jkt_server_ratios.py

# -*- coding: latin-1 -*-
#
# auto generated TopDown/TMA 4.8-full-perf description for Intel Xeon E5 (code named SandyBridge EP)
# Please see http://ark.intel.com for more details on these CPUs.
#
# References:
# http://bit.ly/tma-ispass14
# http://halobates.de/blog/p/262
# https://sites.google.com/site/analysismethods/yasin-pubs
# https://download.01.org/perfmon/
# https://github.com/andikleen/pmu-tools/wiki/toplev-manual
#

# Helpers

print_error = lambda msg: False
smt_enabled = False
ebs_mode = False
version = "4.8-full-perf"
base_frequency = -1.0
Memory = 0
Average_Frequency = 0.0
num_cores = 1
num_threads = 1
num_sockets = 1


def handle_error(obj, msg):
    print_error(msg)
    obj.errcount += 1
    obj.val = 0
    obj.thresh = False

def handle_error_metric(obj, msg):
    print_error(msg)
    obj.errcount += 1
    obj.val = 0



# Constants

Exe_Ports = 6
Mem_L3_Weight = 7
Mem_STLB_Hit_Cost = 7
BAClear_Cost = 12
MS_Switches_Cost = 3
Pipeline_Width = 4
OneMillion = 1000000
OneBillion = 1000000000
EBS_Mode = 0
DS = 1

# Aux. formulas


def Backend_Bound_Cycles(self, EV, level):
    return (STALLS_TOTAL(self, EV, level) + EV("UOPS_DISPATCHED.THREAD:c1", level) - Few_Uops_Executed_Threshold(self, EV, level) - Frontend_RS_Empty_Cycles(self, EV, level) + EV("RESOURCE_STALLS.SB", level))

def DurationTimeInSeconds(self, EV, level):
    return EV("interval-ms", 0) / 1000

def Execute_Cycles(self, EV, level):
    return (EV("UOPS_DISPATCHED.CORE:c1", level) / 2) if smt_enabled else EV("UOPS_DISPATCHED.CORE:c1", level)

def Fetched_Uops(self, EV, level):
    return (EV("IDQ.DSB_UOPS", level) + EV("LSD.UOPS", level) + EV("IDQ.MITE_UOPS", level) + EV("IDQ.MS_UOPS", level))

def Few_Uops_Executed_Threshold(self, EV, level):
    EV("UOPS_DISPATCHED.THREAD:c3", level)
    EV("UOPS_DISPATCHED.THREAD:c2", level)
    return EV("UOPS_DISPATCHED.THREAD:c3", level) if (IPC(self, EV, level)> 1.8) else EV("UOPS_DISPATCHED.THREAD:c2", level)

# Floating Point computational (arithmetic) Operations Count
def FLOP_Count(self, EV, level):
    return (1 *(EV("FP_COMP_OPS_EXE.SSE_SCALAR_SINGLE", level) + EV("FP_COMP_OPS_EXE.SSE_SCALAR_DOUBLE", level)) + 2 * EV("FP_COMP_OPS_EXE.SSE_PACKED_DOUBLE", level) + 4 *(EV("FP_COMP_OPS_EXE.SSE_PACKED_SINGLE", level) + EV("SIMD_FP_256.PACKED_DOUBLE", level)) + 8 * EV("SIMD_FP_256.PACKED_SINGLE", level))

# Floating Point computational (arithmetic) Operations Count
def FP_Arith_Scalar(self, EV, level):
    return EV("FP_COMP_OPS_EXE.SSE_SCALAR_SINGLE", level) + EV("FP_COMP_OPS_EXE.SSE_SCALAR_DOUBLE", level)

