Parallel Algorithms

The Parallel Patterns Library (PPL) provides algorithms that concurrently perform work on collections of data. These algorithms resemble those provided by the C++ Standard Library.

The parallel algorithms are composed from existing functionality in the Concurrency Runtime. For example, the concurrency::parallel_for algorithm uses a concurrency::structured_task_group object to perform the parallel loop iterations. The parallel_for algorithm partitions work in an optimal way given the available number of computing resources.

Sections

• The parallel_for Algorithm

• The parallel_for_each Algorithm

• The parallel_invoke Algorithm

• The parallel_transform and parallel_reduce Algorithms

  • The parallel_transform Algorithm

  • The parallel_reduce Algorithm

  • Example: Performing Map and Reduce in Parallel

• Partitioning Work

• Parallel Sorting

  • Choosing a Sorting Algorithm

The parallel_for Algorithm

The concurrency::parallel_for algorithm repeatedly performs the same task in parallel. Each of these tasks is parameterized by an iteration value. This algorithm is useful when you have a loop body that does not share resources among iterations of that loop.

The parallel_for algorithm partitions tasks in an optimum way for parallel execution. It uses a work-stealing algorithm and range stealing to balance these partitions when workloads are unbalanced. When one loop iteration blocks cooperatively, the runtime redistributes the range of iterations that is assigned to the current thread to other threads or processors. Similarly, when a thread completes a range of iterations, the runtime redistributes work from other threads to that thread. The parallel_for algorithm also supports nested parallelism. When one parallel loop contains another parallel loop, the runtime coordinates processing resources between the loop bodies in an efficient way for parallel execution.

The parallel_for algorithm has several overloaded versions. The first version takes a start value, an end value, and a work function (a lambda expression, function object, or function pointer). The second version takes a start value, an end value, a value by which to step, and a work function. The first version of this function uses 1 as the step value. The remaining versions take partitioner objects, which enable you to specify how parallel_for should partition ranges among threads. Partitioners are explained in greater detail in the section Partitioning Work in this document.

You can convert many for loops to use parallel_for. However, the parallel_for algorithm differs from the for statement in the following ways:

• The parallel_for algorithm parallel_for does not execute the tasks in a pre-determined order.

• The parallel_for algorithm does not support arbitrary termination conditions. The parallel_for algorithm stops when the current value of the iteration variable is one less than last.

• The _Index_type type parameter must be an integral type. This integral type can be signed or unsigned.

• The loop iteration must be forward. The parallel_for algorithm throws an exception of type std::invalid_argument if the _Step parameter is less than 1.

• The exception-handling mechanism for the parallel_for algorithm differs from that of a for loop. If multiple exceptions occur simultaneously in a parallel loop body, the runtime propagates only one of the exceptions to the thread that called parallel_for. In addition, when one loop iteration throws an exception, the runtime does not immediately stop the overall loop. Instead, the loop is placed in the canceled state and the runtime discards any tasks that have not yet started. For more information about exception-handling and parallel algorithms, see Exception Handling.

Although the parallel_for algorithm does not support arbitrary termination conditions, you can use cancellation to stop all tasks. For more information about cancellation, see Cancellation in the PPL.

Example

The following example shows the basic structure of the parallel_for algorithm. This example prints to the console each value in the range [1, 5] in parallel.

// parallel-for-structure.cpp
// compile with: /EHsc
#include <ppl.h>
#include <array>
#include <sstream>
#include <iostream>

using namespace concurrency;
using namespace std;

int wmain()
{
   // Print each value from 1 to 5 in parallel.
   parallel_for(1, 6, [](int value) {
      wstringstream ss;
      ss << value << L' ';
      wcout << ss.str();
   });
}

This example produces the following sample output:

1 2 4 3 5

Because the parallel_for algorithm acts on each item in parallel, the order in which the values are printed to the console will vary.

For a complete example that uses the parallel_for algorithm, see How to: Write a parallel_for Loop.

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The parallel_for_each Algorithm

The concurrency::parallel_for_each algorithm performs tasks on an iterative container, such as those provided by the C++ Standard Library, in parallel. It uses the same partitioning logic that the parallel_for algorithm uses.

