Pass Data to Algorithms#

For an algorithm to access data, it is important that the used execution policy matches the data storage type. The following table shows which execution policies can be used with various data storage types.

Data Storage	Device Policies	Host Policies
SYCL buffer	Yes	No
Device-allocated unified shared memory (USM)	Yes	No
Shared and host-allocated USM	Yes	Yes
`std::vector` with `sycl::usm_allocator`	Yes	Yes
`std::vector` with an ordinary allocator	See Use std::vector	Yes
Other data in host memory	No	Yes

When using the standard-aligned (or host) execution policies, oneDPL supports data being passed to its algorithms as specified in the C++ standard (C++17 for algorithms working with iterators, C++20 for parallel range algorithms), with known restrictions and limitations.

According to the standard, the calling code must prevent data races when using algorithms with parallel execution policies.

Note

Implementations of std::vector<bool> are not required to avoid data races for concurrent modifications of vector elements. Some implementations may optimize multiple bool elements into a bitfield, making it unsafe for multithreading. For this reason, it is recommended to avoid std::vector<bool> for anything but a read-only input with the standard-aligned execution policies.

The following subsections describe proper ways to pass data to an algorithm invoked with a device execution policy.

Use oneapi::dpl::begin and oneapi::dpl::end Functions#

oneapi::dpl::begin and oneapi::dpl::end are special helper functions that allow you to pass SYCL buffers to parallel algorithms. These functions accept a SYCL buffer and return an object of an unspecified type that provides the following API:

It satisfies CopyConstructible and CopyAssignable C++ named requirements and comparable with operator== and operator!=.
It gives the following valid expressions: a + n, a - n, and a - b, where a and b are objects of the type, and n is an integer value. The effect of those operations is the same as for the type that satisfies the LegacyRandomAccessIterator, a C++ named requirement.
It provides the get_buffer method, which returns the buffer passed to the begin and end functions.

The begin and end functions can take SYCL 2020 deduction tags and sycl::no_init as arguments to explicitly control which access mode should be applied to a particular buffer when submitting a SYCL kernel to a device:

sycl::buffer<int> buf{/*...*/};
auto first_ro = oneapi::dpl::begin(buf, sycl::read_only);
auto first_wo = oneapi::dpl::begin(buf, sycl::write_only, sycl::no_init);
auto first_ni = oneapi::dpl::begin(buf, sycl::no_init);

To use the functions, add #include <oneapi/dpl/iterator> to your code. For example:

#include <oneapi/dpl/execution>
#include <oneapi/dpl/algorithm>
#include <oneapi/dpl/iterator>
#include <random>
#include <sycl/sycl.hpp>

int main(){
  std::vector<int> vec(1000);
  std::generate(vec.begin(), vec.end(), std::minstd_rand{});

  sycl::buffer<int> buf{ vec.data(), vec.size() };
  auto buf_begin = oneapi::dpl::begin(buf);
  auto buf_end   = oneapi::dpl::end(buf);

  oneapi::dpl::sort(oneapi::dpl::execution::dpcpp_default, buf_begin, buf_end);
  return 0;
}

Use Unified Shared Memory#

If you have USM-allocated data, pass the pointers to the start and past the end of the data sequence to a parallel algorithm. Make sure that the execution policy and the USM allocation use the same SYCL queue. For example:

#include <oneapi/dpl/execution>
#include <oneapi/dpl/algorithm>
#include <random>
#include <sycl/sycl.hpp>

int main(){
  sycl::queue q;
  const int n = 1000;
  int* d_head = sycl::malloc_shared<int>(n, q);
  std::generate(d_head, d_head + n, std::minstd_rand{});

  oneapi::dpl::sort(oneapi::dpl::execution::make_device_policy(q), d_head, d_head + n);

  sycl::free(d_head, q);
  return 0;
}

Note

Use of non-USM pointers is not supported for algorithms with device execution policies.

When using device USM, such as allocated by malloc_device, you are responsible for data transfers to and from the device to ensure that input data is device accessible during oneDPL algorithm execution and that the result is available to the subsequent operations.

Use std::vector#

You can use iterators to an ordinary std::vector with data in host memory, as shown in the following example:

#include <oneapi/dpl/execution>
#include <oneapi/dpl/algorithm>
#include <random>
#include <vector>

int main(){
  std::vector<int> vec( 1000 );
  std::generate(vec.begin(), vec.end(), std::minstd_rand{});

  oneapi::dpl::sort(oneapi::dpl::execution::dpcpp_default, vec.begin(), vec.end());
  return 0;
}

In this case a temporary SYCL buffer is created, the data is copied to this buffer, and it is processed according to the algorithm semantics. After processing on a device is complete, the modified data is copied from the temporary buffer back to the host container.

