()) {
         // If we missed some, let's report that.
         int64_t missed_allocations =
             new_default_allocations - memory_pool->num_allocations();
         ARROW_LOG(WARNING) << "BenchmarkMemoryTracker recorded some allocations "
                            << "for a benchmark, but missed " << missed_allocations
                            << " allocations.\n";
       }
 
       result.max_bytes_used = memory_pool->max_memory();
       result.total_allocated_bytes = memory_pool->total_bytes_allocated();
       result.num_allocs = memory_pool->num_allocations();
     }
   }
 
  public:
   std::shared_ptr<::arrow::ProxyMemoryPool> memory_pool;
 
  protected:
   int64_t global_allocations_start;
 };
 
 /// \brief Track memory pool allocations in benchmarks.
 ///
 /// Instantiate as a global variable to register the hooks into Google Benchmark
 /// to collect memory metrics. Before each benchmark, a new ProxyMemoryPool is
 /// created. It can then be accessed with memory_pool(). Once the benchmark is
 /// complete, the hook will record the maximum memory used, the total bytes
 /// allocated, and the total number of allocations. If no allocations were seen,
 /// (for example, if you forgot to pass down the memory pool), then these metrics
 /// will not be saved.
 ///
 /// Since this is used as one global variable, this will not work if multiple
 /// benchmarks are run concurrently or for multi-threaded benchmarks (ones
 /// that use `->ThreadRange(...)`).
 class BenchmarkMemoryTracker {
  public:
   BenchmarkMemoryTracker() : manager_() { ::benchmark::RegisterMemoryManager(&manager_); }
   ::arrow::MemoryPool* memory_pool() const { return manager_.memory_pool.get(); }
 
  protected:
   ::arrow::MemoryPoolMemoryManager manager_;
 };
 
 }  // namespace arrow

This page was automatically generated by the 2.3.7 LXR engine. The LXR team