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computecpp-sdk/samples/parallel-for.cpp at master · codeplaysoftware/computecpp-sdk · GitHub
This repository was archived by the owner on Aug 11, 2023. It is now read-only.
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/*
**************************************************************************
*
* Copyright (C) 2016 Codeplay Software Limited
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* For your convenience, a copy of the License has been included in this
* repository.
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*
* Codeplay's ComputeCpp SDK
*
* parallel-for.cpp
*
* Description:
* Samples using the parallel_for API in SYCL.
*
*************************************************************************
*/
#
include
<
CL/sycl.hpp
>
#
include
<
iostream
>
using
namespace
cl
::sycl
;
/*
We define the number of work items to enqueue.
*/
const
int
nElems =
64u
;
class
assign_elements
;
int
main
() {
/*
We define and initialize data to be copied to the device.
*/
int
data[nElems] = {
0
};
try
{
default_selector selector;
queue
myQueue
(selector, [](exception_list l) {
for
(
auto
ep : l) {
try
{
std::rethrow_exception
(ep);
}
catch
(
const
exception& e) {
std::cout <<
"
Asynchronous exception caught:
\n
"
<< e.
what
();
}
}
});
buffer<
int
,
1
>
buf
(data, range<
1
>(nElems));
myQueue.
submit
([&](handler& cgh) {
auto
ptr = buf.
get_access
<access::mode::read_write>(cgh);
/*
We create an nd_range to describe the work space that the kernel is
* to be executed across. Here we create a linear (one dimensional)
* nd_range, which creates a work item per element of the vector. The
* first parameter of the nd_range is the range of global work items
* and the second is the range of local work items (i.e. the work group
* range).
*/
auto
myRange = nd_range<
1
>(range<
1
>(nElems), range<
1
>(nElems /
4
));
/*
We construct the lambda outside of the parallel_for function call,
* though it can be inline inside the function call too. For this
* parallel_for API the lambda is required to take a single parameter;
* an item<N> of the same dimensionality as the nd_range - in this
* case one. Other kernel dispatches might have different parameters -
* for example, the single_task takes no arguments.
*/
auto
myKernel = ([=](nd_item<
1
> item) {
/*
Items have various methods to extract ids and ranges. The
* specification has full details of these. Here we use the
* item::get_global() to retrieve the global id as an id<1>.
* This particular kernel will set the ith element to the value
* of i.
*/
ptr[item.
get_global_id
()] = item.
get_global_id
()[
0
];
});
/*
We call the parallel_for() API with two parameters; the nd_range
* we constructed above and the lambda that we constructed. Because
* the kernel is a lambda we *must* specify a template parameter to
* use as a name.
*/
cgh.
parallel_for
<assign_elements>(myRange, myKernel);
});
}
catch
(
const
exception& e) {
std::cout <<
"
Synchronous exception caught:
\n
"
<< e.
what
();
return
2
;
}
/*
Check the result is correct.
*/
int
result =
0
;
for
(
int
i =
0
; i < nElems; i++) {
if
(data[i] != i) {
std::cout <<
"
The results are incorrect (element
"
<< i <<
"
is
"
<< data[i] <<
"
)!
\n
"
;
result =
1
;
}
}
if
(result !=
1
) {
std::cout <<
"
The results are correct.
"
<< std::endl;
}
return
result;
}
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