The parallel_for_each algorithm resembles the C++ Standard Library std::for_each algorithm, except that the parallel_for_each algorithm executes the tasks concurrently. Like other parallel algorithms, parallel_for_each does not execute the tasks in a specific order.

Although the parallel_for_each algorithm works on both forward iterators and random access iterators, it performs better with random access iterators.

Example

The following example shows the basic structure of the parallel_for_each algorithm. This example prints to the console each value in a std::array object in parallel.

// parallel-for-each-structure.cpp
// compile with: /EHsc
#include <ppl.h>
#include <array>
#include <sstream>
#include <iostream>

using namespace concurrency;
using namespace std;

int wmain()
{
   // Create an array of integer values.
   array<int, 5> values = { 1, 2, 3, 4, 5 };

   // Print each value in the array in parallel.
   parallel_for_each(begin(values), end(values), [](int value) {
      wstringstream ss;
      ss << value << L' ';
      wcout << ss.str();
   });
}
/* Sample output:
   5 4 3 1 2
*/

This example produces the following sample output:

4 5 1 2 3

Because the parallel_for_each algorithm acts on each item in parallel, the order in which the values are printed to the console will vary.

For a complete example that uses the parallel_for_each algorithm, see How to: Write a parallel_for_each Loop.

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The parallel_invoke Algorithm

The concurrency::parallel_invoke algorithm executes a set of tasks in parallel. It does not return until each task finishes. This algorithm is useful when you have several independent tasks that you want to execute at the same time.

The parallel_invoke algorithm takes as its parameters a series of work functions (lambda functions, function objects, or function pointers). The parallel_invoke algorithm is overloaded to take between two and ten parameters. Every function that you pass to parallel_invoke must take zero parameters.

Like other parallel algorithms, parallel_invoke does not execute the tasks in a specific order. The topic Task Parallelism explains how the parallel_invoke algorithm relates to tasks and task groups.

Example

The following example shows the basic structure of the parallel_invoke algorithm. This example concurrently calls the twice function on three local variables and prints the result to the console.

// parallel-invoke-structure.cpp
// compile with: /EHsc
#include <ppl.h>
#include <string>
#include <iostream>

using namespace concurrency;
using namespace std;

// Returns the result of adding a value to itself.
template <typename T>
T twice(const T& t) {
   return t + t;
}

int wmain()
{
   // Define several values.
   int n = 54;
   double d = 5.6;
   wstring s = L"Hello";

   // Call the twice function on each value concurrently.
   parallel_invoke(
      [&n] { n = twice(n); },
      [&d] { d = twice(d); },
      [&s] { s = twice(s); }
   );

   // Print the values to the console.
   wcout << n << L' ' << d << L' ' << s << endl;
}

This example produces the following output:

108 11.2 HelloHello

For complete examples that use the parallel_invoke algorithm, see How to: Use parallel_invoke to Write a Parallel Sort Routine and How to: Use parallel_invoke to Execute Parallel Operations.

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The parallel_transform and parallel_reduce Algorithms

The concurrency::parallel_transform and concurrency::parallel_reduce algorithms are parallel versions of the C++ Standard Library algorithms std::transform and std::accumulate, respectively. The Concurrency Runtime versions behave like the C++ Standard Library versions except that the operation order is not determined because they execute in parallel. Use these algorithms when you work with a set that is large enough to get performance and scalability benefits from being processed in parallel.

The parallel_transform Algorithm

You can use the parallel transform algorithm to perform many data parallelization operations. For example, you can:

• Adjust the brightness of an image, and perform other image processing operations.

• Sum or take the dot product between two vectors, and perform other numeric calculations on vectors.

• Perform 3-D ray tracing, where each iteration refers to one pixel that must be rendered.

The following example shows the basic structure that is used to call the parallel_transform algorithm. This example negates each element of a std::vector object in two ways. The first way uses a lambda expression. The second way uses std::negate, which derives from std::unary_function.