# Floating Point computational (arithmetic) Operations Count
def FP_Arith_Vector(self, EV, level):
    return EV("FP_COMP_OPS_EXE.SSE_PACKED_DOUBLE", level) + EV("FP_COMP_OPS_EXE.SSE_PACKED_SINGLE", level) + EV("SIMD_FP_256.PACKED_SINGLE", level) + EV("SIMD_FP_256.PACKED_DOUBLE", level)

def Frontend_RS_Empty_Cycles(self, EV, level):
    EV("RS_EVENTS.EMPTY_CYCLES", level)
    return EV("RS_EVENTS.EMPTY_CYCLES", level) if (self.Fetch_Latency.compute(EV)> 0.1) else 0

def Frontend_Latency_Cycles(self, EV, level):
    return EV(lambda EV , level : min(EV("CPU_CLK_UNHALTED.THREAD", level) , EV("IDQ_UOPS_NOT_DELIVERED.CYCLES_0_UOPS_DELIV.CORE", level)) , level )

def HighIPC(self, EV, level):
    val = IPC(self, EV, level) / Pipeline_Width
    return val

def ITLB_Miss_Cycles(self, EV, level):
    return (12 * EV("ITLB_MISSES.STLB_HIT", level) + EV("ITLB_MISSES.WALK_DURATION", level))

def Mem_L3_Hit_Fraction(self, EV, level):
    return EV("MEM_LOAD_UOPS_RETIRED.LLC_HIT", level) / (EV("MEM_LOAD_UOPS_RETIRED.LLC_HIT", level) + Mem_L3_Weight * EV("MEM_LOAD_UOPS_RETIRED.LLC_MISS", level))

def Memory_Bound_Fraction(self, EV, level):
    return (STALLS_MEM_ANY(self, EV, level) + EV("RESOURCE_STALLS.SB", level)) / Backend_Bound_Cycles(self, EV, level)

def Mispred_Clears_Fraction(self, EV, level):
    return EV("BR_MISP_RETIRED.ALL_BRANCHES", level) / (EV("BR_MISP_RETIRED.ALL_BRANCHES", level) + EV("MACHINE_CLEARS.COUNT", level))

def ORO_DRD_Any_Cycles(self, EV, level):
    return EV(lambda EV , level : min(EV("CPU_CLK_UNHALTED.THREAD", level) , EV("OFFCORE_REQUESTS_OUTSTANDING.CYCLES_WITH_DATA_RD", level)) , level )

def ORO_DRD_BW_Cycles(self, EV, level):
    return EV(lambda EV , level : min(EV("CPU_CLK_UNHALTED.THREAD", level) , EV("OFFCORE_REQUESTS_OUTSTANDING.ALL_DATA_RD:c6", level)) , level )

def STALLS_MEM_ANY(self, EV, level):
    return EV(lambda EV , level : min(EV("CPU_CLK_UNHALTED.THREAD", level) , EV("CYCLE_ACTIVITY.STALLS_L1D_PENDING", level)) , level )

def STALLS_TOTAL(self, EV, level):
    return EV(lambda EV , level : min(EV("CPU_CLK_UNHALTED.THREAD", level) , EV("CYCLE_ACTIVITY.CYCLES_NO_DISPATCH", level)) , level )

def Recovery_Cycles(self, EV, level):
    return (EV("INT_MISC.RECOVERY_CYCLES_ANY", level) / 2) if smt_enabled else EV("INT_MISC.RECOVERY_CYCLES", level)

def Retire_Fraction(self, EV, level):
    return Retired_Slots(self, EV, level) / EV("UOPS_ISSUED.ANY", level)

# Retired slots per Logical Processor
def Retired_Slots(self, EV, level):
    return EV("UOPS_RETIRED.RETIRE_SLOTS", level)

# Number of logical processors (enabled or online) on the target system
def Num_CPUs(self, EV, level):
    return 8 if smt_enabled else 4

# Instructions Per Cycle (per Logical Processor)
def IPC(self, EV, level):
    return EV("INST_RETIRED.ANY", level) / CLKS(self, EV, level)

# Uops Per Instruction
def UopPI(self, EV, level):
    val = Retired_Slots(self, EV, level) / EV("INST_RETIRED.ANY", level)
    self.thresh = (val > 1.05)
    return val

# Cycles Per Instruction (per Logical Processor)
def CPI(self, EV, level):
    return 1 / IPC(self, EV, level)

# Per-Logical Processor actual clocks when the Logical Processor is active.
def CLKS(self, EV, level):
    return EV("CPU_CLK_UNHALTED.THREAD", level)

# Total issue-pipeline slots (per-Physical Core till ICL; per-Logical Processor ICL onward)
def SLOTS(self, EV, level):
    return Pipeline_Width * CORE_CLKS(self, EV, level)

