inline size_t bitonic_block_sort(KeyIterator keys_first,
ValueIterator values_first,
Compare compare,
const size_t count,
const bool sort_by_key,
command_queue &queue)
{
typedef typename std::iterator_traits<KeyIterator>::value_type key_type;
typedef typename std::iterator_traits<ValueIterator>::value_type value_type;
meta_kernel k("bitonic_block_sort");
size_t count_arg = k.add_arg<const uint_>("count");
size_t local_keys_arg = k.add_arg<key_type *>(memory_object::local_memory, "lkeys");
size_t local_vals_arg = 0;
if(sort_by_key) {
local_vals_arg = k.add_arg<uchar_ *>(memory_object::local_memory, "lidx");
}
k <<
// Work item global and local ids
k.decl<const uint_>("gid") << " = get_global_id(0);\n" <<
k.decl<const uint_>("lid") << " = get_local_id(0);\n";
// declare my_key and my_value
k <<
k.decl<key_type>("my_key") << ";\n";
// Instead of copying values (my_value) in local memory with keys
// we save local index (uchar) and copy my_value at the end at
// final index. This saves local memory.
if(sort_by_key)
{
k <<
k.decl<uchar_>("my_index") << " = (uchar)(lid);\n";
}
// load key
k <<
"if(gid < count) {\n" <<
k.var<key_type>("my_key") << " = " <<
keys_first[k.var<const uint_>("gid")] << ";\n" <<
"}\n";
// load key and index to local memory
k <<
"lkeys[lid] = my_key;\n";
if(sort_by_key)
{
k <<
"lidx[lid] = my_index;\n";
}
k <<
k.decl<const uint_>("offset") << " = get_group_id(0) * get_local_size(0);\n" <<
k.decl<const uint_>("n") << " = min((uint)(get_local_size(0)),(count - offset));\n";
// When work group size is a power of 2 bitonic sorter can be used;
// otherwise, slower odd-even sort is used.
k <<
// check if n is power of 2
"if(((n != 0) && ((n & (~n + 1)) == n))) {\n";
// bitonic sort, not stable
k <<
// wait for keys and vals to be stored in local memory
"barrier(CLK_LOCAL_MEM_FENCE);\n" <<
"#pragma unroll\n" <<
"for(" <<
k.decl<uint_>("length") << " = 1; " <<
"length < n; " <<
"length <<= 1" <<
") {\n" <<
// direction of sort: false -> asc, true -> desc
k.decl<bool>("direction") << "= ((lid & (length<<1)) != 0);\n" <<
"for(" <<
k.decl<uint_>("k") << " = length; " <<
"k > 0; " <<
"k >>= 1" <<
") {\n" <<
// sibling to compare with my key
k.decl<uint_>("sibling_idx") << " = lid ^ k;\n" <<
k.decl<key_type>("sibling_key") << " = lkeys[sibling_idx];\n" <<
k.decl<bool>("compare") << " = " <<
compare(k.var<key_type>("sibling_key"),
k.var<key_type>("my_key")) << ";\n" <<
k.decl<bool>("equal") << " = !(compare || " <<
compare(k.var<key_type>("my_key"),
k.var<key_type>("sibling_key")) << ");\n" <<
k.decl<bool>("swap") <<
" = compare ^ (sibling_idx < lid) ^ direction;\n" <<
"swap = equal ? false : swap;\n" <<
"my_key = swap ? sibling_key : my_key;\n";
if(sort_by_key)
{
k <<
"my_index = swap ? lidx[sibling_idx] : my_index;\n";
}
k <<
"barrier(CLK_LOCAL_MEM_FENCE);\n" <<
"lkeys[lid] = my_key;\n";
if(sort_by_key)
{
k <<
"lidx[lid] = my_index;\n";
}
k <<
"barrier(CLK_LOCAL_MEM_FENCE);\n" <<
"}\n" <<
"}\n";
// end of bitonic sort
// odd-even sort, not stable
k <<
"}\n" <<
"else { \n";
k <<
k.decl<bool>("lid_is_even") << " = (lid%2) == 0;\n" <<
k.decl<uint_>("oddsibling_idx") << " = " <<
"(lid_is_even) ? max(lid,(uint)(1)) - 1 : min(lid+1,n-1);\n" <<
k.decl<uint_>("evensibling_idx") << " = " <<
"(lid_is_even) ? min(lid+1,n-1) : max(lid,(uint)(1)) - 1;\n" <<
// wait for keys and vals to be stored in local memory
"barrier(CLK_LOCAL_MEM_FENCE);\n" <<
"#pragma unroll\n" <<
"for(" <<
k.decl<uint_>("i") << " = 0; " <<
"i < n; " <<
"i++" <<
") {\n" <<
k.decl<uint_>("sibling_idx") <<
" = i%2 == 0 ? evensibling_idx : oddsibling_idx;\n" <<
k.decl<key_type>("sibling_key") << " = lkeys[sibling_idx];\n" <<
k.decl<bool>("compare") << " = " <<
compare(k.var<key_type>("sibling_key"),
k.var<key_type>("my_key")) << ";\n" <<
k.decl<bool>("equal") << " = !(compare || " <<
compare(k.var<key_type>("my_key"),
k.var<key_type>("sibling_key")) << ");\n" <<
k.decl<bool>("swap") <<
" = compare ^ (sibling_idx < lid);\n" <<
"swap = equal ? false : swap;\n" <<
"my_key = swap ? sibling_key : my_key;\n";
if(sort_by_key)
{
k <<
"my_index = swap ? lidx[sibling_idx] : my_index;\n";
}
k <<
"barrier(CLK_LOCAL_MEM_FENCE);\n" <<
"lkeys[lid] = my_key;\n";
if(sort_by_key)
{
k <<
"lidx[lid] = my_index;\n";
}
k <<
"barrier(CLK_LOCAL_MEM_FENCE);\n"
"}\n" << // for
"}\n"; // else
// end of odd-even sort
// save key and value
k <<
"if(gid < count) {\n" <<
keys_first[k.var<const uint_>("gid")] << " = " <<
k.var<key_type>("my_key") << ";\n";
if(sort_by_key)
{
k <<
k.decl<value_type>("my_value") << " = " <<
values_first[k.var<const uint_>("offset + my_index")] << ";\n" <<
"barrier(CLK_GLOBAL_MEM_FENCE);\n" <<
values_first[k.var<const uint_>("gid")] << " = my_value;\n";
}
k <<
// end if
"}\n";
const context &context = queue.get_context();
const device &device = queue.get_device();
::boost::compute::kernel kernel = k.compile(context);
const size_t work_group_size =
pick_bitonic_block_sort_block_size<key_type, uchar_>(
kernel.get_work_group_info<size_t>(
device, CL_KERNEL_WORK_GROUP_SIZE
),
device.get_info<size_t>(CL_DEVICE_LOCAL_MEM_SIZE),
sort_by_key
);
const size_t global_size =
work_group_size * static_cast<size_t>(
std::ceil(float(count) / work_group_size)
);
kernel.set_arg(count_arg, static_cast<uint_>(count));
kernel.set_arg(local_keys_arg, local_buffer<key_type>(work_group_size));
if(sort_by_key) {
kernel.set_arg(local_vals_arg, local_buffer<uchar_>(work_group_size));
}
queue.enqueue_1d_range_kernel(kernel, 0, global_size, work_group_size);
// return size of the block
return work_group_size;
}
InputIterator find_extrema_with_reduce(InputIterator first,
InputIterator last,
::boost::compute::less<
typename std::iterator_traits<
InputIterator
>::value_type
>
compare,
const bool find_minimum,
command_queue &queue)
{
typedef typename std::iterator_traits<InputIterator>::difference_type difference_type;
typedef typename std::iterator_traits<InputIterator>::value_type input_type;
