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‎LICENSE‎

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Apache License
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‎README.md‎

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# Collection of sample oneAPI Python extensions
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oneAPI Python extensions is a native Python extension compiled with DPC++ and
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targeting various devices programmable by DPC++, e.g. GPUs, multi-core CPUs or
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accelerators such as FPGA.
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This collection of examples is part of "[oneAPI for Scientific Python Community][poster]"
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virtual poster by @oleksandr-pavlyk and @diptorupd presented at [SciPy 2022][scipy22] conference.
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[poster]: https://intelpython.github.io/oneAPI-for-SciPy
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[scipy22]: https://www.scipy2022.scipy.org/

‎common_src/kde.hpp‎

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#include <CL/sycl.hpp>
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#include <cstdint>
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namespace example {
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namespace detail {
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template <typename T>
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T upper_quotient_of(T n, T wg)
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{
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return ((n + wg - 1) / wg);
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}
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template <typename T1, typename T2>
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T1 upper_quotient_of(T1 n, T2 wg) {
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return upper_quotient_of(n, static_cast<T1>(wg));
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}
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} // namespace detail
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/*
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Evaluates
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f(x, h) = sum(
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1/(sqrt(2*pi)*h)**dim * exp( - dist_squared(x, x_data[j])/(2*h*h)),
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0 <= j < n_data)
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writes out f(x, h) for every x.
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Execution target is specified with sycl::queue argument.
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All pointers are presumed allocated with system allocator (i.e. malloc, or new)
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*/
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template <typename T>
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void
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kernel_density_estimate(
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sycl::queue q,
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std::uint16_t dim, // dimensionality of the data
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const T* x, // points at which KDE is evaluated
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T *f, // where values of kde(x, h) are written to
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size_t n, // number of points to evaluate
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const T* data, // data-set
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size_t n_data, // size of data-set
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T h // smoothing parameter
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)
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{
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const sycl::property_list buf_props = {sycl::property::buffer::use_host_ptr()};
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sycl::buffer<T, 2> buf_X(x, sycl::range<2>(n, dim), buf_props);
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sycl::buffer<T, 2> buf_D(data, sycl::range<2>(n_data, dim), buf_props);
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sycl::buffer<T, 1> buf_F(f, sycl::range<1>(n), buf_props);
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// initialize buffer of function values with zeros
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q.submit(
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[&](sycl::handler &cgh) {
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sycl::accessor acc_F(buf_F, cgh, sycl::write_only, sycl::no_init);
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cgh.fill(acc_F, T(0));
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});
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// populate function values buffer
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// perform 2D loop in parallel
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q.submit(
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[&](sycl::handler &cgh) {
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sycl::accessor acc_X(buf_X, cgh, sycl::read_only);
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sycl::accessor acc_D(buf_D, cgh, sycl::read_only);
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sycl::accessor acc_F(buf_F, cgh, sycl::write_only, sycl::no_init);
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T two_pi = T(8) * sycl::atan(T(1));
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T gaussian_norm;
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if (dim == 1) {
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// use reciprocal sqrt function for efficiency
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gaussian_norm = sycl::rsqrt(two_pi) / h;
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} else if (dim % 2 == 1) {
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gaussian_norm = sycl::rsqrt(two_pi) / h / sycl::pown(two_pi * h * h, dim / 2);
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} else {
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gaussian_norm = T(1) / sycl::pown(two_pi * h * h, dim / 2);
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}
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size_t wg = 256;
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size_t n_data_per_wi = 16;
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auto gRange = sycl::range<2>(n, detail::upper_quotient_of(n_data, wg * n_data_per_wi) * wg);
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auto lRange = sycl::range<2>(1, wg);
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cgh.parallel_for(
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sycl::nd_range<2>(gRange, lRange),
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[=](sycl::nd_item<2> it) {
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auto x_id = it.get_global_id(0);
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auto x_data_batch_id = it.get_group(1);
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auto x_data_local_id = it.get_local_id(1);
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// work-items sums over data-points with indices
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// x_data_id = x_data_batch_id * wg * n_data_per_wi + m * wg + x_data_local_id
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// for 0 <= m < n_wi
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T local_sum(0);
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for(size_t m = 0; m < n_data_per_wi; ++m) {
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T dist_sq(0);
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size_t x_data_id = x_data_local_id + m * wg + x_data_batch_id * wg * n_data_per_wi;
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if (x_data_id < n_data) {
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for(std::uint8_t k=0; k < dim; ++k) {
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T diff =
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acc_X[sycl::id<2>( x_id, k)] -
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acc_D[sycl::id<2>(x_data_id, k)];
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dist_sq += diff * diff;
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}
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local_sum += (gaussian_norm / n_data) * sycl::exp(-dist_sq / T(2) / (h*h));
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}
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}
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auto work_group = it.get_group();
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T sum_over_wg = sycl::reduce_over_group(work_group, local_sum, sycl::plus<T>());
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if (work_group.leader()) {
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sycl::atomic_ref<T, sycl::memory_order::relaxed,
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sycl::memory_scope::device,
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sycl::access::address_space::global_space> f_ref(acc_F[x_id]);
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f_ref += sum_over_wg;
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}
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});
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});
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return;
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}
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} // namespace example

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