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ANN bench fix latency measurement overhead #2084

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merged 5 commits into from
Jan 24, 2024
Merged

ANN bench fix latency measurement overhead #2084

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508c57e
Decrease latency measurement overhead
tfeher Jan 9, 2024
6efd970
Fix timer start, use kAvgIteratins for latency
tfeher Jan 23, 2024
ec33ce6
Update cpp/bench/ann/src/common/benchmark.hpp
achirkin Jan 24, 2024
1b3c8ae
Merge branch 'branch-24.02' into ann_bench_timers
achirkin Jan 24, 2024
6241439
Fix copyright year
tfeher Jan 24, 2024
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18 changes: 8 additions & 10 deletions cpp/bench/ann/src/common/benchmark.hpp
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Original file line number Diff line number Diff line change
@@ -1,5 +1,5 @@
/*
* Copyright (c) 2023, NVIDIA CORPORATION.
* Copyright (c) 2023-2024, NVIDIA CORPORATION.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
Expand Down Expand Up @@ -287,11 +287,11 @@ void bench_search(::benchmark::State& state,
std::make_shared<buf<std::size_t>>(current_algo_props->query_memory_type, k * query_set_size);

cuda_timer gpu_timer;
auto start = std::chrono::high_resolution_clock::now();
{
nvtx_case nvtx{state.name()};

auto algo = dynamic_cast<ANN<T>*>(current_algo.get())->copy();
auto algo = dynamic_cast<ANN<T>*>(current_algo.get())->copy();
auto start = std::chrono::high_resolution_clock::now();
for (auto _ : state) {
[[maybe_unused]] auto ntx_lap = nvtx.lap();
[[maybe_unused]] auto gpu_lap = gpu_timer.lap();
Expand All @@ -314,17 +314,15 @@ void bench_search(::benchmark::State& state,

queries_processed += n_queries;
}
auto end = std::chrono::high_resolution_clock::now();
auto duration = std::chrono::duration_cast<std::chrono::duration<double>>(end - start).count();
if (state.thread_index() == 0) { state.counters.insert({{"end_to_end", duration}}); }
state.counters.insert({"Latency", {duration, benchmark::Counter::kAvgIterations}});
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I have also applied Artem's suggestion to store latency values with benchmark::Counter::kAvgIterations marker.

Earlier we manually divided by number of iterations and let gbench average over threads using kAvgThreads. Since iterations are counted as total iterations performed by all threads, using kAviIterations leads to the same results (without manual divisions by iterations).

}
auto end = std::chrono::high_resolution_clock::now();
auto duration = std::chrono::duration_cast<std::chrono::duration<double>>(end - start).count();
if (state.thread_index() == 0) { state.counters.insert({{"end_to_end", duration}}); }
state.counters.insert(
{"Latency", {duration / double(state.iterations()), benchmark::Counter::kAvgThreads}});

state.SetItemsProcessed(queries_processed);
if (cudart.found()) {
double gpu_time_per_iteration = gpu_timer.total_time() / (double)state.iterations();
state.counters.insert({"GPU", {gpu_time_per_iteration, benchmark::Counter::kAvgThreads}});
state.counters.insert({"GPU", {gpu_timer.total_time(), benchmark::Counter::kAvgIterations}});
}

// This will be the total number of queries across all threads
Expand Down