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import NvRules
from RequestedMetrics import MetricRequest, RequestedMetricsParser, Importance

requested_metrics = [
    MetricRequest("device__attribute_compute_capability_major", "cc_major"),
    MetricRequest("device__attribute_compute_capability_minor", "cc_minor"),
    MetricRequest(
        "lts__gcomp_input_sectors.sum",
        "compression_input_sectors",
        Importance.OPTIONAL,
        0,
        False,
    ),
    MetricRequest(
        "lts__average_gcomp_input_sector_success_rate.pct",
        "compression_success_rate",
        Importance.OPTIONAL,
        1,
    ),
    MetricRequest("lts__t_sectors.sum", "input_sectors"),
    MetricRequest("l1tex__m_l1tex2xbar_write_bytes.sum"),
    MetricRequest(
        "dram__bytes_read.sum.pct_of_peak_sustained_elapsed",
        "dram_read_bandwidth",
        Importance.OPTIONAL,
        0,
    ),
]


def get_identifier():
    return "MemoryL2Compression"

def get_name():
    return "Memory L2 Compression"

def get_description():
    return "Detection of inefficient use of L2 Compute Data Compression"

def get_section_identifier():
    return "MemoryWorkloadAnalysis_Chart"

def get_parent_rules_identifiers():
    return ["Memory"]

def get_estimated_speedup(metrics):
    compression_input_sectors = metrics["compression_input_sectors"].value()
    input_sectors = metrics["input_sectors"].value()
    compression_success_rate = metrics["compression_success_rate"].value()
    dram_bandwidth_percent = metrics["dram_read_bandwidth"].value()

    if input_sectors == 0:
        return NvRules.IFrontend.SpeedupType_LOCAL, 0

    improvement_percent = (
        (1 - compression_success_rate)
        * (1 - compression_input_sectors / input_sectors)
        * dram_bandwidth_percent
    )
    return NvRules.IFrontend.SpeedupType_GLOBAL, improvement_percent


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)

    low_compression_threshold = 20

    cc = metrics["cc_major"].value() * 10 + metrics["cc_minor"].value()
    if 90 <= cc:
        return

    if 80 <= cc:
        compression_input_sectors = metrics["compression_input_sectors"].value()

        if compression_input_sectors > 0:
            compression_input_bytes = 32.0 * compression_input_sectors
            compression_success_rate = metrics["compression_success_rate"].value()
            input_sectors = metrics["input_sectors"].value()

            # Check for low success rate
            if compression_success_rate < low_compression_threshold:
                message = "Out of the {:.1f} bytes sent to the L2 Compression unit only {:.2f}% were successfully compressed. To increase this success rate, consider marking only those memory regions as compressible that contain the most zero values and/or expose the most homogeneous values.".format(compression_input_bytes, compression_success_rate)
                msg_id = fe.message(NvRules.IFrontend.MsgType_MSG_OPTIMIZATION, message, "Low Compression Rate")

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

                fe.focus_metric(msg_id, metrics["compression_success_rate"].name(), compression_success_rate, NvRules.IFrontend.Severity_SEVERITY_HIGH, "Increase the compression success rate")
                if input_sectors > 0:
                    fe.focus_metric(msg_id, metrics["compression_input_sectors"].name(), compression_input_sectors, NvRules.IFrontend.Severity_SEVERITY_HIGH,
                        "Increase the number of compressed sectors towards all L2 input sectors ({:,.0f})".format(input_sectors))
                fe.focus_metric(msg_id, metrics["dram_read_bandwidth"].name(), metrics["dram_read_bandwidth"].value(), NvRules.IFrontend.Severity_SEVERITY_LOW,
                    "The higher the DRAM peak read utilization the more can be gained by compression")

            l1tex_write_bytes = metrics["l1tex__m_l1tex2xbar_write_bytes.sum"].value()
            # Check for write access pattern
            if (l1tex_write_bytes > 0) and (4.0 * l1tex_write_bytes < compression_input_bytes):
                message = "The access patterns for writes to compressible memory are not well suited for the L2 Compression unit. As a consequence, {:.1f}x the data written to the L2 cache has to be communicated to the L2 Compression unit. Try maximizing local coherence for the write operations to compressible memory. For example, avoid writes with large strides as they lead to partial accesses to many L2 cache lines. Instead, try accessing fewer overall cache lines by modifying many values per cache line with each warp's execution of a write operation.".format(compression_input_bytes/l1tex_write_bytes)
                msg_id = fe.message(NvRules.IFrontend.MsgType_MSG_OPTIMIZATION, message, "Access Pattern")

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

                fe.focus_metric(msg_id, metrics["compression_success_rate"].name(), compression_success_rate, NvRules.IFrontend.Severity_SEVERITY_HIGH, "Increase the compression success rate")
                if input_sectors > 0:
                    fe.focus_metric(msg_id, metrics["compression_input_sectors"].name(), compression_input_sectors, NvRules.IFrontend.Severity_SEVERITY_HIGH,
                        "Increase the number of compressed sectors towards all L2 input sectors ({:,.0f})".format(input_sectors))
                fe.focus_metric(msg_id, metrics["dram_read_bandwidth"].name(), metrics["dram_read_bandwidth"].value(), NvRules.IFrontend.Severity_SEVERITY_LOW,
                    "The higher the DRAM peak read utilization the more can be gained by compression")