const context &context = queue.get_context();
const device &device = queue.get_device();
// Getting information about used queue and device
const size_t compute_units_no = device.get_info<CL_DEVICE_MAX_COMPUTE_UNITS>();
const size_t max_work_group_size = device.get_info<CL_DEVICE_MAX_WORK_GROUP_SIZE>();
const size_t count = detail::iterator_range_size(first, last);
std::string cache_key = std::string("__boost_find_extrema_with_reduce_")
+ type_name<input_type>();
// load parameters
boost::shared_ptr<parameter_cache> parameters =
detail::parameter_cache::get_global_cache(device);
// get preferred work group size and preferred number
// of work groups per compute unit
size_t work_group_size = parameters->get(cache_key, "wgsize", 256);
size_t work_groups_per_cu = parameters->get(cache_key, "wgpcu", 64);
// calculate work group size and number of work groups
work_group_size = (std::min)(max_work_group_size, work_group_size);
size_t work_groups_no = compute_units_no * work_groups_per_cu;
work_groups_no = (std::min)(
work_groups_no,
static_cast<size_t>(std::ceil(float(count) / work_group_size))
);
// phase I: finding candidates for extremum
// device buffors for extremum candidates and their indices
// each work-group computes its candidate
// zero-copy buffers are used to eliminate copying data back to host
vector<input_type, ::boost::compute::pinned_allocator<input_type> >
candidates(work_groups_no, context);
vector<uint_, ::boost::compute::pinned_allocator <uint_> >
candidates_idx(work_groups_no, context);
// finding candidates for first extremum and their indices
find_extrema_with_reduce(
first, count, candidates.begin(), candidates_idx.begin(),
work_groups_no, work_group_size, compare, find_minimum, queue
);
// phase II: finding extremum from among the candidates
// mapping candidates and their indices to host
input_type* candidates_host_ptr =
static_cast<input_type*>(
queue.enqueue_map_buffer(
candidates.get_buffer(), command_queue::map_read,
0, work_groups_no * sizeof(input_type)
)
);
uint_* candidates_idx_host_ptr =
static_cast<uint_*>(
queue.enqueue_map_buffer(
candidates_idx.get_buffer(), command_queue::map_read,
0, work_groups_no * sizeof(uint_)
)
);
input_type* i = candidates_host_ptr;
uint_* idx = candidates_idx_host_ptr;
uint_* extremum_idx = idx;
input_type extremum = *candidates_host_ptr;
i++; idx++;
// find extremum (serial) from among the candidates on host
if(!find_minimum) {
while(idx != (candidates_idx_host_ptr + work_groups_no)) {
input_type next = *i;
bool compare_result = next > extremum;
bool equal = next == extremum;
extremum = compare_result ? next : extremum;
extremum_idx = compare_result ? idx : extremum_idx;
extremum_idx = equal ? ((*extremum_idx < *idx) ? extremum_idx : idx) : extremum_idx;
idx++, i++;
}
}
else {
while(idx != (candidates_idx_host_ptr + work_groups_no)) {
input_type next = *i;
bool compare_result = next < extremum;
bool equal = next == extremum;
extremum = compare_result ? next : extremum;
extremum_idx = compare_result ? idx : extremum_idx;
extremum_idx = equal ? ((*extremum_idx < *idx) ? extremum_idx : idx) : extremum_idx;
idx++, i++;
}
}
return first + static_cast<difference_type>(*extremum_idx);
}
size_t reduce(InputIterator first,
size_t count,
OutputIterator result,
size_t block_size,
BinaryFunction function,
command_queue &queue)
{
typedef typename
std::iterator_traits<InputIterator>::value_type
input_type;
typedef typename
boost::compute::result_of<BinaryFunction(input_type, input_type)>::type
result_type;
const context &context = queue.get_context();
size_t block_count = count / 2 / block_size;
size_t total_block_count =
static_cast<size_t>(std::ceil(float(count) / 2.f / float(block_size)));
if(block_count != 0){
meta_kernel k("block_reduce");
size_t output_arg = k.add_arg<result_type *>(memory_object::global_memory, "output");
size_t block_arg = k.add_arg<input_type *>(memory_object::local_memory, "block");
k <<
"const uint gid = get_global_id(0);\n" <<
"const uint lid = get_local_id(0);\n" <<
// copy values to local memory
"block[lid] = " <<
function(first[k.make_var<uint_>("gid*2+0")],
first[k.make_var<uint_>("gid*2+1")]) << ";\n" <<
// perform reduction
"for(uint i = 1; i < " << uint_(block_size) << "; i <<= 1){\n" <<
" barrier(CLK_LOCAL_MEM_FENCE);\n" <<
" uint mask = (i << 1) - 1;\n" <<
" if((lid & mask) == 0){\n" <<
" block[lid] = " <<
function(k.expr<input_type>("block[lid]"),
k.expr<input_type>("block[lid+i]")) << ";\n" <<
" }\n" <<
"}\n" <<
// write block result to global output
"if(lid == 0)\n" <<
" output[get_group_id(0)] = block[0];\n";
kernel kernel = k.compile(context);
kernel.set_arg(output_arg, result.get_buffer());
kernel.set_arg(block_arg, local_buffer<input_type>(block_size));
queue.enqueue_1d_range_kernel(kernel,
0,
block_count * block_size,
block_size);
}
// serially reduce any leftovers
if(block_count * block_size * 2 < count){
size_t last_block_start = block_count * block_size * 2;
meta_kernel k("extra_serial_reduce");
size_t count_arg = k.add_arg<uint_>("count");
size_t offset_arg = k.add_arg<uint_>("offset");
size_t output_arg = k.add_arg<result_type *>(memory_object::global_memory, "output");
size_t output_offset_arg = k.add_arg<uint_>("output_offset");
k <<
k.decl<result_type>("result") << " = \n" <<
first[k.expr<uint_>("offset")] << ";\n" <<
"for(uint i = offset + 1; i < count; i++)\n" <<
" result = " <<
function(k.var<result_type>("result"),
first[k.var<uint_>("i")]) << ";\n" <<
"output[output_offset] = result;\n";
kernel kernel = k.compile(context);
kernel.set_arg(count_arg, static_cast<uint_>(count));
kernel.set_arg(offset_arg, static_cast<uint_>(last_block_start));
kernel.set_arg(output_arg, result.get_buffer());
kernel.set_arg(output_offset_arg, static_cast<uint_>(block_count));
queue.enqueue_task(kernel);
}
return total_block_count;
}
/// Returns raw context id for the given queue.