// basic-parallel-transform.cpp
// compile with: /EHsc
#include <ppl.h>
#include <random>

using namespace concurrency;
using namespace std;

int wmain()
{
    // Create a large vector that contains random integer data.
    vector<int> values(1250000);
    generate(begin(values), end(values), mt19937(42));

    // Create a vector to hold the results.
    // Depending on your requirements, you can also transform the 
    // vector in-place.
    vector<int> results(values.size());

    // Negate each element in parallel.
    parallel_transform(begin(values), end(values), begin(results), [](int n) {
        return -n;
    });

    // Alternatively, use the negate class to perform the operation.
    parallel_transform(begin(values), end(values), begin(values), negate<int>());
}

The parallel_transform algorithm has two overloads. The first overload takes one input range and a unary function. The unary function can be a lambda expression that takes one argument, a function object, or a type that derives from unary_function. The second overload takes two input ranges and a binary function. The binary function can be a lambda expression that takes two arguments, a function object, or a type that derives from std::binary_function. The following example illustrates these differences.

//
// Demonstrate use of parallel_transform together with a unary function.

// This example uses a lambda expression.
parallel_transform(begin(values), end(values), 
    begin(results), [](int n) { 
        return -n;
    });

// Alternatively, use the negate class:
parallel_transform(begin(values), end(values), 
    begin(results), negate<int>());

//
// Demonstrate use of parallel_transform together with a binary function.

// This example uses a lambda expression.
parallel_transform(begin(values), end(values), begin(results), 
    begin(results), [](int n, int m) {
        return n * m;
    });

// Alternatively, use the multiplies class:
parallel_transform(begin(values), end(values), begin(results), 
    begin(results), multiplies<int>());

The parallel_reduce Algorithm

The parallel_reduce algorithm is useful when you have a sequence of operations that satisfy the associative property. (This algorithm does not require the commutative property.) Here are some of the operations that you can perform with parallel_reduce:

• Multiply sequences of matrices to produce a matrix.

• Multiply a vector by a sequence of matrices to produce a vector.

• Compute the length of a sequence of strings.

• Combine a list of elements, such as strings, into one element.

The following basic example shows how to use the parallel_reduce algorithm to combine a sequence of strings into one string. As with the examples for parallel_transform, performance gains are not expected in this basic example.

// basic-parallel-reduce.cpp
// compile with: /EHsc
#include <ppl.h>
#include <iostream>
#include <string>
#include <vector>

using namespace concurrency;
using namespace std;

int wmain()
{
  // Create a vector of strings.
  vector<wstring> words{
      L"Lorem ",
      L"ipsum ",
      L"dolor ",
      L"sit ",
      L"amet, ",
      L"consectetur ",
      L"adipiscing ",
      L"elit."};
      
  // Reduce the vector to one string in parallel.
  wcout << parallel_reduce(begin(words), end(words), wstring()) << endl;
}

/* Output:
   Lorem ipsum dolor sit amet, consectetur adipiscing elit.
*/

In many cases, you can think of parallel_reduce as shorthand for the use of the parallel_for_each algorithm together with the concurrency::combinable class.

Example: Performing Map and Reduce in Parallel

A map operation applies a function to each value in a sequence. A reduce operation combines the elements of a sequence into one value. You can use the C++ Standard Library std::transform and std::accumulate functions to perform map and reduce operations. However, for many problems, you can use the parallel_transform algorithm to perform the map operation in parallel and the parallel_reduce algorithm perform the reduce operation in parallel.

The following example compares the time that it takes to compute the sum of prime numbers serially and in parallel. The map phase transforms non-prime values to 0 and the reduce phase sums the values.

// parallel-map-reduce-sum-of-primes.cpp
// compile with: /EHsc
#include <windows.h>
#include <ppl.h>
#include <array>
#include <numeric>
#include <iostream>

using namespace concurrency;
using namespace std;

// Calls the provided work function and returns the number of milliseconds 
// that it takes to call that function.
template <class Function>
__int64 time_call(Function&& f)
{
   __int64 begin = GetTickCount();
   f();
   return GetTickCount() - begin;
}

// Determines whether the input value is prime.
bool is_prime(int n)
{
   if (n < 2)
      return false;
   for (int i = 2; i < n; ++i)
   {
      if ((n % i) == 0)
         return false;
   }
   return true;
}

int wmain()
{   
   // Create an array object that contains 200000 integers.
   array<int, 200000> a;