# The ratio of Executed- by Issued-Uops. Ratio > 1 suggests high rate of uop micro-fusions. Ratio < 1 suggest high rate of "execute" at rename stage.
def Execute_per_Issue(self, EV, level):
    return EV("UOPS_DISPATCHED.THREAD", level) / EV("UOPS_ISSUED.ANY", level)

# Instructions Per Cycle across hyper-threads (per physical core)
def CoreIPC(self, EV, level):
    return EV("INST_RETIRED.ANY", level) / CORE_CLKS(self, EV, level)

# Floating Point Operations Per Cycle
def FLOPc(self, EV, level):
    return FLOP_Count(self, EV, level) / CORE_CLKS(self, EV, level)

# Instruction-Level-Parallelism (average number of uops executed when there is execution) per thread (logical-processor)
def ILP(self, EV, level):
    return EV("UOPS_DISPATCHED.THREAD", level) / Execute_Cycles(self, EV, level)

# Core actual clocks when any Logical Processor is active on the Physical Core
def CORE_CLKS(self, EV, level):
    return ((EV("CPU_CLK_UNHALTED.THREAD", level) / 2) * (1 + EV("CPU_CLK_UNHALTED.ONE_THREAD_ACTIVE", level) / EV("CPU_CLK_UNHALTED.REF_XCLK", level))) if ebs_mode else(EV("CPU_CLK_UNHALTED.THREAD_ANY", level) / 2) if smt_enabled else CLKS(self, EV, level)

# Total number of retired Instructions
def Instructions(self, EV, level):
    return EV("INST_RETIRED.ANY", level)

# Average number of Uops retired in cycles where at least one uop has retired.
def Retire(self, EV, level):
    return Retired_Slots(self, EV, level) / EV("UOPS_RETIRED.RETIRE_SLOTS:c1", level)

# Fraction of Uops delivered by the DSB (aka Decoded ICache; or Uop Cache). See section 'Decoded ICache' in Optimization Manual. http://www.intel.com/content/www/us/en/architecture-and-technology/64-ia-32-architectures-optimization-manual.html
def DSB_Coverage(self, EV, level):
    val = EV("IDQ.DSB_UOPS", level) / Fetched_Uops(self, EV, level)
    self.thresh = (val < 0.7) and HighIPC(self, EV, 1)
    return val

# Average CPU Utilization (percentage)
def CPU_Utilization(self, EV, level):
    return CPUs_Utilized(self, EV, level) / Num_CPUs(self, EV, level)

# Average number of utilized CPUs
def CPUs_Utilized(self, EV, level):
    return EV("CPU_CLK_UNHALTED.REF_TSC", level) / EV("msr/tsc/", 0)

# Measured Average Core Frequency for unhalted processors [GHz]
def Core_Frequency(self, EV, level):
    return Turbo_Utilization(self, EV, level) * EV("msr/tsc/", 0) / OneBillion / Time(self, EV, level)

# Measured Average Uncore Frequency for the SoC [GHz]
def Uncore_Frequency(self, EV, level):
    return Socket_CLKS(self, EV, level) / 1e9 / Time(self, EV, level)

# Giga Floating Point Operations Per Second. Aggregate across all supported options of: FP precisions, scalar and vector instructions, vector-width
def GFLOPs(self, EV, level):
    return (FLOP_Count(self, EV, level) / OneBillion) / Time(self, EV, level)

# Average Frequency Utilization relative nominal frequency
def Turbo_Utilization(self, EV, level):
    return CLKS(self, EV, level) / EV("CPU_CLK_UNHALTED.REF_TSC", level)

# Fraction of cycles where both hardware Logical Processors were active
def SMT_2T_Utilization(self, EV, level):
    return 1 - EV("CPU_CLK_UNHALTED.ONE_THREAD_ACTIVE", level) / (EV("CPU_CLK_UNHALTED.REF_XCLK_ANY", level) / 2) if smt_enabled else 0

# Fraction of cycles spent in the Operating System (OS) Kernel mode
def Kernel_Utilization(self, EV, level):
    val = EV("CPU_CLK_UNHALTED.THREAD_P:SUP", level) / EV("CPU_CLK_UNHALTED.THREAD", level)
    self.thresh = (val > 0.05)
    return val

