Visible to Intel only — GUID: GUID-76DEBDE6-DDA3-412B-B325-A0D1FA87CE94
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
Visible to Intel only — GUID: GUID-76DEBDE6-DDA3-412B-B325-A0D1FA87CE94
Avoiding Redundant Queue Constructions
To execute kernels on a device, the user must create a queue, which references an associated context, platform, and device. These may be chosen automatically, or specified by the user.
A context is constructed, either directly by the user or implicitly when creating a queue, to hold all the runtime information required by the SYCL runtime and the SYCL backend to operate on a device. When a queue is created with no context specified, a new context is implicitly constructed using the default constructor. In general, creating a new context is a heavy duty operation due to the need for JIT compiling the program every time a kernel is submitted to a queue with a new context. For good performance one should use as few contexts as possible in their application.
In the following example, a queue is created inside the loop and the kernel is submitted to this new queue. This will essentially invoke the JIT compiler for every iteration of the loop.
int reductionMultipleQMultipleC(std::vector<int> &data, int iter) {
const size_t data_size = data.size();
int sum = 0;
int work_group_size = 512;
int num_work_groups = 1;
int num_work_items = work_group_size;
const sycl::property_list props = {sycl::property::buffer::use_host_ptr()};
sycl::buffer<int> buf(data.data(), data_size, props);
sycl::buffer<int> sum_buf(&sum, 1, props);
sycl::queue q1{sycl::default_selector_v, exception_handler};
// initialize data on the device
q1.submit([&](auto &h) {
sycl::accessor buf_acc(buf, h, sycl::write_only, sycl::no_init);
h.parallel_for(data_size, [=](auto index) { buf_acc[index] = 1; });
});
double elapsed = 0;
for (int i = 0; i < iter; i++) {
sycl::queue q2{sycl::default_selector_v, exception_handler};
if (i == 0)
std::cout << q2.get_device().get_info<sycl::info::device::name>() << "\n";
// reductionMultipleQMultipleC main begin
Timer timer;
q2.submit([&](auto &h) {
sycl::accessor buf_acc(buf, h, sycl::read_only);
sycl::accessor sum_acc(sum_buf, h, sycl::write_only, sycl::no_init);
sycl::local_accessor<int, 1> scratch(work_group_size, h);
h.parallel_for(sycl::nd_range<1>{num_work_items, work_group_size},
[=](sycl::nd_item<1> item) {
size_t loc_id = item.get_local_id(0);
int sum = 0;
for (int i = loc_id; i < data_size; i += num_work_items)
sum += buf_acc[i];
scratch[loc_id] = sum;
for (int i = work_group_size / 2; i > 0; i >>= 1) {
item.barrier(sycl::access::fence_space::local_space);
if (loc_id < i)
scratch[loc_id] += scratch[loc_id + i];
}
if (loc_id == 0)
sum_acc[0] = scratch[0];
});
});
// reductionMultipleQMultipleC main end
q2.wait();
sycl::host_accessor h_acc(sum_buf);
sum = h_acc[0];
elapsed += timer.Elapsed();
}
elapsed = elapsed / iter;
if (sum == sum_expected)
std::cout << "SUCCESS: Time reductionMultipleQMultipleC = " << elapsed
<< "s"
<< " sum = " << sum << "\n";
else
std::cout << "ERROR: reductionMultipleQMultipleC Expected " << sum_expected
<< " but got " << sum << "\n";
return sum;
} // end reductionMultipleQMultipleC
The above program can be rewritten by moving the queue declaration outside the loop, which improves performance quite dramatically.