   // Initialize the array such that a[i] == i.
   iota(begin(a), end(a), 0);

   int prime_sum;
   __int64 elapsed;

   // Compute the sum of the numbers in the array that are prime.
   elapsed = time_call([&] {
       transform(begin(a), end(a), begin(a), [](int i) { 
         return is_prime(i) ? i : 0; 
      });
      prime_sum = accumulate(begin(a), end(a), 0);
   });   
   wcout << prime_sum << endl;   
   wcout << L"serial time: " << elapsed << L" ms" << endl << endl;

   // Now perform the same task in parallel.
   elapsed = time_call([&] {
      parallel_transform(begin(a), end(a), begin(a), [](int i) { 
         return is_prime(i) ? i : 0; 
      });
      prime_sum = parallel_reduce(begin(a), end(a), 0);
   });
   wcout << prime_sum << endl;
   wcout << L"parallel time: " << elapsed << L" ms" << endl << endl;
}
/* Sample output:
   1709600813
   serial time: 7406 ms
   
   1709600813
   parallel time: 1969 ms
*/

For another example that performs a map and reduce operation in parallel, see How to: Perform Map and Reduce Operations in Parallel.

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Partitioning Work

To parallelize an operation on a data source, an essential step is to partition the source into multiple sections that can be accessed concurrently by multiple threads. A partitioner specifies how a parallel algorithm should partition ranges among threads. As explained previously in this document, the PPL uses a default partitioning mechanism that creates an initial workload and then uses a work-stealing algorithm and range stealing to balance these partitions when workloads are unbalanced. For example, when one loop iteration completes a range of iterations, the runtime redistributes work from other threads to that thread. However, for some scenarios, you might want to specify a different partitioning mechanism that is better suited to your problem.

The parallel_for, parallel_for_each, and parallel_transform algorithms provide overloaded versions that take an additional parameter, _Partitioner. This parameter defines the partitioner type that divides work. Here are the kinds of partitioners that the PPL defines:

concurrency::affinity_partitioner
Divides work into a fixed number of ranges (typically the number of worker threads that are available to work on the loop). This partitioner type resembles static_partitioner, but improves cache affinity by the way it maps ranges to worker threads. This partitioner type can improve performance when a loop is executed over the same data set multiple times (such as a loop within a loop) and the data fits in cache. This partitioner does not fully participate in cancellation. It also does not use cooperative blocking semantics and therefore cannot be used with parallel loops that have a forward dependency.

concurrency::auto_partitioner
Divides work into an initial number of ranges (typically the number of worker threads that are available to work on the loop). The runtime uses this type by default when you do not call an overloaded parallel algorithm that takes a _Partitioner parameter. Each range can be divided into sub-ranges, and thereby enables load balancing to occur. When a range of work completes, the runtime redistributes sub-ranges of work from other threads to that thread. Use this partitioner if your workload does not fall under one of the other categories or you require full support for cancellation or cooperative blocking.

concurrency::simple_partitioner
Divides work into ranges such that each range has at least the number of iterations that are specified by the given chunk size. This partitioner type participates in load balancing; however, the runtime does not divide ranges into sub-ranges. For each worker, the runtime checks for cancellation and performs load-balancing after _Chunk_size iterations complete.

concurrency::static_partitioner
Divides work into a fixed number of ranges (typically the number of worker threads that are available to work on the loop). This partitioner type can improve performance because it does not use work-stealing and therefore has less overhead. Use this partitioner type when each iteration of a parallel loop performs a fixed and uniform amount of work and you do not require support for cancellation or forward cooperative blocking.

Typically, these partitioners are used in the same way, except for affinity_partitioner. Most partitioner types do not maintain state and are not modified by the runtime. Therefore you can create these partitioner objects at the call site, as shown in the following example.

