Merge branch 'master' into sf-unpacked-path-struct

CHANGELOG.md

@@ -25,6 +25,7 @@
       - CHANGED: Use node-api instead of NAN. [#6452](https://github.com/Project-OSRM/osrm-backend/pull/6452)
     - Misc:
       - CHANGED: Use struct instead of tuple to define UnpackedPath. [#6974](https://github.com/Project-OSRM/osrm-backend/pull/6974)
+      - CHANGED: Micro performance optimisation in map matching. [#6976](https://github.com/Project-OSRM/osrm-backend/pull/6976)
       - CHANGED: Re-use priority queue in StaticRTree. [#6952](https://github.com/Project-OSRM/osrm-backend/pull/6952)
       - CHANGED: Optimise encodePolyline function. [#6940](https://github.com/Project-OSRM/osrm-backend/pull/6940)
       - CHANGED: Avoid reallocations in base64 encoding. [#6951](https://github.com/Project-OSRM/osrm-backend/pull/6951)

scripts/ci/e2e_benchmark.py

@@ -86,9 +86,9 @@ def bootstrap_confidence_interval(data, num_samples=1000, confidence_level=0.95)
     mean = np.mean(means)
     return mean, lower_bound, upper_bound
 
-def calculate_confidence_interval(data):
+def calculate_confidence_interval(data, min_is_best=True):
     mean, lower, upper = bootstrap_confidence_interval(data)
-    min_value = np.min(data)
+    min_value = np.min(data) if min_is_best else np.max(data)
     return mean, (upper - lower) / 2, min_value
 
 
@@ -117,7 +117,7 @@ def main():
 
 
     total_time, total_ci, total_best = calculate_confidence_interval(np.sum(all_times, axis=1))
-    ops_per_sec, ops_per_sec_ci, ops_per_sec_best = calculate_confidence_interval(float(all_times.shape[1]) / np.sum(all_times / 1000, axis=1))
+    ops_per_sec, ops_per_sec_ci, ops_per_sec_best = calculate_confidence_interval(float(all_times.shape[1]) / np.sum(all_times / 1000, axis=1), min_is_best=False)
     min_time, min_ci, _ = calculate_confidence_interval(np.min(all_times, axis=1))
     mean_time, mean_ci, _ = calculate_confidence_interval(np.mean(all_times, axis=1))
     median_time, median_ci, _ = calculate_confidence_interval(np.median(all_times, axis=1))

src/benchmarks/bench.cpp

@@ -134,6 +134,7 @@ struct ConfidenceInterval
     double mean;
     double confidence;
     double min;
+    double max;
 };
 
 // Helper function to calculate the bootstrap confidence interval
@@ -161,8 +162,10 @@ ConfidenceInterval confidenceInterval(const std::vector<double> &data,
     double upper_bound = means[(int)((1 + confidence_level) / 2 * num_samples)];
     double mean = std::accumulate(means.begin(), means.end(), 0.0) / means.size();
 
-    ConfidenceInterval ci = {
-        mean, (upper_bound - lower_bound) / 2, *std::min_element(data.begin(), data.end())};
+    ConfidenceInterval ci = {mean,
+                             (upper_bound - lower_bound) / 2,
+                             *std::min_element(data.begin(), data.end()),
+                             *std::max_element(data.begin(), data.end())};
     return ci;
 }
 
@@ -260,7 +263,7 @@ std::ostream &operator<<(std::ostream &os, Statistics &statistics)
     ConfidenceInterval ops_ci = statistics.ops_per_sec();
 
     os << "ops: " << ops_ci.mean << " ± " << ops_ci.confidence << " ops/s. "
-       << "best: " << ops_ci.min << "ops/s." << std::endl;
+       << "best: " << ops_ci.max << "ops/s." << std::endl;
     os << "total: " << total_ci.mean << " ± " << total_ci.confidence << "ms. "
        << "best: " << total_ci.min << "ms." << std::endl;
     os << "avg: " << mean_ci.mean << " ± " << mean_ci.confidence << "ms" << std::endl;

src/engine/routing_algorithms/map_matching.cpp

@@ -401,6 +401,7 @@ SubMatchingList mapMatching(SearchEngineData<Algorithm> &engine_working_data,
         auto trace_distance = 0.0;
         matching.nodes.reserve(reconstructed_indices.size());
         matching.indices.reserve(reconstructed_indices.size());
+        matching.alternatives_count.reserve(reconstructed_indices.size());
         for (const auto &idx : reconstructed_indices)
         {
             const auto timestamp_index = idx.first;
@@ -428,7 +429,7 @@ SubMatchingList mapMatching(SearchEngineData<Algorithm> &engine_working_data,
 
         matching.confidence = confidence(trace_distance, matching_distance);
 
-        sub_matchings.push_back(matching);
+        sub_matchings.emplace_back(std::move(matching));
         sub_matching_begin = sub_matching_end;
     }