int reductionSingleQ(std::vector<int> &data, int iter) {
const size_t data_size = data.size();
int sum = 0;
int work_group_size = 512;
int num_work_groups = 1;
int num_work_items = work_group_size;
const sycl::property_list props = {sycl::property::buffer::use_host_ptr()};
sycl::buffer<int> buf(data.data(), data_size, props);
sycl::buffer<int> sum_buf(&sum, 1, props);
sycl::queue q{sycl::default_selector_v, exception_handler};
std::cout << q.get_device().get_info<sycl::info::device::name>() << "\n";
// initialize data on the device
q.submit([&](auto &h) {
sycl::accessor buf_acc(buf, h, sycl::write_only, sycl::no_init);
h.parallel_for(data_size, [=](auto index) { buf_acc[index] = 1; });
});
double elapsed = 0;
for (int i = 0; i < iter; i++) {
// reductionIntBarrier main begin
Timer timer;
q.submit([&](auto &h) {
sycl::accessor buf_acc(buf, h, sycl::read_only);
sycl::accessor sum_acc(sum_buf, h, sycl::write_only, sycl::no_init);
sycl::local_accessor<int, 1> scratch(work_group_size, h);
h.parallel_for(sycl::nd_range<1>{num_work_items, work_group_size},
[=](sycl::nd_item<1> item) {
size_t loc_id = item.get_local_id(0);
int sum = 0;
for (int i = loc_id; i < data_size; i += num_work_items)
sum += buf_acc[i];
scratch[loc_id] = sum;
for (int i = work_group_size / 2; i > 0; i >>= 1) {
item.barrier(sycl::access::fence_space::local_space);
if (loc_id < i)
scratch[loc_id] += scratch[loc_id + i];
}
if (loc_id == 0)
sum_acc[0] = scratch[0];
});
});
// reductionSingleQ main end
q.wait();
sycl::host_accessor h_acc(sum_buf);
sum = h_acc[0];
elapsed += timer.Elapsed();
}
elapsed = elapsed / iter;
if (sum == sum_expected)
std::cout << "SUCCESS: Time reductionSingleQ = " << elapsed << "s"
<< " sum = " << sum << "\n";
else
std::cout << "ERROR: reductionSingleQ Expected " << sum_expected
<< " but got " << sum << "\n";
return sum;
} // end reductionSingleQ
In case you need to create multiple queues, try to share the contexts among the queues. This will improve the performance. The above kernel is rewritten as shown below where the new queues created inside the loop and the queue outside the loop share the context. In this case the performance is same as the one with one queue.
int reductionMultipleQSingleC(std::vector<int> &data, int iter) {
const size_t data_size = data.size();
int sum = 0;
int work_group_size = 512;
int num_work_groups = 1;
int num_work_items = work_group_size;
const sycl::property_list props = {sycl::property::buffer::use_host_ptr()};
sycl::buffer<int> buf(data.data(), data_size, props);
sycl::buffer<int> sum_buf(&sum, 1, props);
sycl::queue q1{sycl::default_selector_v, exception_handler};
// initialize data on the device
q1.submit([&](auto &h) {
sycl::accessor buf_acc(buf, h, sycl::write_only, sycl::no_init);
h.parallel_for(data_size, [=](auto index) { buf_acc[index] = 1; });
});
double elapsed = 0;
for (int i = 0; i < iter; i++) {
sycl::queue q2{q1.get_context(), sycl::default_selector_v,
exception_handler};
if (i == 0)
std::cout << q2.get_device().get_info<sycl::info::device::name>() << "\n";
// reductionMultipleQSingleC main begin
Timer timer;
q2.submit([&](auto &h) {
sycl::accessor buf_acc(buf, h, sycl::read_only);
sycl::accessor sum_acc(sum_buf, h, sycl::write_only, sycl::no_init);
sycl::local_accessor<int, 1> scratch(work_group_size, h);
h.parallel_for(sycl::nd_range<1>{num_work_items, work_group_size},
[=](sycl::nd_item<1> item) {
size_t loc_id = item.get_local_id(0);
int sum = 0;
for (int i = loc_id; i < data_size; i += num_work_items)
sum += buf_acc[i];
scratch[loc_id] = sum;
for (int i = work_group_size / 2; i > 0; i >>= 1) {
item.barrier(sycl::access::fence_space::local_space);
if (loc_id < i)
scratch[loc_id] += scratch[loc_id + i];
}
if (loc_id == 0)
sum_acc[0] = scratch[0];
});
});
// reductionMultipleQSingleC main end
q2.wait();
sycl::host_accessor h_acc(sum_buf);
sum = h_acc[0];
elapsed += timer.Elapsed();
}
elapsed = elapsed / iter;
if (sum == sum_expected)
std::cout << "SUCCESS: Time reductionMultipleQSingleContext = " << elapsed
<< "s"
<< " sum = " << sum << "\n";
else
std::cout << "ERROR: reductionMultipleQSingleContext Expected "
<< sum_expected << " but got " << sum << "\n";
return sum;
} // end reductionMultipleQSingleC