// static-partitioner.cpp
// compile with: /EHsc
#include <ppl.h>

using namespace concurrency;

void DoWork(int n)
{
    // TODO: Perform a fixed amount of work...
}

int wmain()
{
    // Use a static partitioner to perform a fixed amount of parallel work.
    parallel_for(0, 100000, [](int n) {
        DoWork(n);
    }, static_partitioner());
}

However, you must pass an affinity_partitioner object as a non-const, l-value reference so that the algorithm can store state for future loops to reuse. The following example shows a basic application that performs the same operation on a data set in parallel multiple times. The use of affinity_partitioner can improve performance because the array is likely to fit in cache.

// affinity-partitioner.cpp
// compile with: /EHsc
#include <ppl.h>
#include <array>

using namespace concurrency;
using namespace std;

int wmain()
{
    // Create an array and fill it with zeroes.
    array<unsigned char, 8 * 1024> data;
    data.fill(0);

    // Use an affinity partitioner to perform parallel work on data
    // that is likely to remain in cache.
    // We use the same affinitiy partitioner throughout so that the 
    // runtime can schedule work to occur at the same location for each 
    // iteration of the outer loop.

    affinity_partitioner ap;
    for (int i = 0; i < 100000; i++)
    {
        parallel_for_each(begin(data), end(data), [](unsigned char& c)
        {
            c++;
        }, ap);
    }
}

The best way to determine whether to use a partitioner in any given scenario is to experiment and measure how long it takes operations to complete under representative loads and computer configurations. For example, static partitioning might provide significant speedup on a multi-core computer that has only a few cores, but it might result in slowdowns on computers that have relatively many cores.

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Parallel Sorting

The PPL provides three sorting algorithms: concurrency::parallel_sort, concurrency::parallel_buffered_sort, and concurrency::parallel_radixsort. These sorting algorithms are useful when you have a data set that can benefit from being sorted in parallel. In particular, sorting in parallel is useful when you have a large dataset or when you use a computationally-expensive compare operation to sort your data. Each of these algorithms sorts elements in place.

The parallel_sort and parallel_buffered_sort algorithms are both compare-based algorithms. That is, they compare elements by value. The parallel_sort algorithm has no additional memory requirements, and is suitable for general-purpose sorting. The parallel_buffered_sort algorithm can perform better than parallel_sort, but it requires O(N) space.

The parallel_radixsort algorithm is hash-based. That is, it uses integer keys to sort elements. By using keys, this algorithm can directly compute the destination of an element instead of using comparisons. Like parallel_buffered_sort, this algorithm requires O(N) space.

The following table summarizes the important properties of the three parallel sorting algorithms.

The following illustration shows the important properties of the three parallel sorting algorithms more graphically.

These parallel sorting algorithms follow the rules of cancellation and exception handling. For more information about cancellation and exception handling in the Concurrency Runtime, see Canceling Parallel Algorithms and Exception Handling.

The following basic example shows how to use parallel_sort to sort a vector of int values. By default, parallel_sort uses std::less to compare values.

// basic-parallel-sort.cpp
// compile with: /EHsc
#include <ppl.h>
#include <random>
#include <iostream>

using namespace concurrency;
using namespace std;

int wmain()
{
    // Create and sort a large vector of random values.
    vector<int> values(25000000);
    generate(begin(values), end(values), mt19937(42));
    parallel_sort(begin(values), end(values));

    // Print a few values.
    wcout << "V(0)        = " << values[0] << endl;
    wcout << "V(12500000) = " << values[12500000] << endl;
    wcout << "V(24999999) = " << values[24999999] << endl;
} 
/* Output:
   V(0)        = -2147483129
   V(12500000) = -427327
   V(24999999) = 2147483311
*/

This example shows how to provide a custom compare function. It uses the std::complex::real method to sort std::complex<double> values in ascending order.

// For this example, ensure that you add the following #include directive:
// #include <complex>

// Create and sort a large vector of random values.
vector<complex<double>> values(25000000);
generate(begin(values), end(values), mt19937(42));
parallel_sort(begin(values), end(values),
    [](const complex<double>& left, const complex<double>& right) {
        return left.real() < right.real();
    });

// Print a few values.
wcout << "V(0)        = " << values[0] << endl;
wcout << "V(12500000) = " << values[12500000] << endl;
wcout << "V(24999999) = " << values[24999999] << endl;
/* Output:
   V(0)        = (383,0)
   V(12500000) = (2.1479e+009,0)
   V(24999999) = (4.29497e+009,0)
*/

This example shows how to provide a hash function to the parallel_radixsort algorithm. This example sorts 3-D points. The points are sorted based on their distance from a reference point.

