Visible to Intel only — GUID: GUID-4D15200B-F021-4848-85B9-4DB01C021728
Introduction
Getting Started
Parallelization
Intel® Iris® Xe GPU Architecture
GPU Execution Model Overview
SYCL* Thread Mapping and GPU Occupancy
Kernels
Using Libraries for GPU Offload
Host/Device Memory, Buffer and USM
Host/Device Coordination
Using Multiple Heterogeneous Devices
Compilation
Optimizing Media Pipelines
OpenMP Offloading Tuning Guide
Debugging and Profiling
GPU Analysis with Intel® Graphics Performance Analyzers (Intel® GPA)
Reference
Terms and Conditions
Sub-groups and SIMD Vectorization
Removing Conditional Checks
Registerization and Avoid Register Spills
Shared Local Memory
Pointer Aliasing and the Restrict Directive
Synchronization among Threads in a Kernel
Considerations for Selecting Work-group Size
Reduction
Kernel Launch
Executing Multiple Kernels on the Device at the Same Time
Submitting Kernels to Multiple Queues
Avoid Redundant Queue Construction
Visible to Intel only — GUID: GUID-4D15200B-F021-4848-85B9-4DB01C021728
Using the Timers
The standard C++ chrono library can be used for tracking times with varying degrees of precision in SYCL. The following example shows how to use the chrono timer class to time kernel execution from the host side.
#include <CL/sycl.hpp>
#include <iostream>
using namespace sycl;
// Array type and data size for this example.
constexpr size_t array_size = (1 << 16);
typedef std::array<int, array_size> IntArray;
double VectorAdd(queue &q, const IntArray &a, const IntArray &b, IntArray &sum) {
range<1> num_items{a.size()};
buffer a_buf(a);
buffer b_buf(b);
buffer sum_buf(sum.data(), num_items);
auto t1 = std::chrono::steady_clock::now(); // Start timing
q.submit([&](handler &h) {
// Input accessors
auto a_acc = a_buf.get_access<access::mode::read>(h);
auto b_acc = b_buf.get_access<access::mode::read>(h);
// Output accessor
auto sum_acc = sum_buf.get_access<access::mode::write>(h);
h.parallel_for(num_items, [=](id<1> i) { sum_acc[i] = a_acc[i] + b_acc[i]; });
}).wait();
auto t2 = std::chrono::steady_clock::now(); // Stop timing
return(std::chrono::duration_cast<std::chrono::microseconds>(t2 - t1).count());
}
void InitializeArray(IntArray &a) {
for (size_t i = 0; i < a.size(); i++) a[i] = i;
}
int main() {
default_selector d_selector;
IntArray a, b, sum;
InitializeArray(a);
InitializeArray(b);
queue q(d_selector);
std::cout << "Running on device: "
<< q.get_device().get_info<info::device::name>() << "\n";
std::cout << "Vector size: " << a.size() << "\n";
double t = VectorAdd(q, a, b, sum);
std::cout << "Vector add successfully completed on device in " << t << " microseconds\n";
return 0;
}
Note that this timing is purely from the host side. The actual execution of the kernel on the device may start much later, after the submission of the kernel by the host. SYCL provides a profiling capability that let you keep track of the time it took to execute kernels.
#include <CL/sycl.hpp>
#include <array>
#include <iostream>
using namespace sycl;
// Array type and data size for this example.
constexpr size_t array_size = (1 << 16);
typedef std::array<int, array_size> IntArray;
double VectorAdd(queue &q, const IntArray &a, const IntArray &b, IntArray &sum) {
range<1> num_items{a.size()};
buffer a_buf(a);
buffer b_buf(b);
buffer sum_buf(sum.data(), num_items);
event e = q.submit([&](handler &h) {
// Input accessors
auto a_acc = a_buf.get_access<access::mode::read>(h);
auto b_acc = b_buf.get_access<access::mode::read>(h);
// Output accessor
auto sum_acc = sum_buf.get_access<access::mode::write>(h);
h.parallel_for(num_items, [=](id<1> i) { sum_acc[i] = a_acc[i] + b_acc[i]; });
});
q.wait();
return(e.template get_profiling_info<info::event_profiling::command_end>() -
e.template get_profiling_info<info::event_profiling::command_start>());
}
void InitializeArray(IntArray &a) {
for (size_t i = 0; i < a.size(); i++) a[i] = i;
}
int main() {
default_selector d_selector;
IntArray a, b, sum;
InitializeArray(a);
InitializeArray(b);
queue q(d_selector, property::queue::enable_profiling{});
std::cout << "Running on device: "
<< q.get_device().get_info<info::device::name>() << "\n";
std::cout << "Vector size: " << a.size() << "\n";
double t = VectorAdd(q, a, b, sum);
std::cout << "Vector add successfully completed on device in " << t << " nanoseconds\n";
return 0;
}
When these examples are run, it is quite possible that the time reported by chrono is much larger than the time reported by the SYCL profiling class. This is because the SYCL profiling does not include any data transfer times between the host and the offload device.