inline context_id get_context_id(const command_queue &q) {
return q.get_context().get();
}
inline void inplace_reduce(Iterator first,
Iterator last,
BinaryFunction function,
command_queue &queue)
{
typedef typename
std::iterator_traits<Iterator>::value_type
value_type;
size_t input_size = iterator_range_size(first, last);
if(input_size < 2){
return;
}
const context &context = queue.get_context();
size_t block_size = 64;
size_t values_per_thread = 8;
size_t block_count = input_size / (block_size * values_per_thread);
if(block_count * block_size * values_per_thread != input_size)
block_count++;
vector<value_type> output(block_count, context);
meta_kernel k("inplace_reduce");
size_t input_arg = k.add_arg<value_type *>(memory_object::global_memory, "input");
size_t input_size_arg = k.add_arg<const uint_>("input_size");
size_t output_arg = k.add_arg<value_type *>(memory_object::global_memory, "output");
size_t scratch_arg = k.add_arg<value_type *>(memory_object::local_memory, "scratch");
k <<
"const uint gid = get_global_id(0);\n" <<
"const uint lid = get_local_id(0);\n" <<
"const uint values_per_thread =\n"
<< uint_(values_per_thread) << ";\n" <<
// thread reduce
"const uint index = gid * values_per_thread;\n" <<
"if(index < input_size){\n" <<
k.decl<value_type>("sum") << " = input[index];\n" <<
"for(uint i = 1;\n" <<
"i < values_per_thread && (index + i) < input_size;\n" <<
"i++){\n" <<
" sum = " <<
function(k.var<value_type>("sum"),
k.var<value_type>("input[index+i]")) << ";\n" <<
"}\n" <<
"scratch[lid] = sum;\n" <<
"}\n" <<
// local reduce
"for(uint i = 1; i < get_local_size(0); i <<= 1){\n" <<
" barrier(CLK_LOCAL_MEM_FENCE);\n" <<
" uint mask = (i << 1) - 1;\n" <<
" uint next_index = (gid + i) * values_per_thread;\n"
" if((lid & mask) == 0 && next_index < input_size){\n" <<
" scratch[lid] = " <<
function(k.var<value_type>("scratch[lid]"),
k.var<value_type>("scratch[lid+i]")) << ";\n" <<
" }\n" <<
"}\n" <<
// write output for block
"if(lid == 0){\n" <<
" output[get_group_id(0)] = scratch[0];\n" <<
"}\n"
;
const buffer *input_buffer = &first.get_buffer();
const buffer *output_buffer = &output.get_buffer();
kernel kernel = k.compile(context);
while(input_size > 1){
kernel.set_arg(input_arg, *input_buffer);
kernel.set_arg(input_size_arg, static_cast<uint_>(input_size));
kernel.set_arg(output_arg, *output_buffer);
kernel.set_arg(scratch_arg, local_buffer<value_type>(block_size));
queue.enqueue_1d_range_kernel(kernel,
0,
block_count * block_size,
block_size);
input_size =
static_cast<size_t>(
std::ceil(float(input_size) / (block_size * values_per_thread)
)
);
block_count = input_size / (block_size * values_per_thread);
if(block_count * block_size * values_per_thread != input_size)
block_count++;
std::swap(input_buffer, output_buffer);
}
if(input_buffer != &first.get_buffer()){
::boost::compute::copy(output.begin(),
output.begin() + 1,
first,
queue);
}
}
/// Create command queue on the same context and device as the given one.
inline command_queue duplicate_queue(const command_queue &q) {
return command_queue(q.get_context(), q.get_device(), q.get_properties());
}
/// Returns device associated with the given queue.
inline device get_device(const command_queue &q) {
return q.get_device();
}
inline OutputIterator scan_on_cpu(InputIterator first,
InputIterator last,
OutputIterator result,
bool exclusive,
T init,
BinaryOperator op,
command_queue &queue)
{
if(first == last){
return result;
}
typedef typename
std::iterator_traits<InputIterator>::value_type input_type;
typedef typename
std::iterator_traits<OutputIterator>::value_type output_type;
const context &context = queue.get_context();
// create scan kernel
meta_kernel k("scan_on_cpu");
// Arguments
size_t n_arg = k.add_arg<ulong_>("n");
size_t init_arg = k.add_arg<output_type>("initial_value");
if(!exclusive){
k <<
k.decl<const ulong_>("start_idx") << " = 1;\n" <<
k.decl<output_type>("sum") << " = " << first[0] << ";\n" <<
result[0] << " = sum;\n";
}
else {
k <<
k.decl<const ulong_>("start_idx") << " = 0;\n" <<
k.decl<output_type>("sum") << " = initial_value;\n";
}
k <<
"for(ulong i = start_idx; i < n; i++){\n" <<
k.decl<const input_type>("x") << " = "
<< first[k.var<ulong_>("i")] << ";\n";
if(exclusive){
k << result[k.var<ulong_>("i")] << " = sum;\n";
}
k << " sum = "
<< op(k.var<output_type>("sum"), k.var<output_type>("x"))
<< ";\n";
if(!exclusive){
k << result[k.var<ulong_>("i")] << " = sum;\n";
}
k << "}\n";
// compile scan kernel
kernel scan_kernel = k.compile(context);
// setup kernel arguments
size_t n = detail::iterator_range_size(first, last);
scan_kernel.set_arg<ulong_>(n_arg, n);
scan_kernel.set_arg<output_type>(init_arg, static_cast<output_type>(init));
// execute the kernel
queue.enqueue_1d_range_kernel(scan_kernel, 0, 1, 1);
// return iterator pointing to the end of the result range
return result + n;
}
inline void radix_sort_impl(const buffer_iterator<T> first,
const buffer_iterator<T> last,
const buffer_iterator<T2> values_first,
const bool ascending,
command_queue &queue)
{
typedef T value_type;
typedef typename radix_sort_value_type<sizeof(T)>::type sort_type;
const device &device = queue.get_device();
const context &context = queue.get_context();
// if we have a valid values iterator then we are doing a
// sort by key and have to set up the values buffer
bool sort_by_key = (values_first.get_buffer().get() != 0);
// load (or create) radix sort program
std::string cache_key =
std::string("__boost_radix_sort_") + type_name<value_type>();
if(sort_by_key){
cache_key += std::string("_with_") + type_name<T2>();
}
boost::shared_ptr<program_cache> cache =
program_cache::get_global_cache(context);
boost::shared_ptr<parameter_cache> parameters =
detail::parameter_cache::get_global_cache(device);
// sort parameters
const uint_ k = parameters->get(cache_key, "k", 4);
const uint_ k2 = 1 << k;
const uint_ block_size = parameters->get(cache_key, "tpb", 128);
// sort program compiler options
std::stringstream options;
options << "-DK_BITS=" << k;
options << " -DT=" << type_name<sort_type>();
options << " -DBLOCK_SIZE=" << block_size;
if(boost::is_floating_point<value_type>::value){
options << " -DIS_FLOATING_POINT";
}
if(boost::is_signed<value_type>::value){
options << " -DIS_SIGNED";
}
if(sort_by_key){
options << " -DSORT_BY_KEY";
options << " -DT2=" << type_name<T2>();
options << enable_double<T2>();
}
if(ascending){
options << " -DASC";
}
// load radix sort program
program radix_sort_program = cache->get_or_build(
cache_key, options.str(), radix_sort_source, context
);
kernel count_kernel(radix_sort_program, "count");
kernel scan_kernel(radix_sort_program, "scan");
kernel scatter_kernel(radix_sort_program, "scatter");
size_t count = detail::iterator_range_size(first, last);
uint_ block_count = static_cast<uint_>(count / block_size);
if(block_count * block_size != count){
block_count++;
}
// setup temporary buffers
vector<value_type> output(count, context);
vector<T2> values_output(sort_by_key ? count : 0, context);
vector<uint_> offsets(k2, context);
vector<uint_> counts(block_count * k2, context);
const buffer *input_buffer = &first.get_buffer();
uint_ input_offset = static_cast<uint_>(first.get_index());
const buffer *output_buffer = &output.get_buffer();
uint_ output_offset = 0;
const buffer *values_input_buffer = &values_first.get_buffer();
uint_ values_input_offset = static_cast<uint_>(values_first.get_index());