# Cycles Per Instruction for the Operating System (OS) Kernel mode
def Kernel_CPI(self, EV, level):
    return EV("CPU_CLK_UNHALTED.THREAD_P:SUP", level) / EV("INST_RETIRED.ANY_P:SUP", level)

# Average external Memory Bandwidth Use for reads and writes [GB / sec]
def DRAM_BW_Use(self, EV, level):
    return (64 *(EV("UNC_M_CAS_COUNT.RD", level) + EV("UNC_M_CAS_COUNT.WR", level)) / OneBillion) / Time(self, EV, level)

# Average latency of data read request to external memory (in nanoseconds). Accounts for demand loads and L1/L2 prefetches. 
def MEM_Read_Latency(self, EV, level):
    return OneBillion *(EV("UNC_C_TOR_OCCUPANCY.MISS_OPCODE:Match=0x182", level) / EV("UNC_C_TOR_INSERTS.MISS_OPCODE:Match=0x182", level)) / (Socket_CLKS(self, EV, level) / Time(self, EV, level))

# Average number of parallel data read requests to external memory. Accounts for demand loads and L1/L2 prefetches
def MEM_Parallel_Reads(self, EV, level):
    return EV("UNC_C_TOR_OCCUPANCY.MISS_OPCODE:Match=0x182", level) / EV("UNC_C_TOR_OCCUPANCY.MISS_OPCODE:Match=0x182:c1", level)

# Run duration time in seconds
def Time(self, EV, level):
    val = EV("interval-s", 0)
    self.thresh = (val < 1)
    return val

# Socket actual clocks when any core is active on that socket
def Socket_CLKS(self, EV, level):
    return EV("UNC_C_CLOCKTICKS:one_unit", level)

# Instructions per Far Branch ( Far Branches apply upon transition from application to operating system, handling interrupts, exceptions) [lower number means higher occurrence rate]
def IpFarBranch(self, EV, level):
    val = EV("INST_RETIRED.ANY", level) / EV("BR_INST_RETIRED.FAR_BRANCH:USER", level)
    self.thresh = (val < 1000000)
    return val

# Event groups


class Frontend_Bound:
    name = "Frontend_Bound"
    domain = "Slots"
    area = "FE"
    level = 1
    htoff = False
    sample = []
    errcount = 0
    sibling = None
    metricgroup = frozenset(['BvFB', 'BvIO', 'TmaL1', 'PGO'])
    maxval = None
    def compute(self, EV):
        try:
            self.val = EV("IDQ_UOPS_NOT_DELIVERED.CORE", 1) / SLOTS(self, EV, 1)
            self.thresh = (self.val > 0.15)
        except ZeroDivisionError:
            handle_error(self, "Frontend_Bound zero division")
        return self.val
    desc = """
This category represents fraction of slots where the
processor's Frontend undersupplies its Backend. Frontend
denotes the first part of the processor core responsible to
fetch operations that are executed later on by the Backend
part. Within the Frontend; a branch predictor predicts the
next address to fetch; cache-lines are fetched from the
memory subsystem; parsed into instructions; and lastly
decoded into micro-operations (uops). Ideally the Frontend
can issue Pipeline_Width uops every cycle to the Backend.
Frontend Bound denotes unutilized issue-slots when there is
no Backend stall; i.e. bubbles where Frontend delivered no
uops while Backend could have accepted them. For example;
stalls due to instruction-cache misses would be categorized
under Frontend Bound."""


class Fetch_Latency:
    name = "Fetch_Latency"
    domain = "Slots"
    area = "FE"
    level = 2
    htoff = False
    sample = ['RS_EVENTS.EMPTY_END']
    errcount = 0
    sibling = None
    metricgroup = frozenset(['Frontend', 'TmaL2'])
    maxval = None
    def compute(self, EV):
        try:
            self.val = Pipeline_Width * Frontend_Latency_Cycles(self, EV, 2) / SLOTS(self, EV, 2)
            self.thresh = (self.val > 0.1) and self.parent.thresh
        except ZeroDivisionError:
            handle_error(self, "Fetch_Latency zero division")
        return self.val
    desc = """
This metric represents fraction of slots the CPU was stalled
due to Frontend latency issues.  For example; instruction-
cache misses; iTLB misses or fetch stalls after a branch
misprediction are categorized under Frontend Latency. In
such cases; the Frontend eventually delivers no uops for
some period."""


