OpenMP:编写与HDF5同步的数据

我当前正在进行一个项目，其中必须使用HDF5创建一个大数据集。现在，天真的实施都很好，花花公子，但非常缓慢。慢部分是在计算中(比写的慢10倍(，我无法再加速，但是也许可以并行化。

我想我可以使用一个简单的 #pragma op平行于，但是出于速度原因， dataspace.write(..(方法应该弄清楚(也许没关系(。例如，请参见此图片。

应该注意的是，由于维度，写函数使用与缓冲区相同大小的块状布局(实际上围绕1MB(

/*
------------NAIVE IMPLEMENTATION-----------------
|T:<calc0><W0><calc1><W1><calc2><W2>............|
|-----------------------------------------------|
|----------PARALLEL IMPLEMENTATION--------------|
|-----------------------------------------------|
|T0:<calc0----><W0><calc4>.....<W4>.............|
|T1:<calc1---->....<W1><calc5->....<W5>.........|
|T2:<calc2--->.........<W2>calc6-->....<W6>.....|
|T3:<calc3----->...........<W3><calc7-->...<W7>.|
------------DIFFERENT IMPLEMENTATION-------------
i.e.: Queuesize=4
T0:.......<W0><W1><W2><W3><W4><W5><W6>..........|
T1:<calc0><calc3>.....<calc6>...................|
T2:<calc1>....<calc4>.....<calc7>...............|
T3:<calc2>........<calc5>.....<calc8>...........|

T          Thread
<calcn---> Calculation time
<Wn>       Write data n. Order *important*
.          Waiting
*/

codeexample：

#include <chrono>
#include <cmath>
#include <iostream>
#include <memory>
double calculate(float *buf, const struct options *opts) {
  // dummy function just to get a time reference
  double res = 0;
  for (size_t i = 0; i < 10000; i++)
    res += std::sin(i);
  return 1 / (1 + res);
}
struct options {
  size_t idx[6];
};
class Dataspace {
public:
  void selectHyperslab(){}; // selects region in disk space
  void write(float *buf){}; // write buf to selected disk space
};
int main() {
  size_t N = 6;
  size_t dims[6] = {4 * N, 4 * N, 4 * N, 4 * N, 4 * N, 4 * N},
         buf_offs[6] = {4, 4, 4, 4, 4, 4};
  // dims: size of each dimension, multiple of 4
  // buf_offs: size of buffer in each dimension
  // Calcuate buffer size and allocate
  // the size of the buffer is usually around 1Mb
  // and not a float but a compund datatype
  size_t buf_size = buf_offs[0];
  for (auto off : buf_offs)
    buf_size *= off;
  std::unique_ptr<float[]> buf{new float[buf_size]};
  struct options opts;        // options parameters, passed to calculation fun
  struct Dataspace dataspace; // dummy Dataspace. Supplied by HDF5
  size_t i = 0;
  size_t idx0, idx1, idx2, idx3, idx4, idx5;
  auto t_start = std::chrono::high_resolution_clock::now();
  std::cout << "[START]" << std::endl;
  for (idx0 = 0; idx0 < dims[0]; idx0 += buf_offs[0])
    for (idx1 = 0; idx1 < dims[1]; idx1 += buf_offs[1])
      for (idx2 = 0; idx2 < dims[2]; idx2 += buf_offs[2])
        for (idx3 = 0; idx3 < dims[3]; idx3 += buf_offs[3])
          for (idx4 = 0; idx4 < dims[4]; idx4 += buf_offs[4])
            for (idx5 = 0; idx5 < dims[5]; idx5 += buf_offs[5]) {
              i++;
              opts.idx[0] = idx0;
              opts.idx[1] = idx1;
              opts.idx[2] = idx2;
              opts.idx[3] = idx3;
              opts.idx[4] = idx4;
              opts.idx[5] = idx5;
              dataspace.selectHyperslab(/**/); // function from HDF5
              calculate(buf.get(), &opts);     // populate buf with data
              dataspace.write(buf.get());      // has to be sequential
            }
  std::cout << "[DONE] " << i << " calls" << std::endl;
  std::chrono::duration<double> diff =
      std::chrono::high_resolution_clock::now() - t_start;
  std::cout << "Time: " << diff.count() << std::endl;
  return 0;
}

代码应立即起作用。

我已经快速研究了OpenMP，但我还不能缠绕着头。谁能给我一个提示/工作例子？我对并行化不好，但是作家读物的作品不会进行bufferqueue工作吗？还是无论如何使用OpenMP Overkill和Pthreads就足够了？任何帮助都会受到赞赏，

欢呼