const buffer *values_output_buffer = &values_output.get_buffer();
uint_ values_output_offset = 0;
for(uint_ i = 0; i < sizeof(sort_type) * CHAR_BIT / k; i++){
// write counts
count_kernel.set_arg(0, *input_buffer);
count_kernel.set_arg(1, input_offset);
count_kernel.set_arg(2, static_cast<uint_>(count));
count_kernel.set_arg(3, counts);
count_kernel.set_arg(4, offsets);
count_kernel.set_arg(5, block_size * sizeof(uint_), 0);
count_kernel.set_arg(6, i * k);
queue.enqueue_1d_range_kernel(count_kernel,
0,
block_count * block_size,
block_size);
// scan counts
if(k == 1){
typedef uint2_ counter_type;
::boost::compute::exclusive_scan(
make_buffer_iterator<counter_type>(counts.get_buffer(), 0),
make_buffer_iterator<counter_type>(counts.get_buffer(), counts.size() / 2),
make_buffer_iterator<counter_type>(counts.get_buffer()),
queue
);
}
else if(k == 2){
typedef uint4_ counter_type;
::boost::compute::exclusive_scan(
make_buffer_iterator<counter_type>(counts.get_buffer(), 0),
make_buffer_iterator<counter_type>(counts.get_buffer(), counts.size() / 4),
make_buffer_iterator<counter_type>(counts.get_buffer()),
queue
);
}
else if(k == 4){
typedef uint16_ counter_type;
::boost::compute::exclusive_scan(
make_buffer_iterator<counter_type>(counts.get_buffer(), 0),
make_buffer_iterator<counter_type>(counts.get_buffer(), counts.size() / 16),
make_buffer_iterator<counter_type>(counts.get_buffer()),
queue
);
}
else {
BOOST_ASSERT(false && "unknown k");
break;
}
// scan global offsets
scan_kernel.set_arg(0, counts);
scan_kernel.set_arg(1, offsets);
scan_kernel.set_arg(2, block_count);
queue.enqueue_task(scan_kernel);
// scatter values
scatter_kernel.set_arg(0, *input_buffer);
scatter_kernel.set_arg(1, input_offset);
scatter_kernel.set_arg(2, static_cast<uint_>(count));
scatter_kernel.set_arg(3, i * k);
scatter_kernel.set_arg(4, counts);
scatter_kernel.set_arg(5, offsets);
scatter_kernel.set_arg(6, *output_buffer);
scatter_kernel.set_arg(7, output_offset);
if(sort_by_key){
scatter_kernel.set_arg(8, *values_input_buffer);
scatter_kernel.set_arg(9, values_input_offset);
scatter_kernel.set_arg(10, *values_output_buffer);
scatter_kernel.set_arg(11, values_output_offset);
}
queue.enqueue_1d_range_kernel(scatter_kernel,
0,
block_count * block_size,
block_size);
// swap buffers
std::swap(input_buffer, output_buffer);
std::swap(values_input_buffer, values_output_buffer);
std::swap(input_offset, output_offset);
std::swap(values_input_offset, values_output_offset);
}
}
void start()
{
_actorStarted = true;
_actorQueue.start();
_actorThread = std::thread( &actor<COMMAND,RETURN_VALUE>::_entry_point, this );
}
void stop()
{
_actorStarted = false;
_actorQueue.stop();
_actorThread.join();
}
inline OutputIterator scan_on_cpu(InputIterator first,
InputIterator last,
OutputIterator result,
bool exclusive,
T init,
BinaryOperator op,
command_queue &queue)
{
typedef typename
std::iterator_traits<InputIterator>::value_type input_type;
typedef typename
std::iterator_traits<OutputIterator>::value_type output_type;
const context &context = queue.get_context();
const device &device = queue.get_device();
const size_t compute_units = queue.get_device().compute_units();
boost::shared_ptr<parameter_cache> parameters =
detail::parameter_cache::get_global_cache(device);
std::string cache_key =
"__boost_scan_cpu_" + boost::lexical_cast<std::string>(sizeof(T));
// for inputs smaller than serial_scan_threshold
// serial_scan algorithm is used
uint_ serial_scan_threshold =
parameters->get(cache_key, "serial_scan_threshold", 16384 * sizeof(T));
serial_scan_threshold =
(std::max)(serial_scan_threshold, uint_(compute_units));
size_t count = detail::iterator_range_size(first, last);
if(count == 0){
return result;
}
else if(count < serial_scan_threshold) {
return serial_scan(first, last, result, exclusive, init, op, queue);
}
buffer block_partial_sums(context, sizeof(output_type) * compute_units );
// create scan kernel
meta_kernel k("scan_on_cpu_block_scan");
// Arguments
size_t count_arg = k.add_arg<uint_>("count");
size_t init_arg = k.add_arg<output_type>("initial_value");
size_t block_partial_sums_arg =
k.add_arg<output_type *>(memory_object::global_memory, "block_partial_sums");
k <<
"uint block = " <<
"(uint)ceil(((float)count)/(get_global_size(0) + 1));\n" <<
"uint index = get_global_id(0) * block;\n" <<
"uint end = min(count, index + block);\n";
if(!exclusive){
k <<
k.decl<output_type>("sum") << " = " <<
first[k.var<uint_>("index")] << ";\n" <<
result[k.var<uint_>("index")] << " = sum;\n" <<
"index++;\n";
}
else {
k <<
k.decl<output_type>("sum") << ";\n" <<
"if(index == 0){\n" <<
"sum = initial_value;\n" <<
"}\n" <<
"else {\n" <<
"sum = " << first[k.var<uint_>("index")] << ";\n" <<
"index++;\n" <<
"}\n";
}
k <<
"while(index < end){\n" <<
// load next value
k.decl<const input_type>("value") << " = "
<< first[k.var<uint_>("index")] << ";\n";
if(exclusive){
k <<
"if(get_global_id(0) == 0){\n" <<
result[k.var<uint_>("index")] << " = sum;\n" <<
"}\n";
}
k <<
"sum = " << op(k.var<output_type>("sum"),
k.var<output_type>("value")) << ";\n";
if(!exclusive){
k <<
"if(get_global_id(0) == 0){\n" <<
result[k.var<uint_>("index")] << " = sum;\n" <<
"}\n";
}
k <<
"index++;\n" <<
"}\n" << // end while
"block_partial_sums[get_global_id(0)] = sum;\n";
// compile scan kernel
kernel block_scan_kernel = k.compile(context);
// setup kernel arguments
block_scan_kernel.set_arg(count_arg, static_cast<uint_>(count));
block_scan_kernel.set_arg(init_arg, static_cast<output_type>(init));
block_scan_kernel.set_arg(block_partial_sums_arg, block_partial_sums);
// execute the kernel
size_t global_work_size = compute_units;
queue.enqueue_1d_range_kernel(block_scan_kernel, 0, global_work_size, 0);
// scan is done
if(compute_units < 2) {
return result + count;
}
// final scan kernel
meta_kernel l("scan_on_cpu_final_scan");
// Arguments
count_arg = l.add_arg<uint_>("count");
block_partial_sums_arg =
l.add_arg<output_type *>(memory_object::global_memory, "block_partial_sums");
l <<
"uint block = " <<
"(uint)ceil(((float)count)/(get_global_size(0) + 1));\n" <<
"uint index = block + get_global_id(0) * block;\n" <<
"uint end = min(count, index + block);\n" <<
k.decl<output_type>("sum") << " = block_partial_sums[0];\n" <<
"for(uint i = 0; i < get_global_id(0); i++) {\n" <<
"sum = " << op(k.var<output_type>("sum"),
k.var<output_type>("block_partial_sums[i + 1]")) << ";\n" <<
"}\n" <<
"while(index < end){\n";
if(exclusive){
l <<
l.decl<output_type>("value") << " = "
<< first[k.var<uint_>("index")] << ";\n" <<
result[k.var<uint_>("index")] << " = sum;\n" <<
"sum = " << op(k.var<output_type>("sum"),
k.var<output_type>("value")) << ";\n";
}
else {
l <<
"sum = " << op(k.var<output_type>("sum"),
first[k.var<uint_>("index")]) << ";\n" <<
result[k.var<uint_>("index")] << " = sum;\n";
}
l <<
"index++;\n" <<
"}\n";
// compile scan kernel
kernel final_scan_kernel = l.compile(context);
// setup kernel arguments
final_scan_kernel.set_arg(count_arg, static_cast<uint_>(count));
final_scan_kernel.set_arg(block_partial_sums_arg, block_partial_sums);
// execute the kernel
global_work_size = compute_units;
queue.enqueue_1d_range_kernel(final_scan_kernel, 0, global_work_size, 0);
// return iterator pointing to the end of the result range
return result + count;
}
/// Create command queue on the same context and device as the given one.
