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267 lines (211 loc) · 8.81 KB
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/*
* This file is part of AdaptiveCpp, an implementation of SYCL and C++ standard
* parallelism for CPUs and GPUs.
*
* Copyright The AdaptiveCpp Contributors
*
* AdaptiveCpp is released under the BSD 2-Clause "Simplified" License.
* See file LICENSE in the project root for full license details.
*/
// SPDX-License-Identifier: BSD-2-Clause
#include "hipSYCL/common/debug.hpp"
#include "hipSYCL/runtime/application.hpp"
#include "hipSYCL/runtime/inorder_executor.hpp"
#include "hipSYCL/runtime/multi_queue_executor.hpp"
#include "hipSYCL/runtime/hardware.hpp"
#include "hipSYCL/runtime/dag_direct_scheduler.hpp"
#include "hipSYCL/runtime/generic/multi_event.hpp"
#include "hipSYCL/runtime/hints.hpp"
#include "hipSYCL/runtime/operations.hpp"
#include "hipSYCL/runtime/serialization/serialization.hpp"
#include <algorithm>
#include <limits>
#include <memory>
namespace hipsycl {
namespace rt {
namespace {
std::size_t determine_target_lane(const dag_node_ptr& node,
const node_list_t& nonvirtual_reqs,
const multi_queue_executor* executor,
const moving_statistics& device_submission_statistics,
backend_execution_lane_range lane_range) {
if(lane_range.num_lanes <= 1) {
return lane_range.begin;
}
if(node->get_execution_hints().has_hint<hints::prefer_execution_lane>()) {
std::size_t preferred_lane = node->get_execution_hints()
.get_hint<hints::prefer_execution_lane>()
->get_lane_id();
return lane_range.begin + preferred_lane % lane_range.num_lanes;
}
common::small_vector<int, 8> synchronization_cost(lane_range.num_lanes);
for(auto& req : nonvirtual_reqs){
assert(req);
assert(req->is_submitted());
std::size_t lane_id = 0;
if(executor->find_assigned_lane_index(req, lane_id)) {
if (lane_id >= lane_range.begin &&
lane_id < lane_range.begin + lane_range.num_lanes) {
std::size_t relative_lane_id = lane_id - lane_range.begin;
// Don't consider the event if we already know that it is complete
if(!req->is_known_complete()) {
++synchronization_cost[relative_lane_id];
}
}
}
}
// Select the lane that would have the *highest* synchronization cost,
// because by scheduling to this lane all synchronization becomes noops!
// If there are multiple lanes with same synchronization cost,
// use the one with lower recent utilization
auto lane_usage = device_submission_statistics.build_decaying_bins();
int max_sync_cost = 0;
double min_usage = std::numeric_limits<double>::max();
std::size_t current_best_lane = lane_range.begin;
for (std::size_t i = lane_range.begin;
i < lane_range.begin + lane_range.num_lanes; ++i) {
int sync_cost = synchronization_cost[i-lane_range.begin];
if(sync_cost > max_sync_cost) {
max_sync_cost = synchronization_cost[i-lane_range.begin];
current_best_lane = i;
min_usage = lane_usage[i];
} else if(sync_cost == max_sync_cost) {
if(lane_usage[i] < min_usage) {
min_usage = lane_usage[i];
current_best_lane = i;
}
}
}
return current_best_lane;
}
} // anonymous namespace
multi_queue_executor::multi_queue_executor(
const backend &b, queue_factory_function queue_factory)
: _backend{b.get_unique_backend_id()} {
std::size_t num_devices = b.get_hardware_manager()->get_num_devices();
_device_data.resize(num_devices);
for (std::size_t dev = 0; dev < num_devices; ++dev) {
device_id dev_id = b.get_hardware_manager()->get_device_id(dev);
hardware_context *hw_context = b.get_hardware_manager()->get_device(dev);
std::size_t memcpy_concurrency = hw_context->get_max_memcpy_concurrency();
std::size_t kernel_concurrency = hw_context->get_max_kernel_concurrency();
for (std::size_t i = 0; i < memcpy_concurrency; ++i) {
std::unique_ptr<inorder_queue> new_queue = queue_factory(dev_id);
_managed_queues.push_back(new_queue.get());
_device_data[dev].executors.push_back(
std::make_unique<inorder_executor>(std::move(new_queue)));
}
_device_data[dev].memcpy_lanes.begin = 0;
_device_data[dev].memcpy_lanes.num_lanes = memcpy_concurrency;
for(std::size_t i = 0; i < kernel_concurrency; ++i) {
