Optimizing Memory Movement between Host and Accelerator
Buffers can be created using properties to control how they
are allocated. One such property is
use_host_ptr
. This informs
the runtime that if possible, the host memory should be directly
used by the buffer instead of a copy. This will avoid the need to
copy the content of the buffer back and forth between the host
memory and the buffer memory, potentially saving time during the buffer
creation and destruction. Another case when the GPU and CPU have shared memory,
it is possible to avoid copies of memory through sharing of
pages. But for page sharing to be possible, the allocated memory needs to
have some properties like being aligned on page boundary.
In case of discrete devices, the
benefit may not be realized because any memory operation by the
accelerator will have to go across PCIe or some other slower
interface than the memory of the accelerator.The following code shows how to print the memory addresses
on the host, inside the buffer, and on the accelerator device inside
the kernel.
int VectorAdd0(sycl::queue &q, AlignedVector<int> &a, AlignedVector<int> &b,
AlignedVector<int> &sum, int iter) {
sycl::range num_items{a.size()};
const sycl::property_list props = {sycl::property::buffer::use_host_ptr()};
for (int i = 0; i < iter; i++) {
sycl::buffer a_buf(a, props);
sycl::buffer b_buf(b, props);
sycl::buffer sum_buf(sum.data(), num_items, props);
{
sycl::host_accessor a_host_acc(a_buf);
std::cout << "add0: buff memory address =" << a_host_acc.get_pointer()
<< "\n";
std::cout << "add0: address of vector a = " << a.data() << "\n";
}
q.submit([&](auto &h) {
// Input accessors
sycl::accessor a_acc(a_buf, h, sycl::read_only);
sycl::accessor b_acc(b_buf, h, sycl::read_only);
// Output accessor
sycl::accessor sum_acc(sum_buf, h, sycl::write_only, sycl::noinit);
sycl::stream out(1024 * 1024, 1 * 128, h);
h.parallel_for(num_items, [=](auto i) {
if (i[0] == 0)
out << "add0: dev addr = " << a_acc.get_pointer() << "\n";
sum_acc[i] = a_acc[i] + b_acc[i];
});
});
}
q.wait();
return (0);
}
When this program is run it can be seen that the addresses for all
three (host, in the buffer, and on the accelerator) are the same when
the property
use_host_ptr
is set for integrated GPU devices. But
for discrete GPU devices the buffer and device addresses will be different.
Also note that in line 1, none
of the incoming arguments are declared to be const
. If these are
declared const
then during buffer creation they are copied and new
memory is allocated instead of reusing the memory in the host
vectors. The code snippet below demonstrates this. When this code
is executed, we see that the addresses associated with the
incoming vectors are different from the memory present in the buffer
and also the memory present in the accelerator device.int VectorAdd1(sycl::queue &q, const AlignedVector<int> &a,
const AlignedVector<int> &b, AlignedVector<int> &sum, int iter) {
sycl::range num_items{a.size()};
const sycl::property_list props = {sycl::property::buffer::use_host_ptr()};
for (int i = 0; i < iter; i++) {
sycl::buffer a_buf(a, props);
sycl::buffer b_buf(b, props);
sycl::buffer sum_buf(sum.data(), num_items, props);
{
sycl::host_accessor a_host_acc(a_buf);
std::cout << "add1: buff memory address =" << a_host_acc.get_pointer()
<< "\n";
std::cout << "add1: address of vector aa = " << a.data() << "\n";
}
q.submit([&](auto &h) {
// Input accessors
sycl::accessor a_acc(a_buf, h, sycl::read_only);
sycl::accessor b_acc(b_buf, h, sycl::read_only);
// Output accessor
sycl::accessor sum_acc(sum_buf, h, sycl::write_only, sycl::noinit);
sycl::stream out(16 * 1024, 16 * 1024, h);
h.parallel_for(num_items, [=](auto i) {
if (i[0] == 0)
out << "add1: dev addr = " << a_acc.get_pointer() << "\n";
sum_acc[i] = a_acc[i] + b_acc[i];
});
});
}
q.wait();
return (0);
}
The kernel
vectorAdd3
will not incur the cost of copying the
memory contents from the buffer to the accelerator device because the
use_host_ptr
property is set while creating the buffers, and the
buffers are aligned on a page boundary for an integrated GPU device.
If memory pointed to by a
buffer is not aligned on a page boundary, then new memory is allocated
that aligns on a page boundary and the contents of the buffer are copied
into that memory. This new memory from the buffer is then shared with
the accelerator either by copying the contents from the buffer on host
to the device (in case of accelerators that do not share any memory)
or by using the page tables to avoid a physical copy of memory
available on the device in case of shared memory.int VectorAdd2(sycl::queue &q, AlignedVector<int> &a, AlignedVector<int> &b,
AlignedVector<int> &sum, int iter) {
sycl::range num_items{a.size()};
const sycl::property_list props = {sycl::property::buffer::use_host_ptr()};
auto start = std::chrono::steady_clock::now();
for (int i = 0; i < iter; i++) {
sycl::buffer a_buf(a, props);
sycl::buffer b_buf(b, props);
sycl::buffer sum_buf(sum.data(), num_items, props);
q.submit([&](auto &h) {
// Input accessors
sycl::accessor a_acc(a_buf, h, sycl::read_only);
sycl::accessor b_acc(b_buf, h, sycl::read_only);
// Output accessor
sycl::accessor sum_acc(sum_buf, h, sycl::write_only, sycl::noinit);
h.parallel_for(num_items,
[=](auto i) { sum_acc[i] = a_acc[i] + b_acc[i]; });
});
}
q.wait();
auto end = std::chrono::steady_clock::now();
std::cout << "Vector add2 completed on device - took "
<< (end - start).count() << " u-secs\n";
return ((end - start).count());
}
The kernel below will incur the cost of copying memory contents
between the host and buffer, and also from the buffer to the
accelerator.
int VectorAdd3(sycl::queue &q, const AlignedVector<int> &a,
const AlignedVector<int> &b, AlignedVector<int> &sum, int iter) {
sycl::range num_items{a.size()};
auto start = std::chrono::steady_clock::now();
for (int i = 0; i < iter; i++) {
sycl::buffer a_buf(a);
sycl::buffer b_buf(b);
sycl::buffer sum_buf(sum.data(), num_items);
auto e = q.submit([&](auto &h) {
// Input accessors
sycl::accessor a_acc(a_buf, h, sycl::read_only);
sycl::accessor b_acc(b_buf, h, sycl::read_only);
// Output accessor
sycl::accessor sum_acc(sum_buf, h, sycl::write_only, sycl::noinit);
h.parallel_for(num_items,
[=](auto i) { sum_acc[i] = a_acc[i] + b_acc[i]; });
});
}
q.wait();
auto end = std::chrono::steady_clock::now();
std::cout << "Vector add3 completed on device - took "
<< (end - start).count() << " u-secs\n";
return ((end - start).count());
}
Care must be taken to ensure that unnecessary copies are avoided
during the creation of buffers and passing the memory from the buffers
to the kernels. Even when the accelerator shares memory with the
host, a few additional conditions must be satisfied to avoid these
extra copies.