inline command_queue duplicate_queue(const command_queue &q) {
return command_queue(q.context(), q.device(), q.flags());
}
/// Binds the specified CUDA context to the calling CPU thread.
inline void select_context(const command_queue &q) {
q.context().set_current();
}
static inline std::vector<uint>
pad_vector(command_queue &q, const V &v, uint x) {
std::vector<uint> w { v.begin(), v.end() };
w.resize(q.device().max_block_size().size(), x);
return w;
}
/// Create and build a program from source string.
inline vex::backend::program build_sources(
const command_queue &queue, const std::string &source,
const std::string &options = ""
)
{
#ifdef VEXCL_SHOW_KERNELS
std::cout << source << std::endl;
#else
if (getenv("VEXCL_SHOW_KERNELS"))
std::cout << source << std::endl;
#endif
std::string compile_options = options + " " + get_compile_options(queue);
queue.context().set_current();
auto cc = queue.device().compute_capability();
std::ostringstream ccstr;
ccstr << std::get<0>(cc) << std::get<1>(cc);
sha1_hasher sha1;
sha1.process(source)
.process(queue.device().name())
.process(compile_options)
.process(ccstr.str())
;
std::string hash = static_cast<std::string>(sha1);
// Write source to a .cu file
std::string basename = program_binaries_path(hash, true) + "kernel";
std::string ptxfile = basename + ".ptx";
if ( !boost::filesystem::exists(ptxfile) ) {
std::string cufile = basename + ".cu";
{
std::ofstream f(cufile);
f << source;
}
// Compile the source to ptx.
std::ostringstream cmdline;
cmdline
<< "nvcc -ptx -O3"
<< " -arch=sm_" << std::get<0>(cc) << std::get<1>(cc)
<< " " << compile_options
<< " -o " << ptxfile << " " << cufile;
if (0 != system(cmdline.str().c_str()) ) {
#ifndef VEXCL_SHOW_KERNELS
std::cerr << source << std::endl;
#endif
vex::detail::print_backtrace();
throw std::runtime_error("nvcc invocation failed");
}
}
// Load the compiled ptx.
CUmodule prg;
cuda_check( cuModuleLoad(&prg, ptxfile.c_str()) );
return program(queue.context(), prg);
}
bool operator()(const command_queue &a, const command_queue &b) const {
return a.get() < b.get();
}
/// Standard number of workgroups to launch on a device.
static inline size_t num_workgroups(const command_queue &q) {
return 8 * q.device().multiprocessor_count();
}
/// Checks if the compute device is CPU.
inline bool is_cpu(const command_queue &q) {
return q.get_device().get_info<cl_device_type>(CL_DEVICE_TYPE) & CL_DEVICE_TYPE_CPU;
}
/// The size in bytes of shared memory per block available for this kernel.
size_t max_shared_memory_per_block(const command_queue &q) const {
return q.device().max_shared_memory_per_block() -
shared_size_bytes();
}
/// Returns id of the device associated with the given queue.
inline device_id get_device_id(const command_queue &q) {
return q.get_device().get();
}
inline size_t serial_reduce_by_key(InputKeyIterator keys_first,
InputKeyIterator keys_last,
InputValueIterator values_first,
OutputKeyIterator keys_result,
OutputValueIterator values_result,
BinaryFunction function,
BinaryPredicate predicate,
command_queue &queue)
{
typedef typename
std::iterator_traits<InputValueIterator>::value_type value_type;
typedef typename
std::iterator_traits<InputKeyIterator>::value_type key_type;
typedef typename
::boost::compute::result_of<BinaryFunction(value_type, value_type)>::type result_type;
const context &context = queue.get_context();
size_t count = detail::iterator_range_size(keys_first, keys_last);
if(count < 1){
return count;
}
meta_kernel k("serial_reduce_by_key");
size_t count_arg = k.add_arg<uint_>("count");
size_t result_size_arg = k.add_arg<uint_ *>(memory_object::global_memory,
"result_size");
k <<
k.decl<result_type>("result") <<
" = " << values_first[0] << ";\n" <<
k.decl<key_type>("previous_key") << " = " << keys_first[0] << ";\n" <<
k.decl<result_type>("value") << ";\n" <<
k.decl<key_type>("key") << ";\n" <<
k.decl<uint_>("size") << " = 1;\n" <<
keys_result[0] << " = previous_key;\n" <<
values_result[0] << " = result;\n" <<
"for(ulong i = 1; i < count; i++) {\n" <<
" value = " << values_first[k.var<uint_>("i")] << ";\n" <<
" key = " << keys_first[k.var<uint_>("i")] << ";\n" <<
" if (" << predicate(k.var<key_type>("previous_key"),
k.var<key_type>("key")) << ") {\n" <<
" result = " << function(k.var<result_type>("result"),
k.var<result_type>("value")) << ";\n" <<
" }\n " <<
" else { \n" <<
keys_result[k.var<uint_>("size - 1")] << " = previous_key;\n" <<
values_result[k.var<uint_>("size - 1")] << " = result;\n" <<
" result = value;\n" <<
" size++;\n" <<
" } \n" <<
" previous_key = key;\n" <<
"}\n" <<
keys_result[k.var<uint_>("size - 1")] << " = previous_key;\n" <<
values_result[k.var<uint_>("size - 1")] << " = result;\n" <<
"*result_size = size;";
kernel kernel = k.compile(context);
scalar<uint_> result_size(context);
kernel.set_arg(result_size_arg, result_size.get_buffer());
kernel.set_arg(count_arg, static_cast<uint_>(count));
queue.enqueue_task(kernel);
return static_cast<size_t>(result_size.read(queue));
}
/// Returns context for the given queue.