// parallel-sort-points.cpp
// compile with: /EHsc
#include <ppl.h>
#include <random>
#include <iostream>

using namespace concurrency;
using namespace std;

// Defines a 3-D point.
struct Point
{
    int X;
    int Y;
    int Z;
};

// Computes the Euclidean distance between two points.
size_t euclidean_distance(const Point& p1, const Point& p2)
{
    int dx = p1.X - p2.X;
    int dy = p1.Y - p2.Y;
    int dz = p1.Z - p2.Z;
    return static_cast<size_t>(sqrt((dx*dx) + (dy*dy) + (dz*dz)));
}

int wmain()
{
    // The central point of reference.
    const Point center = { 3, 2, 7 };

    // Create a few random Point values.
    vector<Point> values(7);
    mt19937 random(42);
    generate(begin(values), end(values), [&random] {
        Point p = { random()%10, random()%10, random()%10 };
        return p;
    });

    // Print the values before sorting them.
    wcout << "Before sorting:" << endl;
    for_each(begin(values), end(values), [center](const Point& p) {
        wcout << L'(' << p.X << L"," << p.Y << L"," << p.Z 
              << L") D = " << euclidean_distance(p, center) << endl;
    });
    wcout << endl;

    // Sort the values based on their distances from the reference point.
    parallel_radixsort(begin(values), end(values), 
        [center](const Point& p) -> size_t {
            return euclidean_distance(p, center);
        });

    // Print the values after sorting them.
    wcout << "After sorting:" << endl;
    for_each(begin(values), end(values), [center](const Point& p) {
        wcout << L'(' << p.X << L"," << p.Y << L"," << p.Z 
              << L") D = " << euclidean_distance(p, center) << endl;
    });
    wcout << endl;
} 
/* Output:
    Before sorting:
    (2,7,6) D = 5
    (4,6,5) D = 4
    (0,4,0) D = 7
    (3,8,4) D = 6
    (0,4,1) D = 7
    (2,5,5) D = 3
    (7,6,9) D = 6

    After sorting:
    (2,5,5) D = 3
    (4,6,5) D = 4
    (2,7,6) D = 5
    (3,8,4) D = 6
    (7,6,9) D = 6
    (0,4,0) D = 7
    (0,4,1) D = 7
*/

For illustration, this example uses a relatively small data set. You can increase the initial size of the vector to experiment with performance improvements over larger sets of data.

This example uses a lambda expression as the hash function. You can also use one of the built-in implementations of the std::hash class or define your own specialization. You can also use a custom hash function object, as shown in this example:

// Functor class for computing the distance between points.
class hash_distance
{
public:
    hash_distance(const Point& reference)
        : m_reference(reference)
    {
    }

    size_t operator()(const Point& pt) const {
        return euclidean_distance(pt, m_reference);
    }

private:
    Point m_reference;
};

// Use hash_distance to compute the distance between points.
parallel_radixsort(begin(values), end(values), hash_distance(center));

The hash function must return an integral type (std::is_integral::value must be true). This integral type must be convertible to type size_t.

Choosing a Sorting Algorithm

In many cases, parallel_sort provides the best balance of speed and memory performance. However, as you increase the size of your data set, the number of available processors, or the complexity of your compare function, parallel_buffered_sort or parallel_radixsort can perform better. The best way to determine which sorting algorithm to use in any given scenario is to experiment and measure how long it takes to sort typical data under representative computer configurations. Keep the following guidelines in mind when you choose a sorting strategy.

• The size of your data set. In this document, a small dataset contains fewer than 1,000 elements, a medium dataset contains between 10,000 and 100,000 elements, and a large dataset contains more than 100,000 elements.

• The amount of work that your compare function or hash function performs.

• The amount of available computing resources.

• The characteristics of your data set. For example, one algorithm might perform well for data that is already nearly sorted, but not as well for data that is completely unsorted.