std::unique_ptr<inorder_queue> new_queue = queue_factory(dev_id);
_managed_queues.push_back(new_queue.get());
_device_data[dev].executors.push_back(
std::make_unique<inorder_executor>(std::move(new_queue)));
}
_device_data[dev].kernel_lanes.begin = memcpy_concurrency;
_device_data[dev].kernel_lanes.num_lanes = kernel_concurrency;
const std::size_t max_statistics_size = application::get_settings()
.get<setting::mqe_lane_statistics_max_size>();
const double statistics_decay_time_sec = application::get_settings()
.get<setting::mqe_lane_statistics_decay_time_sec>();
_device_data[dev].submission_statistics = moving_statistics{
max_statistics_size,
_device_data[dev].executors.size(),
static_cast<std::size_t>(1e9 * statistics_decay_time_sec)};
}
HIPSYCL_DEBUG_INFO << "multi_queue_executor: Spawned for backend "
<< b.get_name() << " with configuration: " << std::endl;
for(std::size_t i = 0; i < _device_data.size(); ++i) {
HIPSYCL_DEBUG_INFO << " device " << i << ": "<< std::endl;
for(std::size_t j = 0; j < _device_data[i].memcpy_lanes.num_lanes; ++j){
std::size_t lane = j + _device_data[i].memcpy_lanes.begin;
HIPSYCL_DEBUG_INFO << " memcpy lane: " << lane << std::endl;
}
for(std::size_t j = 0; j < _device_data[i].kernel_lanes.num_lanes; ++j){
std::size_t lane = j + _device_data[i].kernel_lanes.begin;
HIPSYCL_DEBUG_INFO << " kernel lane: " << lane << std::endl;
}
}
}
bool multi_queue_executor::is_inorder_queue() const {
return false;
}
bool multi_queue_executor::is_outoforder_queue() const {
return true;
}
bool multi_queue_executor::is_taskgraph() const {
return false;
}
backend_execution_lane_range
multi_queue_executor::get_memcpy_execution_lane_range(device_id dev) const {
assert(static_cast<std::size_t>(dev.get_id()) < _device_data.size());
return this->_device_data[dev.get_id()].memcpy_lanes;
}
backend_execution_lane_range
multi_queue_executor::get_kernel_execution_lane_range(device_id dev) const {
assert(static_cast<std::size_t>(dev.get_id()) < _device_data.size());
return this->_device_data[dev.get_id()].kernel_lanes;
}
void multi_queue_executor::submit_directly(
const dag_node_ptr& node, operation *op,
const node_list_t &reqs) {
HIPSYCL_DEBUG_INFO << "multi_queue_executor: Processing node " << node.get()
<< " with " << reqs.size() << " non-virtual requirement(s) and "
<< node->get_requirements().size() << " direct requirement(s)." << std::endl;
assert(!op->is_requirement());
if (node->is_submitted())
return;
std::size_t op_target_lane;
if (op->is_data_transfer()) {
op_target_lane = determine_target_lane(
node, reqs, this,
_device_data[node->get_assigned_device().get_id()].submission_statistics,
_device_data[node->get_assigned_device().get_id()].memcpy_lanes);
} else {
op_target_lane = determine_target_lane(
node, reqs, this,
_device_data[node->get_assigned_device().get_id()].submission_statistics,
_device_data[node->get_assigned_device().get_id()].kernel_lanes);
}
_device_data[node->get_assigned_device().get_id()]
.submission_statistics.insert(op_target_lane);
inorder_executor *executor = _device_data[node->get_assigned_device().get_id()]
.executors[op_target_lane]
.get();
HIPSYCL_DEBUG_INFO
<< "multi_queue_executor: Dispatching to lane " << op_target_lane << ": "
<< dump(op) << std::endl;
return executor->submit_directly(node, op, reqs);
}
bool multi_queue_executor::can_execute_on_device(const device_id &dev) const {
return _backend == dev.get_backend();
}
bool multi_queue_executor::is_submitted_by_me(const dag_node_ptr& node) const {
if(!node->is_submitted())
return false;
for(const auto& d : _device_data) {
for(const auto& executor : d.executors) {
if(executor->is_submitted_by_me(node))
return true;
}
}
return false;
}
bool multi_queue_executor::find_assigned_lane_index(
const dag_node_ptr &node, std::size_t &index_out) const {
if(!node->is_submitted())
return false;
std::size_t dev_id = node->get_assigned_device().get_id();
std::size_t lane_id = 0;
for(const auto& executor : _device_data[dev_id].executors) {
if(executor->is_submitted_by_me(node)) {
index_out = lane_id;
return true;
}
++lane_id;
}
return false;
}
}
}