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Visible to Intel only — GUID: GUID-DC5F664C-7F2C-4DF9-ADEC-07F3F71B47DE
Visible to Intel only — GUID: GUID-DC5F664C-7F2C-4DF9-ADEC-07F3F71B47DE
Using Standard Library Functions in SYCL Kernels
Some, but not all, standard C++ functions can be called inside SYCL kernels. See Chapter 18 (Libraries) of Data Parallel C++ for an overview of supported functions. A simple example is provided here to illustrate what happens when an unsupported function is called from a SYCL kernel. The following program generates a sequence of random numbers using the rand() function:
#include <CL/sycl.hpp>
#include <iostream>
#include <random>
constexpr int N = 5;
extern SYCL_EXTERNAL int rand(void);
int main(void) {
#if defined CPU
sycl::queue Q(sycl::cpu_selector_v);
#elif defined GPU
sycl::queue Q(sycl::gpu_selector_v);
#else
sycl::queue Q(sycl::default_selector_v);
#endif
std::cout << "Running on: "
<< Q.get_device().get_info<sycl::info::device::name>() << std::endl;
// Attempt to use rand() inside a DPC++ kernel
auto test1 = sycl::malloc_shared<float>(N, Q.get_device(), Q.get_context());
srand((unsigned)time(NULL));
Q.parallel_for(N, [=](auto idx) {
test1[idx] = (float)rand() / (float)RAND_MAX;
}).wait();
// Show the random number sequence
for (int i = 0; i < N; i++)
std::cout << test1[i] << std::endl;
// Cleanup
sycl::free(test1, Q.get_context());
}
The program can be compiled to execute the SYCL kernel on the CPU (i.e., cpu_selector), or GPU (i.e., gpu_selector) devices. It compiles without errors on the two devices, and runs correctly on the CPU, but fails when run on the GPU:
$ icpx -fsycl -DCPU -std=c++17 external_rand.cpp -o external_rand $ ./external_rand Running on: Intel(R) Xeon(R) E-2176G CPU @ 3.70GHz 0.141417 0.821271 0.898045 0.218854 0.304283 $ icpx -fsycl -DGPU -std=c++17 external_rand.cpp -o external_rand $ ./external_rand Running on: Intel(R) Graphics Gen9 [0x3e96] terminate called after throwing an instance of 'cl::sycl::compile_program_error' what(): The program was built for 1 devices Build program log for 'Intel(R) Graphics Gen9 [0x3e96]': error: undefined reference to `rand()' error: backend compiler failed build. -11 (CL_BUILD_PROGRAM_FAILURE) Aborted
The failure occurs during Just-In-Time (JIT) compilation because of an undefined reference to rand(). Even though this function is declared SYCL_EXTERNAL, there’s no SYCL equivalent to the rand() function on the GPU device.
Fortunately, the SYCL library contains alternatives to many standard C++ functions, including those to generate random numbers. The following example shows equivalent functionality using the Intel® oneAPI DPC++ Library (oneDPL) and the Intel® oneAPI Math Kernel Library (oneMKL):
#include <CL/sycl.hpp>
#include <iostream>
#include <oneapi/dpl/random>
#include <oneapi/mkl/rng.hpp>
int main(int argc, char **argv) {
unsigned int N = (argc == 1) ? 20 : std::stoi(argv[1]);
if (N < 20)
N = 20;
// Generate sequences of random numbers between [0.0, 1.0] using oneDPL and
// oneMKL
sycl::queue Q(sycl::gpu_selector_v);
std::cout << "Running on: "
<< Q.get_device().get_info<sycl::info::device::name>() << std::endl;
auto test1 = sycl::malloc_shared<float>(N, Q.get_device(), Q.get_context());
auto test2 = sycl::malloc_shared<float>(N, Q.get_device(), Q.get_context());
std::uint32_t seed = (unsigned)time(NULL); // Get RNG seed value
// oneDPL random number generator on GPU device
clock_t start_time = clock(); // Start timer
Q.parallel_for(N, [=](auto idx) {
oneapi::dpl::minstd_rand rng_engine(seed, idx); // Initialize RNG engine
oneapi::dpl::uniform_real_distribution<float>
rng_distribution; // Set RNG distribution
test1[idx] = rng_distribution(rng_engine); // Generate RNG sequence
}).wait();
clock_t end_time = clock(); // Stop timer
std::cout << "oneDPL took " << float(end_time - start_time) / CLOCKS_PER_SEC
<< " seconds to generate " << N
<< " uniformly distributed random numbers." << std::endl;
// oneMKL random number generator on GPU device
start_time = clock(); // Start timer
oneapi::mkl::rng::mcg31m1 engine(
Q, seed); // Initialize RNG engine, set RNG distribution
oneapi::mkl::rng::uniform<float, oneapi::mkl::rng::uniform_method::standard>
rng_distribution(0.0, 1.0);
oneapi::mkl::rng::generate(rng_distribution, engine, N, test2)
.wait(); // Generate RNG sequence
end_time = clock(); // Stop timer
std::cout << "oneMKL took " << float(end_time - start_time) / CLOCKS_PER_SEC
<< " seconds to generate " << N
<< " uniformly distributed random numbers." << std::endl;
// Show first ten random numbers from each method
std::cout << std::endl
<< "oneDPL"
<< "\t"
<< "oneMKL" << std::endl;
for (int i = 0; i < 10; i++)
std::cout << test1[i] << " " << test2[i] << std::endl;
// Show last ten random numbers from each method
std::cout << "..." << std::endl;
for (size_t i = N - 10; i < N; i++)
std::cout << test1[i] << " " << test2[i] << std::endl;
// Cleanup
sycl::free(test1, Q.get_context());
sycl::free(test2, Q.get_context());
}
The necessary oneDPL and oneMKL functions are included in <oneapi/dpl/random> and <oneapi/mkl/rng.hpp>, respectively. The oneDPL and oneMKL examples perform the same sequence of operations: get a random number seed from the clock, initialize a random number engine, select the desired random number distribution, then generate the random numbers. The oneDPL code performs device offload explicitly using a SYCL kernel. In the oneMKL code, the mkl::rng functions handle the device offload implicitly.