class ITLB_Misses:
    name = "ITLB_Misses"
    domain = "Clocks"
    area = "FE"
    level = 3
    htoff = False
    sample = ['ITLB_MISSES.WALK_COMPLETED']
    errcount = 0
    sibling = None
    metricgroup = frozenset(['BigFootprint', 'BvBC', 'FetchLat', 'MemoryTLB'])
    maxval = None
    def compute(self, EV):
        try:
            self.val = ITLB_Miss_Cycles(self, EV, 3) / CLKS(self, EV, 3)
            self.thresh = (self.val > 0.05) and self.parent.thresh
        except ZeroDivisionError:
            handle_error(self, "ITLB_Misses zero division")
        return self.val
    desc = """
This metric represents fraction of cycles the CPU was
stalled due to Instruction TLB (ITLB) misses.. Consider
large 2M pages for code (selectively prefer hot large-size
function, due to limited 2M entries). Linux options:
standard binaries use libhugetlbfs; Hfsort.. https://github.
com/libhugetlbfs/libhugetlbfs;https://research.fb.com/public
ations/optimizing-function-placement-for-large-scale-data-
center-applications-2/"""


class Branch_Resteers:
    name = "Branch_Resteers"
    domain = "Clocks"
    area = "FE"
    level = 3
    htoff = False
    sample = ['BR_MISP_RETIRED.ALL_BRANCHES']
    errcount = 0
    sibling = None
    metricgroup = frozenset(['FetchLat'])
    maxval = None
    def compute(self, EV):
        try:
            self.val = BAClear_Cost *(EV("BR_MISP_RETIRED.ALL_BRANCHES", 3) + EV("MACHINE_CLEARS.COUNT", 3) + EV("BACLEARS.ANY", 3)) / CLKS(self, EV, 3)
            self.thresh = (self.val > 0.05) and self.parent.thresh
        except ZeroDivisionError:
            handle_error(self, "Branch_Resteers zero division")
        return self.val
    desc = """
This metric represents fraction of cycles the CPU was
stalled due to Branch Resteers. Branch Resteers estimates
the Frontend delay in fetching operations from corrected
path; following all sorts of miss-predicted branches. For
example; branchy code with lots of miss-predictions might
get categorized under Branch Resteers. Note the value of
this node may overlap with its siblings."""


class MS_Switches:
    name = "MS_Switches"
    domain = "Clocks_Estimated"
    area = "FE"
    level = 3
    htoff = False
    sample = ['IDQ.MS_SWITCHES']
    errcount = 0
    sibling = None
    metricgroup = frozenset(['FetchLat', 'MicroSeq'])
    maxval = 1.0
    def compute(self, EV):
        try:
            self.val = MS_Switches_Cost * EV("IDQ.MS_SWITCHES", 3) / CLKS(self, EV, 3)
            self.thresh = (self.val > 0.05) and self.parent.thresh
        except ZeroDivisionError:
            handle_error(self, "MS_Switches zero division")
        return self.val
    desc = """
This metric estimates the fraction of cycles when the CPU
was stalled due to switches of uop delivery to the Microcode
Sequencer (MS). Commonly used instructions are optimized for
delivery by the DSB (decoded i-cache) or MITE (legacy
instruction decode) pipelines. Certain operations cannot be
handled natively by the execution pipeline; and must be
performed by microcode (small programs injected into the
execution stream). Switching to the MS too often can
negatively impact performance. The MS is designated to
deliver long uop flows required by CISC instructions like
CPUID; or uncommon conditions like Floating Point Assists
when dealing with Denormals."""


class LCP:
    name = "LCP"
    domain = "Clocks"
    area = "FE"
    level = 3
    htoff = False
    sample = []
    errcount = 0
    sibling = None
    metricgroup = frozenset(['FetchLat'])
    maxval = None
    def compute(self, EV):
        try:
            self.val = EV("ILD_STALL.LCP", 3) / CLKS(self, EV, 3)
            self.thresh = (self.val > 0.05) and self.parent.thresh
        except ZeroDivisionError:
            handle_error(self, "LCP zero division")
        return self.val
    desc = """
This metric represents fraction of cycles CPU was stalled
due to Length Changing Prefixes (LCPs). Using proper
compiler flags or Intel Compiler by default will certainly
avoid this."""


