Intel® oneAPI Threading Building Blocks Developer Guide and API Reference
Advanced Topic: Other Kinds of Iteration Spaces
The examples so far have used the class blocked_range<T> to specify ranges. This class is useful in many situations, but it does not fit every situation. You can use Intel® oneAPI Threading Building Blocks (oneTBB) to define your own iteration space objects. The object must specify how it can be split into subspaces by providing a basic splitting constructor, an optional proportional splitting constructor, and two predicate methods. If your class is called R, the methods and constructors should be as follows:
class R {
// True if range is empty
bool empty() const;
// True if range can be split into non-empty subranges
bool is_divisible() const;
// Splits r into subranges r and *this
R( R& r, split );
// (optional) Splits r into subranges r and *this in proportion p
R( R& r, proportional_split p );
...
};The method empty should return true if the range is empty. The method is_divisible should return true if the range can be split into two non-empty subspaces, and such a split is worth the overhead. The basic splitting constructor should take two arguments:
The first of type R
The second of type oneapi::tbb::split
The second argument is not used; it serves only to distinguish the constructor from an ordinary copy constructor. The basic splitting constructor should attempt to split r roughly into two halves, and update r to be the first half, and set the constructed object as the second half.
Unlike the basic splitting constructor, the proportional splitting constructor is optional and takes the second argument of type oneapi::tbb::proportional_split. The type has methods left and right that return the values of the proportion. These values should be used to split r accordingly, so that the updated r corresponds to the left part of the proportion, and the constructed object corresponds to the right part.
Both splitting constructors should guarantee that the updated r part and the constructed object are not empty. The parallel algorithm templates call the splitting constructors on r only if r.is_divisible is true.
The iteration space does not have to be linear. Look at oneapi/tbb/blocked_range2d.h for an example of a range that is two-dimensional. Its splitting constructor attempts to split the range along its longest axis. When used with parallel_for, it causes the loop to be “recursively blocked” in a way that improves cache usage. This nice cache behavior means that using parallel_for over a blocked_range2d<T> can make a loop run faster than the sequential equivalent, even on a single processor.
The blocked_range2d allows you to use different value types for its first dimension, rows, and the second one, columns. That means you can combine indexes, pointers, and iterators into a joint iteration space. Use the methods rows() and cols() to obtain blocked_range objects that represent the respective dimensions.
The blocked_range3d class template extends this approach to 3D by adding pages() as the first dimension, followed by rows() and cols().
The blocked_nd_range<T,N> class template represents a blocked iteration space of any dimensionality. Unlike the previously described 2D and 3D ranges, blocked_nd_range uses the same value type for all its axes, and its constructor requires you to pass N instances of blocked_range<T> instead of individual boundary values. The change in the naming pattern reflects these differences.
Example of a Multidimensional Iteration Space
The example demonstrates calculation of a 3-dimensional filter over the pack of feature maps.
The convolution3d function iterates over the output cells, assigning to each cell the result of the kernel3d function that combines the values from a range in the feature maps.
To run the computation in parallel, tbb::parallel_for is called with tbb::blocked_nd_range<int,3> as an argument. The body function processes the received 3D subrange in nested loops, using the method dim to get the loop boundaries for each dimension.
#include "oneapi/tbb/blocked_nd_range.h"
#include "oneapi/tbb/parallel_for.h"
template<typename Features>
float kernel3d(const Features& feature_maps, int i, int j, int k,
int kernel_length, int kernel_width, int kernel_height) {
float result = 0.f;
for (int feature_i = i; feature_i < i + kernel_length; ++feature_i)
for (int feature_j = j; feature_j < j + kernel_width; ++feature_j)
for (int feature_k = k; feature_k < k + kernel_width; ++feature_k)
result += feature_maps[feature_i][feature_j][feature_k];
return result;
}
template<typename Features, typename Output>
void convolution3d(const Features& feature_maps, Output& out,
int out_length, int out_width, int out_heigth,
int kernel_length, int kernel_width, int kernel_height) {
using range_t = oneapi::tbb::blocked_nd_range<int, 3>;
oneapi::tbb::parallel_for(
range_t({0, out_length}, {0, out_width}, {0, out_heigth}),
[&](const range_t& out_range) {
auto out_x = out_range.dim(0);
auto out_y = out_range.dim(1);
auto out_z = out_range.dim(2);
for (int i = out_x.begin(); i < out_x.end(); ++i)
for (int j = out_y.begin(); j < out_y.end(); ++j)
for (int k = out_z.begin(); k < out_z.end(); ++k)
out[i][j][k] = kernel3d(feature_maps, i, j, k,
kernel_length, kernel_width, kernel_height);
}
);
}