• The chunk size. The optional _Chunk_size argument specifies when the algorithm switches from a parallel to a serial sort implementation as it subdivides the overall sort into smaller units of work. For example, if you provide 512, the algorithm switches to serial implementation when a unit of work contains 512 or fewer elements. A serial implementation can improve overall performance because it eliminates the overhead that is required to process data in parallel.

It might not be worthwhile to sort a small dataset in parallel, even when you have a large number of available computing resources or your compare function or hash function performs a relatively large amount of work. You can use std::sort function to sort small datasets. (parallel_sort and parallel_buffered_sort call sort when you specify a chunk size that is larger than the dataset; however, parallel_buffered_sort would have to allocate O(N) space, which could take additional time due to lock contention or memory allocation.)

If you must conserve memory or your memory allocator is subject to lock contention, use parallel_sort to sort a medium-sized dataset. parallel_sort requires no additional space; the other algorithms require O(N) space.

Use parallel_buffered_sort to sort medium-sized datasets and when your application meets the additional O(N) space requirement. parallel_buffered_sort can be especially useful when you have a large number of computing resources or an expensive compare function or hash function.

Use parallel_radixsort to sort large datasets and when your application meets the additional O(N) space requirement. parallel_radixsort can be especially useful when the equivalent compare operation is more expensive or when both operations are expensive.

The following example compares the performance of sort, parallel_sort, parallel_buffered_sort, and parallel_radixsort against the same large set of random data.

// choosing-parallel-sort.cpp
// compile with: /EHsc
#include <ppl.h>
#include <random>
#include <iostream>
#include <windows.h>

using namespace concurrency;
using namespace std;

// Calls the provided work function and returns the number of milliseconds 
// that it takes to call that function.
template <class Function>
__int64 time_call(Function&& f)
{
   __int64 begin = GetTickCount();
   f();
   return GetTickCount() - begin;
}

const size_t DATASET_SIZE = 10000000;

// Create
// Creates the dataset for this example. Each call
// produces the same predefined sequence of random data.
vector<size_t> GetData()
{
    vector<size_t> data(DATASET_SIZE);
    generate(begin(data), end(data), mt19937(42));
    return data;
}

int wmain()
{
    // Use std::sort to sort the data.
    auto data = GetData();
    wcout << L"Testing std::sort...";
    auto elapsed = time_call([&data] { sort(begin(data), end(data)); });
    wcout << L" took " << elapsed << L" ms." <<endl;

    // Use concurrency::parallel_sort to sort the data.
    data = GetData();
    wcout << L"Testing concurrency::parallel_sort...";
    elapsed = time_call([&data] { parallel_sort(begin(data), end(data)); });
    wcout << L" took " << elapsed << L" ms." <<endl;

    // Use concurrency::parallel_buffered_sort to sort the data.
    data = GetData();
    wcout << L"Testing concurrency::parallel_buffered_sort...";
    elapsed = time_call([&data] { parallel_buffered_sort(begin(data), end(data)); });
    wcout << L" took " << elapsed << L" ms." <<endl;

    // Use concurrency::parallel_radixsort to sort the data.
    data = GetData();
    wcout << L"Testing concurrency::parallel_radixsort...";
    elapsed = time_call([&data] { parallel_radixsort(begin(data), end(data)); });
    wcout << L" took " << elapsed << L" ms." <<endl;
} 
/* Sample output (on a computer that has four cores):
    Testing std::sort... took 2906 ms.
    Testing concurrency::parallel_sort... took 2234 ms.
    Testing concurrency::parallel_buffered_sort... took 1782 ms.
    Testing concurrency::parallel_radixsort... took 907 ms.
*/

In this example, which assumes that it is acceptable to allocate O(N) space during the sort, parallel_radixsort performs the best on this dataset on this computer configuration.

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Related Topics

Reference

parallel_for Function

parallel_for_each Function

parallel_invoke Function

affinity_partitioner Class

auto_partitioner Class

simple_partitioner Class

static_partitioner Class

parallel_sort Function

parallel_buffered_sort Function

parallel_radixsort Function