class DSB_Switches:
    name = "DSB_Switches"
    domain = "Clocks"
    area = "FE"
    level = 3
    htoff = False
    sample = []
    errcount = 0
    sibling = None
    metricgroup = frozenset(['DSBmiss', 'FetchLat'])
    maxval = None
    def compute(self, EV):
        try:
            self.val = EV("DSB2MITE_SWITCHES.PENALTY_CYCLES", 3) / CLKS(self, EV, 3)
            self.thresh = (self.val > 0.05) and self.parent.thresh
        except ZeroDivisionError:
            handle_error(self, "DSB_Switches zero division")
        return self.val
    desc = """
This metric represents fraction of cycles the CPU was
stalled due to switches from DSB to MITE pipelines. The DSB
(decoded i-cache) is a Uop Cache where the front-end
directly delivers Uops (micro operations) avoiding heavy x86
decoding. The DSB pipeline has shorter latency and delivered
higher bandwidth than the MITE (legacy instruction decode
pipeline). Switching between the two pipelines can cause
penalties hence this metric measures the exposed penalty..
See section 'Optimization for Decoded Icache' in
Optimization Manual:. http://www.intel.com/content/www/us/en
/architecture-and-technology/64-ia-32-architectures-
optimization-manual.html"""


class Fetch_Bandwidth:
    name = "Fetch_Bandwidth"
    domain = "Slots"
    area = "FE"
    level = 2
    htoff = False
    sample = []
    errcount = 0
    sibling = None
    metricgroup = frozenset(['FetchBW', 'Frontend', 'TmaL2'])
    maxval = None
    def compute(self, EV):
        try:
            self.val = self.Frontend_Bound.compute(EV) - self.Fetch_Latency.compute(EV)
            self.thresh = (self.val > 0.2)
        except ZeroDivisionError:
            handle_error(self, "Fetch_Bandwidth zero division")
        return self.val
    desc = """
This metric represents fraction of slots the CPU was stalled
due to Frontend bandwidth issues.  For example;
inefficiencies at the instruction decoders; or restrictions
for caching in the DSB (decoded uops cache) are categorized
under Fetch Bandwidth. In such cases; the Frontend typically
delivers suboptimal amount of uops to the Backend."""


class Bad_Speculation:
    name = "Bad_Speculation"
    domain = "Slots"
    area = "BAD"
    level = 1
    htoff = False
    sample = []
    errcount = 0
    sibling = None
    metricgroup = frozenset(['TmaL1'])
    maxval = None
    def compute(self, EV):
        try:
            self.val = (EV("UOPS_ISSUED.ANY", 1) - Retired_Slots(self, EV, 1) + Pipeline_Width * Recovery_Cycles(self, EV, 1)) / SLOTS(self, EV, 1)
            self.thresh = (self.val > 0.15)
        except ZeroDivisionError:
            handle_error(self, "Bad_Speculation zero division")
        return self.val
    desc = """
This category represents fraction of slots wasted due to
incorrect speculations. This include slots used to issue
uops that do not eventually get retired and slots for which
the issue-pipeline was blocked due to recovery from earlier
incorrect speculation. For example; wasted work due to miss-
predicted branches are categorized under Bad Speculation
category. Incorrect data speculation followed by Memory
Ordering Nukes is another example."""


class Branch_Mispredicts:
    name = "Branch_Mispredicts"
    domain = "Slots"
    area = "BAD"
    level = 2
    htoff = False
    sample = ['BR_MISP_RETIRED.ALL_BRANCHES:pp']
    errcount = 0
    sibling = None
    metricgroup = frozenset(['BadSpec', 'BrMispredicts', 'BvMP', 'TmaL2'])
    maxval = None
    def compute(self, EV):
        try:
            self.val = Mispred_Clears_Fraction(self, EV, 2) * self.Bad_Speculation.compute(EV)
            self.thresh = (self.val > 0.1) and self.parent.thresh
        except ZeroDivisionError:
            handle_error(self, "Branch_Mispredicts zero division")
        return self.val
    desc = """
This metric represents fraction of slots the CPU has wasted
due to Branch Misprediction.  These slots are either wasted
by uops fetched from an incorrectly speculated program path;
or stalls when the out-of-order part of the machine needs to
recover its state from a speculative path.. Using profile
feedback in the compiler may help. Please see the
Optimization Manual for general strategies for addressing
branch misprediction issues..
http://www.intel.com/content/www/us/en/architecture-and-
technology/64-ia-32-architectures-optimization-manual.html"""


