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from collections import defaultdict

import NvRules
from RequestedMetrics import Importance, MetricRequest, RequestedMetricsParser

requested_metrics = [
    MetricRequest("memory_l2_theoretical_sectors_global", "l2_sectors", Importance.OPTIONAL, None, False),
    MetricRequest("memory_l2_theoretical_sectors_global_ideal", "l2_sectors_ideal", Importance.OPTIONAL, None, False),
    MetricRequest("derived__memory_l2_theoretical_sectors_global_excessive", "excessive_sectors", Importance.OPTIONAL, None, False),
    MetricRequest("lts__cycles_active.sum", "l2_cycles_active", Importance.OPTIONAL, 0),
    MetricRequest("lts__cycles_elapsed.sum", "l2_cycles_elapsed", Importance.OPTIONAL, 0),
]


def get_identifier():
    return "UncoalescedGlobalAccess"

def get_name():
    return "Uncoalesced Global Accesses"

def get_description():
    return "Uncoalesced Global Accesses"

def get_section_identifier():
    return "SourceCounters"

def get_parent_rules_identifiers():
    return ["Memory"]


def get_estimated_speedup(metrics):
    """Estimate potential speedup from reducing uncoalesced global memory accesses.

    The performance improvement is approximated as relative proportion of excessive
    L2 sectors weighted by time spent in the L2 unit.

    """
    active_cycles = metrics["l2_cycles_active"].value()
    elapsed_cycles = metrics["l2_cycles_elapsed"].value()
    excessive_sectors = metrics["excessive_sectors"].value()
    total_sectors = metrics["l2_sectors"].value()

    if (elapsed_cycles > 0) and (total_sectors > 0):
        improvement_percent = (
            (active_cycles / elapsed_cycles) * (excessive_sectors / total_sectors) * 100
        )
        return NvRules.IFrontend.SpeedupType_GLOBAL, improvement_percent
    else:
        return NvRules.IFrontend.SpeedupType_LOCAL, 0


def apply(handle):
    ctx = NvRules.get_context(handle)
    action = ctx.range_by_idx(0).action_by_idx(0)
    fe = ctx.frontend()

    metrics = RequestedMetricsParser(handle, action).parse(requested_metrics)
    if any(metric is None for metric in metrics.values()):
        # Not all SASS metrics are available for all supported workload/profile mode
        # combinations, so we skip the rule if any of them are missing.
        return

    l2_sectors_metric = metrics["l2_sectors"]
    l2_sectors_correlation_ids = l2_sectors_metric.correlation_ids()
    ideal_l2_sectors_metric = metrics["l2_sectors_ideal"]
    total_l2_sectors = l2_sectors_metric.value()
    total_ideal_l2_sectors = ideal_l2_sectors_metric.value()
    # No need to check further if total L2 sectors match with the ideal value
    if total_l2_sectors <= total_ideal_l2_sectors:
        return

    num_l2_sectors_instances = l2_sectors_metric.num_instances()
    num_ideal_l2_sectors_instances = ideal_l2_sectors_metric.num_instances()
    # We cannot execute the rule if we don't get the same instance count for both metrics
    if num_l2_sectors_instances != num_ideal_l2_sectors_instances:
        return
    total_diff = 0
    excess_by_line = defaultdict(int)
    total_by_line = defaultdict(int)
    for i in range(num_l2_sectors_instances):
        per_instance_l2_sectors = l2_sectors_metric.as_uint64(i)
        per_instance_ideal_l2_sectors = ideal_l2_sectors_metric.as_uint64(i)
        if (per_instance_l2_sectors != per_instance_ideal_l2_sectors):
            total_diff += abs(per_instance_ideal_l2_sectors - per_instance_l2_sectors)
        # If there are excessive sectors, create source markers in the appropriate places
        if (per_instance_l2_sectors > per_instance_ideal_l2_sectors):
            address = l2_sectors_correlation_ids.as_uint64(i)
            source_info = action.source_info(address)
            excess = abs(per_instance_ideal_l2_sectors - per_instance_l2_sectors)

            # Create source marker in the SASS file
            fe.source_marker("{:.2f}% of this line's global accesses are excessive.".format(excess / per_instance_l2_sectors * 100), address, NvRules.MarkerKind.SASS, NvRules.MsgType.OPTIMIZATION)

            # Aggregate diffs per line for the Source file marker
            if source_info is not None:
                line = source_info.line()
                file_name = source_info.file_name()
                excess_by_line[line] += excess
                total_by_line[line] += per_instance_l2_sectors

    for line_number, local_diff in excess_by_line.items():
        # Create source marker in the Source file per affected line
        fe.source_marker("{:.2f}% of this line's global accesses are excessive.".format(local_diff / total_by_line[line_number] * 100), line_number, NvRules.MarkerKind.SOURCE, file_name, NvRules.MsgType.OPTIMIZATION)

    if total_diff > 0:
        message = "This workload has uncoalesced global accesses resulting in a total of {} excessive sectors ({:.0f}% of the total {} sectors)." \
            " Check the L2 Theoretical Sectors Global Excessive table for the primary source locations." \
            " The @url:CUDA Programming Guide:https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#device-memory-accesses@ has additional information on reducing uncoalesced device memory accesses." \
            .format(total_diff, 100. * total_diff / total_l2_sectors, total_l2_sectors)
        msg_id = fe.message(NvRules.MsgType.OPTIMIZATION, message)

        speedup_type, speedup_value = get_estimated_speedup(metrics)
        fe.speedup(msg_id, speedup_type, speedup_value)

        fe.focus_metric(msg_id, metrics["excessive_sectors"].name(), total_diff, NvRules.IFrontend.Severity_SEVERITY_DEFAULT, "Reduce the number of excessive wavefronts in L2")
        fe.load_chart_from_file("UncoalescedAccess.chart")
