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#include "tensorflow/core/util/work_sharder.h"
#include "tensorflow/core/platform/logging.h"
#include "tensorflow/core/lib/core/threadpool.h"
#include "tensorflow/core/platform/port.h"
#include "tensorflow/core/platform/test_benchmark.h"
#include <gtest/gtest.h>
namespace tensorflow {
namespace {
void RunSharding(int64 num_workers, int64 total, int64 cost_per_unit) {
thread::ThreadPool threads(Env::Default(), "test", 16);
mutex mu;
int64 num_shards = 0;
int64 num_done_work = 0;
std::vector<bool> work(total, false);
Shard(num_workers, &threads, total, cost_per_unit,
[&mu, &num_shards, &num_done_work, &work](int start, int limit) {
VLOG(1) << "Shard [" << start << "," << limit << ")";
mutex_lock l(mu);
++num_shards;
for (; start < limit; ++start) {
EXPECT_FALSE(work[start]); // No duplicate
++num_done_work;
work[start] = true;
}
});
EXPECT_LE(num_shards, num_workers + 1);
EXPECT_EQ(num_done_work, total);
LOG(INFO) << num_workers << " " << total << " " << cost_per_unit << " "
<< num_shards;
}
TEST(Shard, Basic) {
for (auto workers : {0, 1, 2, 3, 5, 7, 10, 11, 15, 100, 1000}) {
for (auto total : {0, 1, 7, 10, 64, 100, 256, 1000, 9999}) {
for (auto cost_per_unit : {0, 1, 11, 102, 1003, 10005, 1000007}) {
RunSharding(workers, total, cost_per_unit);
}
}
}
}
void BM_Sharding(int iters, int arg) {
thread::ThreadPool threads(Env::Default(), "test", 16);
const int64 total = 1LL << 30;
auto lambda = [](int64 start, int64 limit) {};
auto work = std::cref(lambda);
for (; iters > 0; iters -= arg) {
Shard(arg - 1, &threads, total, 1, work);
}
}
BENCHMARK(BM_Sharding)->Range(1, 128);
} // namespace
} // namespace tensorflow
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