Note

For parallel range algorithms with device execution policies the use of ordinary std::vectors is not supported.

While convenient, direct use of an ordinary std::vector can lead to unintended copying between the host and the device. We recommend working with SYCL buffers or with USM to reduce data copying.

Note

For specialized memory algorithms that begin or end the lifetime of data objects, that is, uninitialized_* and destroy* families of functions, the data to initialize or destroy should be accessible on the device without extra copying. Therefore these algorithms may not use data storage on the host with device execution policies.

You can also use std::vector with a sycl::usm_allocator, as shown in the following example. Make sure that the allocator and the execution policy use the same SYCL queue:

#include <oneapi/dpl/execution>
#include <oneapi/dpl/algorithm>
#include <random>
#include <vector>
#include <sycl/sycl.hpp>

int main(){
  const int n = 1000;
  auto policy = oneapi::dpl::execution::dpcpp_default;
  sycl::usm_allocator<int, sycl::usm::alloc::shared> alloc(policy.queue());
  std::vector<int, decltype(alloc)> vec(n, alloc);
  std::generate(vec.begin(), vec.end(), std::minstd_rand{});

  // Recommended to use USM pointers:
  oneapi::dpl::sort(policy, vec.data(), vec.data() + vec.size());
/*
  // Iterators for USM allocators might require extra copying - not a recommended method
  oneapi::dpl::sort(policy, vec.begin(), vec.end());
*/
  return 0;
}

For std::vector with a USM allocator we recommend to use std::vector::data() in combination with std::vector::size() as shown in the example above, rather than iterators to std::vector. That is because for some implementations of the C++ Standard Library it might not be possible for oneDPL to detect that iterators are pointing to USM-allocated data. In that case the data will be treated as if it were in host memory, with an extra copy made to a SYCL buffer. Retrieving USM pointers from std::vector as shown guarantees no unintended copying.

Use Range Views#

For parallel range algorithms with device execution policies, place the data in USM or a USM-allocated std::vector, and pass it to an algorithm via a device-copyable range or view object such as std::ranges::subrange or std::span.

Note

Use of std::ranges::views::all is not supported for algorithms with device execution policies.

These data ranges as well as supported range adaptors and factories may be combined into data transformation pipelines that also can be used with parallel range algorithms. For example:

#include <oneapi/dpl/execution>
#include <oneapi/dpl/algorithm>
#include <random>
#include <vector>
#include <span>
#include <ranges>
#include <functional>
#include <sycl/sycl.hpp>

int main(){
  const int n = 1000;
  auto policy = oneapi::dpl::execution::dpcpp_default;
  sycl::queue q = policy.queue();

  int* d_head = sycl::malloc_host<int>(n, q);
  std::generate(d_head, d_head + n, std::minstd_rand{});

  sycl::usm_allocator<int, sycl::usm::alloc::shared> alloc(q);
  std::vector<int, decltype(alloc)> vec(n, alloc);

  oneapi::dpl::ranges::copy(policy,
      std::ranges::subrange(d_head, d_head + n) | std::views::transform(std::negate{}),
      std::span(vec));

  oneapi::dpl::ranges::sort(policy, std::span(vec));

  sycl::free(d_head, q);
  return 0;
}

Use oneDPL Iterators#

You can use iterators defined in <oneapi/dpl/iterator> header in the oneapi::dpl namespace:

counting_iterator
zip_iterator
transform_iterator
discard_iterator
permutation_iterator

Iterators section describes them in detail and provides usage examples.

If you want to use these iterators in combination with your own custom iterators, make sure to follow the guidelines in the Use Custom Iterators section.

Use Custom Iterators#

You can create your own iterators that can be used as input to oneDPL algorithms.

These custom iterators must meet the following requirements:

They must be random access iterators.
They must be SYCL device-copyable.
They must be indirectly device accessible if they point to data that is accessible on a device.

Note

If a custom iterator is not indirectly device accessible, the algorithm will create a temporary memory buffer and copy the data from the iterator to this buffer before processing on a device. This may lead to performance degradation, and also requires that the data be accessible on the host to be copied into the temporary memory buffer.

The term indirectly device accessible means that the data referenced by the iterator can be accessed from within a SYCL kernel (that is, on a device). oneDPL determines this by examining the return type of the free function is_onedpl_indirectly_device_accessible(It), where It is the iterator type. If the return type is std::true_type, the iterator is considered indirectly device accessible. This function is defined in <oneapi/dpl/iterator> in the oneapi::dpl namespace.

