File size: 4,845 Bytes
21f3d42 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 |
// Copyright 2021 Google LLC
//
// Licensed 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 "sparse_matmul/os/coop_threads.h"
#include <algorithm>
#include <atomic>
#include <numeric>
#include "gtest/gtest.h"
TEST(Threads, LaunchThreads) {
std::atomic<int> counter(0);
auto f = [&](csrblocksparse::SpinBarrier* barrier, int tid) {
counter.fetch_add(tid);
};
const int kNumThreads = 10;
csrblocksparse::LaunchOnThreadsWithBarrier(kNumThreads, f);
ASSERT_EQ(counter.load(), kNumThreads * (kNumThreads - 1) / 2);
}
TEST(Threads, SpinBarrier) {
const int kNumThreads = 10;
std::vector<int> tids(kNumThreads, 0);
std::vector<std::vector<int>> expected;
for (int i = 0; i < 10; ++i) {
expected.emplace_back(kNumThreads);
std::iota(expected.back().begin(), expected.back().end(), 0);
std::transform(expected.back().begin(), expected.back().end(),
expected.back().begin(),
[i](int x) -> int { return (i + 1) * x; });
}
auto f = [&](csrblocksparse::SpinBarrier* barrier, int tid) {
for (int i = 0; i < 10; ++i) {
tids[tid] += tid;
barrier->barrier();
EXPECT_EQ(tids, expected[i]);
barrier->barrier();
}
};
csrblocksparse::LaunchOnThreadsWithBarrier(kNumThreads, f);
}
TEST(Threads, ProducerConsumer) {
constexpr int kNumThreads = 4;
constexpr int kNumIterations = 10;
std::vector<int> shared_data(kNumThreads, 0);
std::vector<std::pair<int, int>> expected;
for (int i = 1; i <= kNumIterations; ++i) {
// Execute the parallel work sequentially.
// Last two threads write their id * iteration.
std::pair<int, int> inputs =
std::make_pair((kNumThreads - 2) * i, (kNumThreads - 1) * i);
// First two threads compute sum and difference of those values.
std::pair<int, int> diffs = std::make_pair(inputs.first + inputs.second,
inputs.first - inputs.second);
// Last two threads compute sum and product.
std::pair<int, int> sums =
std::make_pair(diffs.first + diffs.second, diffs.first * diffs.second);
// First two threads compute product and difference of those values.
expected.emplace_back(
std::make_pair(sums.first * sums.second, sums.first - sums.second));
// Last two threads will check for the correct result.
}
csrblocksparse::ProducerConsumer first_pc(2, 2);
csrblocksparse::ProducerConsumer second_pc(2, 2);
csrblocksparse::ProducerConsumer third_pc(2, 2);
csrblocksparse::ProducerConsumer fourth_pc(2, 2);
auto f = [&](csrblocksparse::SpinBarrier* barrier, int tid) {
for (int i = 1; i <= kNumIterations; ++i) {
if (tid == kNumThreads - 2) {
// Last two threads write their id * iteration.
shared_data[tid] = tid * i;
first_pc.produce();
second_pc.consume();
// They then compute sum and product.
shared_data[tid] = shared_data[0] + shared_data[1];
third_pc.produce();
// They finally check the result.
fourth_pc.consume();
EXPECT_EQ(expected[i - 1].first, shared_data[0]) << "i=" << i;
} else if (tid == kNumThreads - 1) {
shared_data[tid] = tid * i;
first_pc.produce();
second_pc.consume();
shared_data[tid] = shared_data[0] * shared_data[1];
third_pc.produce();
fourth_pc.consume();
EXPECT_EQ(expected[i - 1].second, shared_data[1]) << "i=" << i;
} else if (tid == 0) {
// First two threads compute sum and difference.
first_pc.consume();
shared_data[tid] =
shared_data[kNumThreads - 2] + shared_data[kNumThreads - 1];
second_pc.produce();
// They then compute product and difference.
third_pc.consume();
shared_data[tid] =
shared_data[kNumThreads - 2] * shared_data[kNumThreads - 1];
fourth_pc.produce();
} else if (tid == 1) {
first_pc.consume();
shared_data[tid] =
shared_data[kNumThreads - 2] - shared_data[kNumThreads - 1];
second_pc.produce();
third_pc.consume();
shared_data[tid] =
shared_data[kNumThreads - 2] - shared_data[kNumThreads - 1];
fourth_pc.produce();
}
}
};
csrblocksparse::LaunchOnThreadsWithBarrier(kNumThreads, f);
}
|