inline context get_context(const command_queue &q) {
return q.get_context();
}
inline void reduce_on_gpu(InputIterator first,
InputIterator last,
buffer_iterator<T> result,
Function function,
command_queue &queue)
{
const device &device = queue.get_device();
const context &context = queue.get_context();
detail::meta_kernel k("reduce");
k.add_arg<const T*>(memory_object::global_memory, "input");
k.add_arg<const uint_>("offset");
k.add_arg<const uint_>("count");
k.add_arg<T*>(memory_object::global_memory, "output");
k.add_arg<const uint_>("output_offset");
k <<
k.decl<const uint_>("block_offset") << " = get_group_id(0) * VPT * TPB;\n" <<
"__global const " << type_name<T>() << " *block = input + offset + block_offset;\n" <<
k.decl<const uint_>("lid") << " = get_local_id(0);\n" <<
"__local " << type_name<T>() << " scratch[TPB];\n" <<
// private reduction
k.decl<T>("sum") << " = 0;\n" <<
"for(uint i = 0; i < VPT; i++){\n" <<
" if(block_offset + lid + i*TPB < count){\n" <<
" sum = sum + block[lid+i*TPB]; \n" <<
" }\n" <<
"}\n" <<
"scratch[lid] = sum;\n";
// discrimination on vendor name
if(is_nvidia_device(device))
k << ReduceBody<T,true>::body();
else
k << ReduceBody<T,false>::body();
k <<
// write sum to output
"if(lid == 0){\n" <<
" output[output_offset + get_group_id(0)] = scratch[0];\n" <<
"}\n";
std::string cache_key = std::string("__boost_reduce_on_gpu_") + type_name<T>();
// load parameters
boost::shared_ptr<parameter_cache> parameters =
detail::parameter_cache::get_global_cache(device);
uint_ vpt = parameters->get(cache_key, "vpt", 8);
uint_ tpb = parameters->get(cache_key, "tpb", 128);
// reduce program compiler flags
std::stringstream options;
options << "-DT=" << type_name<T>()
<< " -DVPT=" << vpt
<< " -DTPB=" << tpb;
// load program
boost::shared_ptr<program_cache> cache =
program_cache::get_global_cache(context);
program reduce_program = cache->get_or_build(
cache_key, options.str(), k.source(), context
);
// create reduce kernel
kernel reduce_kernel(reduce_program, "reduce");
size_t count = std::distance(first, last);
// first pass, reduce from input to ping
buffer ping(context, std::ceil(float(count) / vpt / tpb) * sizeof(T));
initial_reduce(first, last, ping, function, reduce_kernel, vpt, tpb, queue);
// update count after initial reduce
count = std::ceil(float(count) / vpt / tpb);
// middle pass(es), reduce between ping and pong
const buffer *input_buffer = &ping;
buffer pong(context, count / vpt / tpb * sizeof(T));
const buffer *output_buffer = &pong;
if(count > vpt * tpb){
while(count > vpt * tpb){
reduce_kernel.set_arg(0, *input_buffer);
reduce_kernel.set_arg(1, uint_(0));
reduce_kernel.set_arg(2, uint_(count));
reduce_kernel.set_arg(3, *output_buffer);
reduce_kernel.set_arg(4, uint_(0));
size_t work_size = std::ceil(float(count) / vpt);
if(work_size % tpb != 0){
work_size += tpb - work_size % tpb;
}
queue.enqueue_1d_range_kernel(reduce_kernel, 0, work_size, tpb);
std::swap(input_buffer, output_buffer);
count = std::ceil(float(count) / vpt / tpb);
}
}
// final pass, reduce from ping/pong to result
reduce_kernel.set_arg(0, *input_buffer);
reduce_kernel.set_arg(1, uint_(0));
reduce_kernel.set_arg(2, uint_(count));
reduce_kernel.set_arg(3, result.get_buffer());
reduce_kernel.set_arg(4, uint_(result.get_index()));
queue.enqueue_1d_range_kernel(reduce_kernel, 0, tpb, tpb);
}
InputIterator find_extrema_with_reduce(InputIterator first,
InputIterator last,
Compare compare,
const bool find_minimum,
command_queue &queue)
{
typedef typename std::iterator_traits<InputIterator>::difference_type difference_type;
typedef typename std::iterator_traits<InputIterator>::value_type input_type;
const context &context = queue.get_context();
const device &device = queue.get_device();
// Getting information about used queue and device
const size_t compute_units_no = device.get_info<CL_DEVICE_MAX_COMPUTE_UNITS>();
const size_t max_work_group_size = device.get_info<CL_DEVICE_MAX_WORK_GROUP_SIZE>();
const size_t count = detail::iterator_range_size(first, last);
std::string cache_key = std::string("__boost_find_extrema_with_reduce_")
+ type_name<input_type>();
// load parameters
boost::shared_ptr<parameter_cache> parameters =
detail::parameter_cache::get_global_cache(device);
// get preferred work group size and preferred number
// of work groups per compute unit
size_t work_group_size = parameters->get(cache_key, "wgsize", 256);
size_t work_groups_per_cu = parameters->get(cache_key, "wgpcu", 100);
// calculate work group size and number of work groups
work_group_size = (std::min)(max_work_group_size, work_group_size);
size_t work_groups_no = compute_units_no * work_groups_per_cu;
work_groups_no = (std::min)(
work_groups_no,
static_cast<size_t>(std::ceil(float(count) / work_group_size))
);
// phase I: finding candidates for extremum
// device buffors for extremum candidates and their indices
// each work-group computes its candidate
vector<input_type> candidates(work_groups_no, context);
vector<uint_> candidates_idx(work_groups_no, context);
// finding candidates for first extremum and their indices
find_extrema_with_reduce(
first, count, candidates.begin(), candidates_idx.begin(),
work_groups_no, work_group_size, compare, find_minimum, queue
);
// phase II: finding extremum from among the candidates
// zero-copy buffers for final result (value and index)
vector<input_type, ::boost::compute::pinned_allocator<input_type> >
result(1, context);
vector<uint_, ::boost::compute::pinned_allocator<uint_> >
result_idx(1, context);
// get extremum from among the candidates
find_extrema_with_reduce(
candidates.begin(), candidates_idx.begin(), work_groups_no, result.begin(),
result_idx.begin(), 1, work_group_size, compare, find_minimum, true, queue
);
// mapping extremum index to host
uint_* result_idx_host_ptr =
static_cast<uint_*>(
queue.enqueue_map_buffer(
result_idx.get_buffer(), command_queue::map_read,
0, sizeof(uint_)
)
);
return first + static_cast<difference_type>(*result_idx_host_ptr);
}
inline InputIterator find_extrema_on_cpu(InputIterator first,
InputIterator last,
Compare compare,
const bool find_minimum,
command_queue &queue)
{
typedef typename std::iterator_traits<InputIterator>::value_type input_type;
typedef typename std::iterator_traits<InputIterator>::difference_type difference_type;
size_t count = iterator_range_size(first, last);
const device &device = queue.get_device();
const uint_ compute_units = queue.get_device().compute_units();
boost::shared_ptr<parameter_cache> parameters =
detail::parameter_cache::get_global_cache(device);
std::string cache_key =
"__boost_find_extrema_cpu_"
+ boost::lexical_cast<std::string>(sizeof(input_type));
// for inputs smaller than serial_find_extrema_threshold
// serial_find_extrema algorithm is used
uint_ serial_find_extrema_threshold = parameters->get(
cache_key,
"serial_find_extrema_threshold",
16384 * sizeof(input_type)
);
serial_find_extrema_threshold =
(std::max)(serial_find_extrema_threshold, uint_(2 * compute_units));
const context &context = queue.get_context();
if(count < serial_find_extrema_threshold) {