class Machine_Clears:
    name = "Machine_Clears"
    domain = "Slots"
    area = "BAD"
    level = 2
    htoff = False
    sample = ['MACHINE_CLEARS.COUNT']
    errcount = 0
    sibling = None
    metricgroup = frozenset(['BadSpec', 'BvMS', 'MachineClears', 'TmaL2'])
    maxval = None
    def compute(self, EV):
        try:
            self.val = self.Bad_Speculation.compute(EV) - self.Branch_Mispredicts.compute(EV)
            self.thresh = (self.val > 0.1) and self.parent.thresh
        except ZeroDivisionError:
            handle_error(self, "Machine_Clears zero division")
        return self.val
    desc = """
This metric represents fraction of slots the CPU has wasted
due to Machine Clears.  These slots are either wasted by
uops fetched prior to the clear; or stalls the out-of-order
portion of the machine needs to recover its state after the
clear. For example; this can happen due to memory ordering
Nukes (e.g. Memory Disambiguation) or Self-Modifying-Code
(SMC) nukes.. See \"Memory Disambiguation\" in Optimization
Manual and:. https://software.intel.com/sites/default/files/
m/d/4/1/d/8/sma.pdf"""


class Backend_Bound:
    name = "Backend_Bound"
    domain = "Slots"
    area = "BE"
    level = 1
    htoff = False
    sample = []
    errcount = 0
    sibling = None
    metricgroup = frozenset(['BvOB', 'TmaL1'])
    maxval = None
    def compute(self, EV):
        try:
            self.val = 1 -(self.Frontend_Bound.compute(EV) + self.Bad_Speculation.compute(EV) + self.Retiring.compute(EV))
            self.thresh = (self.val > 0.2)
        except ZeroDivisionError:
            handle_error(self, "Backend_Bound zero division")
        return self.val
    desc = """
This category represents fraction of slots where no uops are
being delivered due to a lack of required resources for
accepting new uops in the Backend. Backend is the portion of
the processor core where the out-of-order scheduler
dispatches ready uops into their respective execution units;
and once completed these uops get retired according to
program order. For example; stalls due to data-cache misses
or stalls due to the divider unit being overloaded are both
categorized under Backend Bound. Backend Bound is further
divided into two main categories: Memory Bound and Core
Bound."""


class Memory_Bound:
    name = "Memory_Bound"
    domain = "Slots"
    area = "BE/Mem"
    level = 2
    htoff = False
    sample = []
    errcount = 0
    sibling = None
    metricgroup = frozenset(['Backend', 'TmaL2'])
    maxval = None
    def compute(self, EV):
        try:
            self.val = Memory_Bound_Fraction(self, EV, 2) * self.Backend_Bound.compute(EV)
            self.thresh = (self.val > 0.2) and self.parent.thresh
        except ZeroDivisionError:
            handle_error(self, "Memory_Bound zero division")
        return self.val
    desc = """
This metric represents fraction of slots the Memory
subsystem within the Backend was a bottleneck.  Memory Bound
estimates fraction of slots where pipeline is likely stalled
due to demand load or store instructions. This accounts
mainly for (1) non-completed in-flight memory demand loads
which coincides with execution units starvation; in addition
to (2) cases where stores could impose backpressure on the
pipeline when many of them get buffered at the same time
(less common out of the two)."""


class DTLB_Load:
    name = "DTLB_Load"
    domain = "Clocks_Estimated"
    area = "BE/Mem"
    level = 4
    htoff = False
    sample = ['MEM_UOPS_RETIRED.STLB_MISS_LOADS:pp']
    errcount = 0
    sibling = None
    metricgroup = frozenset(['BvMT', 'MemoryTLB'])
    maxval = 1.0
    def compute(self, EV):
        try:
            self.val = (Mem_STLB_Hit_Cost * EV("DTLB_LOAD_MISSES.STLB_HIT", 4) + EV("DTLB_LOAD_MISSES.WALK_DURATION", 4)) / CLKS(self, EV, 4)
            self.thresh = (self.val > 0.1) and self.parent.thresh
        except ZeroDivisionError:
            handle_error(self, "DTLB_Load zero division")
        return self.val
    desc = """
This metric roughly estimates the fraction of cycles where
the Data TLB (DTLB) was missed by load accesses. TLBs
(Translation Look-aside Buffers) are processor caches for
recently used entries out of the Page Tables that are used
to map virtual- to physical-addresses by the operating
system. This metric approximates the potential delay of
demand loads missing the first-level data TLB (assuming
worst case scenario with back to back misses to different
pages). This includes hitting in the second-level TLB (STLB)
as well as performing a hardware page walk on an STLB miss.."""


