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Execution Model Overview
Thread Mapping and GPU Occupancy
Kernels
Using Libraries for GPU Offload
Host/Device Memory, Buffer and USM
Unified Shared Memory Allocations
Performance Impact of USM and Buffers
Avoiding Moving Data Back and Forth between Host and Device
Optimizing Data Transfers
Avoiding Declaring Buffers in a Loop
Buffer Accessor Modes
Host/Device Coordination
Using Multiple Heterogeneous Devices
Compilation
OpenMP Offloading Tuning Guide
Multi-GPU and Multi-Stack Architecture and Programming
Level Zero
Performance Profiling and Analysis
Configuring GPU Device
Sub-Groups and SIMD Vectorization
Removing Conditional Checks
Registers and Performance
Shared Local Memory
Pointer Aliasing and the Restrict Directive
Synchronization among Threads in a Kernel
Considerations for Selecting Work-Group Size
Prefetch
Reduction
Kernel Launch
Executing Multiple Kernels on the Device at the Same Time
Submitting Kernels to Multiple Queues
Avoiding Redundant Queue Constructions
Programming Intel® XMX Using SYCL Joint Matrix Extension
Doing I/O in the Kernel
Optimizing Explicit SIMD Kernels
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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)
AbortedThe 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.