return serial_find_extrema(first, last, compare, find_minimum, queue);
}
meta_kernel k("find_extrema_on_cpu");
buffer output(context, sizeof(input_type) * compute_units);
buffer output_idx(
context, sizeof(uint_) * compute_units,
buffer::read_write | buffer::alloc_host_ptr
);
size_t count_arg = k.add_arg<uint_>("count");
size_t output_arg =
k.add_arg<input_type *>(memory_object::global_memory, "output");
size_t output_idx_arg =
k.add_arg<uint_ *>(memory_object::global_memory, "output_idx");
k <<
"uint block = " <<
"(uint)ceil(((float)count)/get_global_size(0));\n" <<
"uint index = get_global_id(0) * block;\n" <<
"uint end = min(count, index + block);\n" <<
"uint value_index = index;\n" <<
k.decl<input_type>("value") << " = " << first[k.var<uint_>("index")] << ";\n" <<
"index++;\n" <<
"while(index < end){\n" <<
k.decl<input_type>("candidate") <<
" = " << first[k.var<uint_>("index")] << ";\n" <<
"#ifndef BOOST_COMPUTE_FIND_MAXIMUM\n" <<
"bool compare = " << compare(k.var<input_type>("candidate"),
k.var<input_type>("value")) << ";\n" <<
"#else\n" <<
"bool compare = " << compare(k.var<input_type>("value"),
k.var<input_type>("candidate")) << ";\n" <<
"#endif\n" <<
"value = compare ? candidate : value;\n" <<
"value_index = compare ? index : value_index;\n" <<
"index++;\n" <<
"}\n" <<
"output[get_global_id(0)] = value;\n" <<
"output_idx[get_global_id(0)] = value_index;\n";
size_t global_work_size = compute_units;
std::string options;
if(!find_minimum){
options = "-DBOOST_COMPUTE_FIND_MAXIMUM";
}
kernel kernel = k.compile(context, options);
kernel.set_arg(count_arg, static_cast<uint_>(count));
kernel.set_arg(output_arg, output);
kernel.set_arg(output_idx_arg, output_idx);
queue.enqueue_1d_range_kernel(kernel, 0, global_work_size, 0);
buffer_iterator<input_type> result = serial_find_extrema(
make_buffer_iterator<input_type>(output),
make_buffer_iterator<input_type>(output, global_work_size),
compare,
find_minimum,
queue
);
uint_* output_idx_host_ptr =
static_cast<uint_*>(
queue.enqueue_map_buffer(
output_idx, command_queue::map_read,
0, global_work_size * sizeof(uint_)
)
);
difference_type extremum_idx =
static_cast<difference_type>(*(output_idx_host_ptr + result.get_index()));
return first + extremum_idx;
}
inline void find_extrema_with_reduce(InputIterator input,
vector<uint_>::iterator input_idx,
size_t count,
ResultIterator result,
vector<uint_>::iterator result_idx,
size_t work_groups_no,
size_t work_group_size,
Compare compare,
const bool find_minimum,
const bool use_input_idx,
command_queue &queue)
{
typedef typename std::iterator_traits<InputIterator>::value_type input_type;
const context &context = queue.get_context();
meta_kernel k("find_extrema_reduce");
size_t count_arg = k.add_arg<uint_>("count");
size_t block_arg = k.add_arg<input_type *>(memory_object::local_memory, "block");
size_t block_idx_arg = k.add_arg<uint_ *>(memory_object::local_memory, "block_idx");
k <<
// Work item global id
k.decl<const uint_>("gid") << " = get_global_id(0);\n" <<
// Index of element that will be read from input buffer
k.decl<uint_>("idx") << " = gid;\n" <<
k.decl<input_type>("acc") << ";\n" <<
k.decl<uint_>("acc_idx") << ";\n" <<
"if(gid < count) {\n" <<
// Real index of currently best element
"#ifdef BOOST_COMPUTE_USE_INPUT_IDX\n" <<
k.var<uint_>("acc_idx") << " = " << input_idx[k.var<uint_>("idx")] << ";\n" <<
"#else\n" <<
k.var<uint_>("acc_idx") << " = idx;\n" <<
"#endif\n" <<
// Init accumulator with first[get_global_id(0)]
"acc = " << input[k.var<uint_>("idx")] << ";\n" <<
"idx += get_global_size(0);\n" <<
"}\n" <<
k.decl<bool>("compare_result") << ";\n" <<
k.decl<bool>("equal") << ";\n\n" <<
"while( idx < count ){\n" <<
// Next element
k.decl<input_type>("next") << " = " << input[k.var<uint_>("idx")] << ";\n" <<
"#ifdef BOOST_COMPUTE_USE_INPUT_IDX\n" <<
k.decl<input_type>("next_idx") << " = " << input_idx[k.var<uint_>("idx")] << ";\n" <<
"#endif\n" <<
// Comparison between currently best element (acc) and next element
"#ifdef BOOST_COMPUTE_FIND_MAXIMUM\n" <<
"compare_result = " << compare(k.var<input_type>("next"),
k.var<input_type>("acc")) << ";\n" <<
"# ifdef BOOST_COMPUTE_USE_INPUT_IDX\n" <<
"equal = !compare_result && !" <<
compare(k.var<input_type>("acc"),
k.var<input_type>("next")) << ";\n" <<
"# endif\n" <<
"#else\n" <<
"compare_result = " << compare(k.var<input_type>("acc"),
k.var<input_type>("next")) << ";\n" <<
"# ifdef BOOST_COMPUTE_USE_INPUT_IDX\n" <<
"equal = !compare_result && !" <<
compare(k.var<input_type>("next"),
k.var<input_type>("acc")) << ";\n" <<
"# endif\n" <<
"#endif\n" <<
// save the winner
"acc = compare_result ? acc : next;\n" <<
"#ifdef BOOST_COMPUTE_USE_INPUT_IDX\n" <<
"acc_idx = compare_result ? " <<
"acc_idx : " <<
"(equal ? min(acc_idx, next_idx) : next_idx);\n" <<
"#else\n" <<
"acc_idx = compare_result ? acc_idx : idx;\n" <<
"#endif\n" <<
"idx += get_global_size(0);\n" <<
"}\n\n" <<
// Work item local id
k.decl<const uint_>("lid") << " = get_local_id(0);\n" <<
"block[lid] = acc;\n" <<
"block_idx[lid] = acc_idx;\n" <<
"barrier(CLK_LOCAL_MEM_FENCE);\n" <<
k.decl<uint_>("group_offset") <<
" = count - (get_local_size(0) * get_group_id(0));\n\n";
k <<
"#pragma unroll\n"
"for(" << k.decl<uint_>("offset") << " = " << uint_(work_group_size) << " / 2; offset > 0; " <<
"offset = offset / 2) {\n" <<
"if((lid < offset) && ((lid + offset) < group_offset)) { \n" <<
k.decl<input_type>("mine") << " = block[lid];\n" <<
k.decl<input_type>("other") << " = block[lid+offset];\n" <<
"#ifdef BOOST_COMPUTE_FIND_MAXIMUM\n" <<
"compare_result = " << compare(k.var<input_type>("other"),
k.var<input_type>("mine")) << ";\n" <<
"equal = !compare_result && !" <<
compare(k.var<input_type>("mine"),
k.var<input_type>("other")) << ";\n" <<
"#else\n" <<
"compare_result = " << compare(k.var<input_type>("mine"),
k.var<input_type>("other")) << ";\n" <<
"equal = !compare_result && !" <<
compare(k.var<input_type>("other"),
k.var<input_type>("mine")) << ";\n" <<
"#endif\n" <<
"block[lid] = compare_result ? mine : other;\n" <<
k.decl<uint_>("mine_idx") << " = block_idx[lid];\n" <<
k.decl<uint_>("other_idx") << " = block_idx[lid+offset];\n" <<
"block_idx[lid] = compare_result ? " <<
"mine_idx : " <<
"(equal ? min(mine_idx, other_idx) : other_idx);\n" <<
"}\n"
"barrier(CLK_LOCAL_MEM_FENCE);\n" <<
"}\n\n" <<
// write block result to global output
"if(lid == 0){\n" <<
result[k.var<uint_>("get_group_id(0)")] << " = block[0];\n" <<
result_idx[k.var<uint_>("get_group_id(0)")] << " = block_idx[0];\n" <<
"}";
std::string options;
if(!find_minimum){
options = "-DBOOST_COMPUTE_FIND_MAXIMUM";
}
if(use_input_idx){
options += " -DBOOST_COMPUTE_USE_INPUT_IDX";
}
kernel kernel = k.compile(context, options);
kernel.set_arg(count_arg, static_cast<uint_>(count));
kernel.set_arg(block_arg, local_buffer<input_type>(work_group_size));
kernel.set_arg(block_idx_arg, local_buffer<uint_>(work_group_size));
queue.enqueue_1d_range_kernel(kernel,
0,
work_groups_no * work_group_size,
work_group_size);
}
/// Creates a new threefry_engine and seeds it with \p value.