To make a custom iterator indirectly device accessible (assume its type is It), define an overload of is_onedpl_indirectly_device_accessible that accepts an argument of type It and returns std::true_type. This overload must be visible through argument-dependent lookup. If it is found, the trait oneapi::dpl::is_onedpl_indirectly_device_accessible_v<It>, which is also defined in <oneapi/dpl/iterator>, evaluates to true. The example below shows how to define such an overload:

#include <oneapi/dpl/execution>
#include <oneapi/dpl/iterator>
#include <oneapi/dpl/numeric>
#include <oneapi/dpl/algorithm>
#include <iterator>
#include <cstddef>
#include <iostream>
#include <sycl/sycl.hpp>

template <typename It>
class strided_iterator
{
public:
    using value_type = typename std::iterator_traits<It>::value_type;
    using difference_type = typename std::iterator_traits<It>::difference_type;
    using iterator_category = std::random_access_iterator_tag;
    using reference = typename std::iterator_traits<It>::reference;
    using pointer = typename std::iterator_traits<It>::pointer;

    strided_iterator(It ptr, difference_type stride): ptr(ptr), stride(stride) {}

    reference operator*() const { return *ptr; }
    pointer operator->() const { return ptr; }
    reference operator[](difference_type n) const { return *(*this + n);}

    strided_iterator& operator++() { ptr += stride; return *this; }
    strided_iterator& operator--() { ptr -= stride; return *this; }
    strided_iterator& operator+=(difference_type n) { ptr += n * stride; return *this;}
    strided_iterator& operator-=(difference_type n) { ptr -= n * stride; return *this; }
    strided_iterator operator+(difference_type n) const { return strided_iterator(ptr + n * stride, stride);}
    strided_iterator operator-(difference_type n) const { return strided_iterator(ptr - n * stride, stride); }
    difference_type operator-(const strided_iterator& other) const {return (ptr - other.ptr) / stride; }

    bool operator==(const strided_iterator& other) const { return ptr == other.ptr; }
    bool operator!=(const strided_iterator& other) const { return ptr != other.ptr; }
    bool operator<(const strided_iterator& other) const { return ptr < other.ptr; }
    bool operator>(const strided_iterator& other) const { return ptr > other.ptr; }
    bool operator<=(const strided_iterator& other) const { return ptr <= other.ptr; }
    bool operator>=(const strided_iterator& other) const { return ptr >= other.ptr; }

    // Another way to make this iterator indirectly device accessible
    // friend oneapi::dpl::is_indirectly_device_accessible<It> is_onedpl_indirectly_device_accessible(strided_iterator) { return {}; }
private:
    It ptr;
    difference_type stride;
};

// Make strided_iterator indirectly device accessible when it wraps an indirectly device accessible type
template <typename It>
auto is_onedpl_indirectly_device_accessible(strided_iterator<It>) -> oneapi::dpl::is_indirectly_device_accessible<It>;

int main() {
    sycl::queue q{};
    const int n = 10;
    int* d_head = sycl::malloc_device<int>(n, q);

    // Fill the memory with values from 0 to 9
    oneapi::dpl::copy(oneapi::dpl::execution::make_device_policy<class copy_kernel>(q),
                      oneapi::dpl::counting_iterator<int>(0),
                      oneapi::dpl::counting_iterator<int>(n),
                      d_head);

    // Reduce every second element, 5 elements in total: 0, 2, 4, 6, 8
    strided_iterator<int*> stride2(d_head, 2);
    auto res = oneapi::dpl::reduce(oneapi::dpl::execution::make_device_policy<class reduce_kernel>(q),
                                   stride2, stride2 + 5);

    // is_indirectly_device_accessible_v: 1
    // result: 20
    std::cout << "is_indirectly_device_accessible_v: "
              << (oneapi::dpl::is_indirectly_device_accessible_v<strided_iterator<int*>>) << std::endl;
    std::cout << "result: " << res << std::endl;

    sycl::free(d_head, q);
    return 0;
}

The example above uses oneapi::dpl::is_indirectly_device_accessible<It>, where It is int*. oneDPL predefines an overload of oneapi::dpl::is_indirectly_device_accessible<int*> that returns std::true_type assuming that pointers refer to USM-allocated data. It also automatically treats the following entities as indirectly device accessible:

Iterators to std::vector with a USM allocator.
Objects returned by oneapi::dpl::begin and oneapi::dpl::end functions.
counting_iterator and discard_iterator.
zip_iterator, transform_iterator, and permutation_iterator, if their underlying iterators are indirectly device accessible.

For more information, refer to the Iterators section of oneDPL specification.

Pass Data to Algorithms

Contents

Pass Data to Algorithms#

Use oneapi::dpl::begin and oneapi::dpl::end Functions#

Use Unified Shared Memory#

Use std::vector#

Use Range Views#

Use oneDPL Iterators#

Use Custom Iterators#