radix_sort Function Templates

The radix_sort function sorts data using the radix sort algorithm. The sorting is stable, preserving the relative order of elements with equal keys. Both in-place and out-of-place overloads are provided. Out-of-place overloads do not alter the input sequence.

The functions implement a Onesweep* ¹ algorithm variant.

A synopsis of the radix_sort function is provided below:

// defined in <oneapi/dpl/experimental/kernel_templates>

namespace oneapi::dpl::experimental::kt::gpu::esimd {

// Sort in-place
template <bool IsAscending = true, std::uint8_t RadixBits = 8,
          typename KernelParam, typename Iterator>
sycl::event
radix_sort (sycl::queue q, Iterator first, Iterator last,
            KernelParam param); // (1)

template <bool IsAscending = true, std::uint8_t RadixBits = 8,
          typename KernelParam, typename Range>
sycl::event
radix_sort (sycl::queue q, Range&& r, KernelParam param); // (2)


// Sort out-of-place
template <bool IsAscending = true, std::uint8_t RadixBits = 8,
          typename KernelParam, typename Iterator1,
          typename Iterator2>
sycl::event
radix_sort (sycl::queue q, Iterator1 first, Iterator1 last,
            Iterator2 first_out, KernelParam param); // (3)

template <bool IsAscending = true, std::uint8_t RadixBits = 8,
          typename KernelParam, typename Range1, typename Range2>
sycl::event
radix_sort (sycl::queue q, Range1&& r, Range2&& r_out,
            KernelParam param); // (4)
}

Template Parameters

Parameters

Type Requirements:

• The element type of sequence(s) to sort must be a C++ integral or floating-point type other than bool with a width of up to 64 bits.

Return Value

A sycl::event object representing the status of the algorithm execution.

Usage Examples

In-Place Example

// possible build and run commands:
//    icpx -fsycl radix_sort.cpp -o radix_sort -I /path/to/oneDPL/include && ./radix_sort

#include <cstdint>
#include <iostream>
#include <sycl/sycl.hpp>

#include <oneapi/dpl/experimental/kernel_templates>

namespace kt = oneapi::dpl::experimental::kt;

int main()
{
   std::size_t n = 6;
   sycl::queue q{sycl::gpu_selector_v};
   std::uint32_t* keys = sycl::malloc_shared<std::uint32_t>(n, q);

   // initialize
   keys[0] = 3, keys[1] = 2, keys[2] = 1, keys[3] = 5, keys[4] = 3, keys[5] = 3;

   // sort
   auto e = kt::gpu::esimd::radix_sort<false, 8>(q, keys, keys + n, kt::kernel_param<416, 64>{}); // (1)
   e.wait();

   // print
   for(std::size_t i = 0; i < n; ++i)
      std::cout << keys[i] << ' ';
   std::cout << '\n';

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

Output:

5 3 3 3 2 1

Out-of-Place Example

// possible build and run commands:
//    icpx -fsycl radix_sort.cpp -o radix_sort -I /path/to/oneDPL/include && ./radix_sort

#include <cstdint>
#include <iostream>
#include <sycl/sycl.hpp>

#include <oneapi/dpl/experimental/kernel_templates>

namespace kt = oneapi::dpl::experimental::kt;

int main()
{
   std::size_t n = 6;
   sycl::queue q{sycl::gpu_selector_v};
   std::uint32_t* keys = sycl::malloc_shared<std::uint32_t>(n, q);
   std::uint32_t* keys_out = sycl::malloc_shared<std::uint32_t>(n, q);

   // initialize
   keys[0] = 3, keys[1] = 2, keys[2] = 1, keys[3] = 5, keys[4] = 3, keys[5] = 3;

   // sort
   auto e = kt::gpu::esimd::radix_sort<false, 8>(q, keys, keys + n, keys_out, kt::kernel_param<416, 64>{}); // (3)
   e.wait();

   // print
   for(std::size_t i = 0; i < n; ++i)
      std::cout << keys[i] << ' ';
   std::cout << '\n';
   for(std::size_t i = 0; i < n; ++i)
      std::cout << keys_out[i] << ' ';
   std::cout << '\n';

   sycl::free(keys, q);
   sycl::free(keys_out, q);
   return 0;
}

Output:

3 2 1 5 3 3
5 3 3 3 2 1

Memory Requirements

The algorithm uses global and local device memory (see SYCL 2020 Specification) for intermediate data storage. For the algorithm to operate correctly, there must be enough memory on the device. If there is not enough global device memory, a std::bad_alloc exception is thrown. The behavior is undefined if there is not enough local memory. The amount of memory that is required depends on input data and configuration parameters, as described below.

Global Memory Requirements

Global memory is used for copying the input sequence(s) and storing internal data such as radix value counters. The used amount depends on many parameters; below is an upper bound approximation:

N_keys + C * N_keys

where the sequence with keys takes N_keys space, and the additional space is C * N_keys.

The value of C depends on param.data_per_workitem, param.workgroup_size, and RadixBits. For param.data_per_workitem set to 32, param.workgroup_size to 64, and RadixBits to 8, C approximately equals to 1. Incrementing RadixBits increases C up to twice, while doubling either param.data_per_workitem or param.workgroup_size leads to a halving of C.

Local Memory Requirements

Local memory is used for reordering keys within a work-group, and for storing internal data such as radix value counters. The used amount depends on many parameters; below is an upper bound approximation:

N_{keys_per_workgroup} + C

where N_{keys_per_workgroup} is the amount of memory to store keys. C is some additional space for storing internal data.

N_{keys_per_workgroup} equals to sizeof(key_type) * param.data_per_workitem * param.workgroup_size, C does not exceed 4KB.

Recommended Settings for Best Performance

The general advice is to choose kernel parameters based on performance measurements and profiling information. The initial configuration may be selected according to these high-level guidelines:

• When the number of elements to sort (N) is small (~16K or less) and the algorithm is radix_sort, generally sorting is done more efficiently by a single work-group. Increase the param values to make N <= param.data_per_workitem * param.workgroup_size.

• When the number of elements to sort N is between 16K and 1M, utilizing all available compute cores is key for better performance. Allow creating enough work chunks to feed all X^e-cores ² on a GPU: param.data_per_workitem * param.workgroup_size ≈ N / xe_core_count.

• When the number of elements to sort is large (more than ~1M), maximizing the number of elements processed by a work-group, which equals to param.data_per_workitem * param.workgroup_size, reduces synchronization overheads between work-groups and usually benefits the overall performance.

[1]

Andy Adinets and Duane Merrill (2022). Onesweep: A Faster Least Significant Digit Radix Sort for GPUs. https://arxiv.org/abs/2206.01784.

[2]

The X^e-core term is described in the oneAPI GPU Optimization Guide. Check the number of cores in the device specification, such as Intel® Data Center GPU Max specification.