explicit threefry_engine(command_queue &queue)
: m_context(queue.get_context())
{
// setup program
load_program();
}
void generate(OutputIterator first, OutputIterator last, Function op, command_queue &queue)
{
vector<T> tmp(std::distance(first, last), queue.get_context());
generate(tmp.begin(), tmp.end(), queue);
transform(tmp.begin(), tmp.end(), first, op, queue);
}
inline void merge_blocks_on_gpu(KeyIterator keys_first,
ValueIterator values_first,
KeyIterator out_keys_first,
ValueIterator out_values_first,
Compare compare,
const size_t count,
const size_t block_size,
const bool sort_by_key,
command_queue &queue)
{
typedef typename std::iterator_traits<KeyIterator>::value_type key_type;
typedef typename std::iterator_traits<ValueIterator>::value_type value_type;
meta_kernel k("merge_blocks");
size_t count_arg = k.add_arg<const uint_>("count");
size_t block_size_arg = k.add_arg<const uint_>("block_size");
k <<
// get global id
k.decl<const uint_>("gid") << " = get_global_id(0);\n" <<
"if(gid >= count) {\n" <<
"return;\n" <<
"}\n" <<
k.decl<const key_type>("my_key") << " = " <<
keys_first[k.var<const uint_>("gid")] << ";\n";
if(sort_by_key) {
k <<
k.decl<const value_type>("my_value") << " = " <<
values_first[k.var<const uint_>("gid")] << ";\n";
}
k <<
// get my block idx
k.decl<const uint_>("my_block_idx") << " = gid / block_size;\n" <<
k.decl<const bool>("my_block_idx_is_odd") << " = " <<
"my_block_idx & 0x1;\n" <<
k.decl<const uint_>("other_block_idx") << " = " <<
// if(my_block_idx is odd) {} else {}
"my_block_idx_is_odd ? my_block_idx - 1 : my_block_idx + 1;\n" <<
// get ranges of my block and the other block
// [my_block_start; my_block_end)
// [other_block_start; other_block_end)
k.decl<const uint_>("my_block_start") << " = " <<
"min(my_block_idx * block_size, count);\n" << // including
k.decl<const uint_>("my_block_end") << " = " <<
"min((my_block_idx + 1) * block_size, count);\n" << // excluding
k.decl<const uint_>("other_block_start") << " = " <<
"min(other_block_idx * block_size, count);\n" << // including
k.decl<const uint_>("other_block_end") << " = " <<
"min((other_block_idx + 1) * block_size, count);\n" << // excluding
// other block is empty, nothing to merge here
"if(other_block_start == count){\n" <<
out_keys_first[k.var<uint_>("gid")] << " = my_key;\n";
if(sort_by_key) {
k <<
out_values_first[k.var<uint_>("gid")] << " = my_value;\n";
}
k <<
"return;\n" <<
"}\n" <<
// lower bound
// left_idx - lower bound
k.decl<uint_>("left_idx") << " = other_block_start;\n" <<
k.decl<uint_>("right_idx") << " = other_block_end;\n" <<
"while(left_idx < right_idx) {\n" <<
k.decl<uint_>("mid_idx") << " = (left_idx + right_idx) / 2;\n" <<
k.decl<key_type>("mid_key") << " = " <<
keys_first[k.var<const uint_>("mid_idx")] << ";\n" <<
k.decl<bool>("smaller") << " = " <<
compare(k.var<key_type>("mid_key"),
k.var<key_type>("my_key")) << ";\n" <<
"left_idx = smaller ? mid_idx + 1 : left_idx;\n" <<
"right_idx = smaller ? right_idx : mid_idx;\n" <<
"}\n" <<
// left_idx is found position in other block
// if my_block is odd we need to get the upper bound
"right_idx = other_block_end;\n" <<
"if(my_block_idx_is_odd && left_idx != right_idx) {\n" <<
k.decl<key_type>("upper_key") << " = " <<
keys_first[k.var<const uint_>("left_idx")] << ";\n" <<
"while(" <<
"!(" << compare(k.var<key_type>("upper_key"),
k.var<key_type>("my_key")) <<
") && " <<
"!(" << compare(k.var<key_type>("my_key"),
k.var<key_type>("upper_key")) <<
") && " <<
"left_idx < right_idx" <<
")" <<
"{\n" <<
k.decl<uint_>("mid_idx") << " = (left_idx + right_idx) / 2;\n" <<
k.decl<key_type>("mid_key") << " = " <<
keys_first[k.var<const uint_>("mid_idx")] << ";\n" <<
k.decl<bool>("equal") << " = " <<
"!(" << compare(k.var<key_type>("mid_key"),
k.var<key_type>("my_key")) <<
") && " <<
"!(" << compare(k.var<key_type>("my_key"),
k.var<key_type>("mid_key")) <<
");\n" <<
"left_idx = equal ? mid_idx + 1 : left_idx + 1;\n" <<
"right_idx = equal ? right_idx : mid_idx;\n" <<
"upper_key = " <<
keys_first[k.var<const uint_>("left_idx")] << ";\n" <<
"}\n" <<
"}\n" <<
k.decl<uint_>("offset") << " = 0;\n" <<
"offset += gid - my_block_start;\n" <<
"offset += left_idx - other_block_start;\n" <<
"offset += min(my_block_start, other_block_start);\n" <<
out_keys_first[k.var<uint_>("offset")] << " = my_key;\n";
if(sort_by_key) {
k <<
out_values_first[k.var<uint_>("offset")] << " = my_value;\n";
}
const context &context = queue.get_context();
::boost::compute::kernel kernel = k.compile(context);
const size_t work_group_size = (std::min)(
size_t(256),
kernel.get_work_group_info<size_t>(
queue.get_device(), CL_KERNEL_WORK_GROUP_SIZE
)
);
const size_t global_size =
work_group_size * static_cast<size_t>(
std::ceil(float(count) / work_group_size)
);
kernel.set_arg(count_arg, static_cast<uint_>(count));
kernel.set_arg(block_size_arg, static_cast<uint_>(block_size));
queue.enqueue_1d_range_kernel(kernel, 0, global_size, work_group_size);
}