Auto-Tune Policy#

The dynamic selection API is an experimental feature in the oneAPI DPC++ Library (oneDPL) that selects an execution resource based on a chosen selection policy. There are several policies provided as part of the API. Policies encapsulate the logic and any associated state needed to make a selection.

The auto-tune policy selects resources using runtime profiling. auto_tune_policy is useful for determining which resource performs best for a given kernel. The choice is made based on runtime performance history, so this policy is only useful for kernels that have stable performance. Initially, this policy acts like round_robin_policy, rotating through each resource (one or more times). Then, once it has determined which resource is performing best, it uses that resource thereafter. Optionally, a resampling interval can be set to return to the profiling phase periodically.

namespace oneapi::dpl::experimental {

  template<typename Backend = sycl_backend>
  class auto_tune_policy {
    // useful types
    using resource_type = typename Backend::resource_type;
    using wait_type = typename Backend::wait_type;

    class selection_type {
      auto_tune_policy<Backend> get_policy() const;
      resource_type unwrap() const;

    // constructors
    auto_tune_policy(uint64_t resample_interval_in_milliseconds = 0);
    auto_tune_policy(const std::vector<resource_type>& u,
                     uint64_t resample_interval_in_milliseconds = 0);

    // deferred initializer
    void initialize(uint64_t resample_interval_in_milliseconds = 0);
    void initialize(const std::vector<resource_type>& u,
                    uint64_t resample_interval_in_milliseconds = 0);

    // queries
    auto get_resources() const;
    auto get_submission_group();

    // other implementation defined functions...


This policy can be used with all the dynamic selection functions, such as select, submit, and submit_and_wait. It can also be used with policy_traits.


In the following example, an auto_tune_policy is used to dynamically select between two queues, a CPU queue and a GPU queue.

#include <oneapi/dpl/dynamic_selection>
#include <sycl/sycl.hpp>
#include <iostream>

namespace ex = oneapi::dpl::experimental;

int main() {
  std::vector<sycl::queue> r { sycl::queue{sycl::cpu_selector_v},
                               sycl::queue{sycl::gpu_selector_v} };

  const std::size_t N = 10000;
  std::vector<float> av(N, 0.0);
  std::vector<float> bv(N, 0.0);
  std::vector<float> cv(N, 0.0);
  for (int i = 0; i < N; ++i) {
    av[i] = bv[i] = i;

  ex::auto_tune_policy p{r}; // (1)

    sycl::buffer<float> a_b(av);
    sycl::buffer<float> b_b(bv);
    sycl::buffer<float> c_b(cv);

    for (int i = 0; i < 6; ++i) {
      ex::submit_and_wait(p, [&](sycl::queue q) { // (2)
        // (3)
        std::cout << (q.get_device().is_cpu() ? "using cpu\n" : "using gpu\n");
        return q.submit([&](sycl::handler &h) { // (4)
          sycl::accessor a_a(a_b, h, sycl::read_only);
          sycl::accessor b_a(b_b, h, sycl::read_only);
          sycl::accessor c_a(c_b, h, sycl::read_write);
          h.parallel_for(N, [=](auto i) { c_a[i] = a_a[i] + b_a[i]; });

  for (int i = 0; i < N; ++i) {
    if (cv[i] != 2*i) {
       std::cout << "ERROR!\n";
  std::cout << "Done.\n";

The key points in this example are:

  1. An auto_tune_policy is constructed to select between the CPU and GPU.

  2. submit_and_wait is invoked with the policy as the first argument. The selected queue will be passed to the user-provided function.

  3. For clarity when run, the type of device is displayed.

  4. The queue is used in function to perform and asynchronous offload. The SYCL event returned from the call to submit is returned. Returning an event is required for functions passed to submit and submit_and_wait.

Selection Algorithm#

The selection algorithm for auto_tune_policy uses runtime profiling to choose the best resource for the given function. A simplified, expository implementation of the selection algorithm follows:

template<typename Function, typename ...Args>
selection_type auto_tune_policy::select(Function&& f, Args&&...args) {
  if (initialized_) {
    auto k = make_task_key(f, args...);
    auto tuner = get_tuner(k);
    auto offset = tuner->get_resource_to_profile();
    if (offset == use_best) {
      return selection_type {*this, tuner->best_resource_, tuner};
    } else {
      auto r = resources_[offset];
      return selection{*this, r, tuner};
  } else {
    throw std::logic_error(“selected called before initialization”);

where make_task_key combines the inputs, including the function and its arguments, into a key that uniquely identifies the user function that is being profiled. tuner is the encapsulated logic for performing runtime profiling and choosing the best option for a given key. When the call to get_resource_to_profile() return use_best, the tuner is not in the profiling phase, and so the previously determined best resource is used. Otherwise, the resource at index offset in the resources_ vector is used and its resulting performance is profiled. When an auto_tune_policy is initialized with a non-zero resample interval, the policy will periodically return to the profiling phase base on the provided interval value.


auto_tune_policy provides three constructors.

auto_tune_policy constructors#




Defers initialization. An initialize function must be called prior to use.

auto_tune_policy(uint64_t resample_interval_in_milliseconds = 0);

Initialized to use the default set of resources. An optional resampling interval can be provided.

auto_tune_policy(const std::vector<resource_type>& u, uint64_t resample_interval_in_milliseconds = 0);

Overrides the default set of resources. An optional resampling interval can be provided.

Deferred Initialization#

A auto_tune_policy that was constructed with deferred initialization must be initialized by calling one its initialize member functions before it can be used to select or submit.

auto_tune_policy constructors#



initialize(uint64_t resample_interval_in_milliseconds = 0);

Initialize to use the default set of resources. An optional resampling interval can be provided.

initialize(const std::vector<resource_type>& u, uint64_t resample_interval_in_milliseconds = 0);

Overrides the default set of resources. An optional resampling interval can be provided.


A auto_tune_policy has get_resources and get_submission_group member functions.

auto_tune_policy constructors#



std::vector<resource_type> get_resources();

Returns the set of resources the policy is selecting from.

auto get_submission_group();

Returns an object that can be used to wait for all active submissions.

Reporting Requirements#

If a resource returned by select is used directly without calling submit or submit_and_wait, it may be necessary to call report to provide feedback to the policy. The auto_tune_policy tracks the performance of submissions on each device via callbacks that report the execution time. The instrumentation to report these events is included in the implementations of submit and submit_and_wait. However, if you use select and then submit work directly to the selected resource, it is necessary to explicitly report these events.

auto_tune_policy reporting requirements#


is reporting required?







In generic code, it is possible to perform compile-time checks to avoid reporting overheads when reporting is not needed, while still writing code that will work with any policy, as demonstrated below:

auto s = select(my_policy);
if constexpr (report_info_v<decltype(s), execution_info::task_submission_t>)