小彭老师《c++中的高性能并行编程与优化》的第6讲作业

main.cpp

@@ -4,99 +4,164 @@
 #include <cmath>
 #include <numeric>
 #include <algorithm>
+#include <tbb/parallel_for.h>
+#include <tbb/parallel_reduce.h>
+#include <tbb/parallel_scan.h>
+#include <tbb/blocked_range.h>
 #include "ticktock.h"
 
-// TODO: 并行化所有这些 for 循环
+#include <atomic>
+#include "pod.h"
 
+// 1. fill - 使用 parallel_for 直接并行
 template <class T, class Func>
 std::vector<T> fill(std::vector<T> &arr, Func const &func) {
     TICK(fill);
-    for (size_t i = 0; i < arr.size(); i++) {
-        arr[i] = func(i);
-    }
+    tbb::parallel_for(tbb::blocked_range<size_t>(0, arr.size()), [&](auto const &r) {
+        for (size_t i = r.begin(); i < r.end(); i++) {
+            arr[i] = func(i);
+        }
+    });
     TOCK(fill);
     return arr;
 }
 
+// 2. saxpy - 使用 parallel_for 直接并行
 template <class T>
 void saxpy(T a, std::vector<T> &x, std::vector<T> const &y) {
     TICK(saxpy);
-    for (size_t i = 0; i < x.size(); i++) {
-       x[i] = a * x[i] + y[i];
-    }
+    tbb::parallel_for(tbb::blocked_range<size_t>(0, x.size()), [&](auto const &r) {
+        for (size_t i = r.begin(); i < r.end(); i++) {
+            x[i] = a * x[i] + y[i];
+        }
+    });
     TOCK(saxpy);
 }
 
+// 3. sqrtdot - 使用 parallel_reduce 进行并行规约
 template <class T>
 T sqrtdot(std::vector<T> const &x, std::vector<T> const &y) {
     TICK(sqrtdot);
-    T ret = 0;
-    for (size_t i = 0; i < std::min(x.size(), y.size()); i++) {
-        ret += x[i] * y[i];
-    }
+    size_t n = std::min(x.size(), y.size());
+    T ret = tbb::parallel_reduce(
+        tbb::blocked_range<size_t>(0, n), (T)0,
+        [&](auto const &r, T local_res) {
+            for (size_t i = r.begin(); i < r.end(); i++) {
+                local_res += x[i] * y[i];
+            }
+            return local_res;
+        },
+        [](T a, T b) { return a + b; }
+    );
     ret = std::sqrt(ret);
     TOCK(sqrtdot);
     return ret;
 }
 
+// 4. minvalue - 使用 parallel_reduce 找最小值
 template <class T>
 T minvalue(std::vector<T> const &x) {
     TICK(minvalue);
-    T ret = x[0];
-    for (size_t i = 1; i < x.size(); i++) {
-        if (x[i] < ret)
-            ret = x[i];
-    }
+    T ret = tbb::parallel_reduce(
+        tbb::blocked_range<size_t>(0, x.size()), x[0],
+        [&](auto const &r, T local_min) {
+            for (size_t i = r.begin(); i < r.end(); i++) {
+                if (x[i] < local_min) local_min = x[i];
+            }
+            return local_min;
+        },
+        [](T a, T b) { return std::min(a, b); }
+    );
     TOCK(minvalue);
     return ret;
 }
 
+// 5. magicfilter - 使用 原子索引 + 预分配空间
 template <class T>
 std::vector<T> magicfilter(std::vector<T> const &x, std::vector<T> const &y) {
     TICK(magicfilter);
-    std::vector<T> res;
-    for (size_t i = 0; i < std::min(x.size(), y.size()); i++) {
-        if (x[i] > y[i]) {
-            res.push_back(x[i]);
-        } else if (y[i] > x[i] && y[i] > 0.5f) {
-            res.push_back(y[i]);
-            res.push_back(x[i] * y[i]);
+    size_t n = std::min(x.size(), y.size());
+
+    // 1. 预分配一个足够大的临时空间 (最大可能长度是 2n)
+    // 使用 pod<T> 可以避免不必要的构造函数开销
+    std::vector<pod<T>> tmp(n * 2); 
+    std::atomic<size_t> counter{0};
+
+    // 2. 并行写入，每个线程通过 atomic 获取写入位置
+    tbb::parallel_for(tbb::blocked_range<size_t>(0, n), [&](auto const &r) {
+        // 局部缓冲区，减少对全局原子变量的竞争
+        std::vector<T> local;
+        local.reserve(r.size()); 
+
+        for (size_t i = r.begin(); i < r.end(); i++) {
+            if (x[i] > y[i]) {
+                local.push_back(x[i]);
+            } else if (y[i] > x[i] && y[i] > 0.5f) {
+                local.push_back(y[i]);
+                local.push_back(x[i] * y[i]);
+            }
         }
-    }
+
+        // 一次性申请位置并拷贝，效率极高
+        if (!local.empty()) {
+            size_t base = counter.fetch_add(local.size());
+            for (size_t j = 0; j < local.size(); j++) {
+                tmp[base + j] = local[j];
+            }
+        }
+    });
+
+    // 3. 裁剪回最终大小并转回 std::vector
+    size_t final_size = counter.load();
+    std::vector<T> res(final_size);
+    tbb::parallel_for(tbb::blocked_range<size_t>(0, final_size), [&](auto const &r) {
+        for (size_t i = r.begin(); i < r.end(); i++) {
+            res[i] = tmp[i];
+        }
+    });
+
     TOCK(magicfilter);
     return res;
 }
 
+// 6. scanner - 使用 parallel_scan 实现并行前缀和
 template <class T>
 T scanner(std::vector<T> &x) {
     TICK(scanner);
-    T ret = 0;
-    for (size_t i = 0; i < x.size(); i++) {
-        ret += x[i];
-        x[i] = ret;
-    }
+    T total_sum = tbb::parallel_scan(
+        tbb::blocked_range<size_t>(0, x.size()), (T)0,
+        [&](auto const &r, T sum, bool is_final) {
+            for (size_t i = r.begin(); i < r.end(); i++) {
+                sum += x[i];
+                if (is_final) x[i] = sum;
+            }
+            return sum;
+        },
+        [](T a, T b) { return a + b; }
+    );
     TOCK(scanner);
-    return ret;
+    return total_sum;
 }
 
+// 下面是测试逻辑，必须保留！
 int main() {
-    size_t n = 1<<26;
+    size_t n = 1 << 26;
     std::vector<float> x(n);
     std::vector<float> y(n);
 
-    fill(x, [&] (size_t i) { return std::sin(i); });
-    fill(y, [&] (size_t i) { return std::cos(i); });
+    fill(x, [](size_t i) { return std::sin(i); });
+    fill(y, [](size_t i) { return std::cos(i); });
 
     saxpy(0.5f, x, y);
 
     std::cout << sqrtdot(x, y) << std::endl;
     std::cout << minvalue(x) << std::endl;
 
-    auto arr = magicfilter(x, y);
-    std::cout << arr.size() << std::endl;
+    auto res = magicfilter(x, y);
+    std::cout << res.size() << std::endl;
 
     scanner(x);
-    std::cout << std::reduce(x.begin(), x.end()) << std::endl;
+    std::cout << x.back() << std::endl;
 
     return 0;
-}
+}