class L3_Bound:
    name = "L3_Bound"
    domain = "Stalls"
    area = "BE/Mem"
    level = 3
    htoff = False
    sample = ['MEM_LOAD_UOPS_RETIRED.LLC_HIT:pp']
    errcount = 0
    sibling = None
    metricgroup = frozenset(['CacheHits', 'MemoryBound', 'TmaL3mem'])
    maxval = None
    def compute(self, EV):
        try:
            self.val = Mem_L3_Hit_Fraction(self, EV, 3) * EV("CYCLE_ACTIVITY.STALLS_L2_PENDING", 3) / CLKS(self, EV, 3)
            self.thresh = (self.val > 0.05) and self.parent.thresh
        except ZeroDivisionError:
            handle_error(self, "L3_Bound zero division")
        return self.val
    desc = """
This metric estimates how often the CPU was stalled due to
loads accesses to L3 cache or contended with a sibling Core.
Avoiding cache misses (i.e. L2 misses/L3 hits) can improve
the latency and increase performance."""


class DRAM_Bound:
    name = "DRAM_Bound"
    domain = "Stalls"
    area = "BE/Mem"
    level = 3
    htoff = False
    sample = ['MEM_LOAD_UOPS_RETIRED.LLC_MISS:pp']
    errcount = 0
    sibling = None
    metricgroup = frozenset(['MemoryBound', 'TmaL3mem'])
    maxval = 1.0
    def compute(self, EV):
        try:
            self.val = (1 - Mem_L3_Hit_Fraction(self, EV, 3)) * EV("CYCLE_ACTIVITY.STALLS_L2_PENDING", 3) / CLKS(self, EV, 3)
            self.thresh = (self.val > 0.1) and self.parent.thresh
        except ZeroDivisionError:
            handle_error(self, "DRAM_Bound zero division")
        return self.val
    desc = """
This metric estimates how often the CPU was stalled on
accesses to external memory (DRAM) by loads. Better caching
can improve the latency and increase performance."""


class MEM_Bandwidth:
    name = "MEM_Bandwidth"
    domain = "Clocks"
    area = "BE/Mem"
    level = 4
    htoff = False
    sample = []
    errcount = 0
    sibling = None
    metricgroup = frozenset(['BvMB', 'MemoryBW', 'Offcore'])
    maxval = None
    def compute(self, EV):
        try:
            self.val = ORO_DRD_BW_Cycles(self, EV, 4) / CLKS(self, EV, 4)
            self.thresh = (self.val > 0.2) and self.parent.thresh
        except ZeroDivisionError:
            handle_error(self, "MEM_Bandwidth zero division")
        return self.val
    desc = """
This metric estimates fraction of cycles where the core's
performance was likely hurt due to approaching bandwidth
limits of external memory - DRAM ([SPR-HBM] and/or HBM).
The underlying heuristic assumes that a similar off-core
traffic is generated by all IA cores. This metric does not
aggregate non-data-read requests by this logical processor;
requests from other IA Logical Processors/Physical
Cores/sockets; or other non-IA devices like GPU; hence the
maximum external memory bandwidth limits may or may not be
approached when this metric is flagged (see Uncore counters
for that).. Improve data accesses to reduce cacheline
transfers from/to memory. Examples: 1) Consume all bytes of
a each cacheline before it is evicted (e.g. reorder
structure elements and split non-hot ones), 2) merge
computed-limited with BW-limited loops, 3) NUMA
optimizations in multi-socket system. Note: software
prefetches will not help BW-limited application.."""


class MEM_Latency:
    name = "MEM_Latency"
    domain = "Clocks"
    area = "BE/Mem"
    level = 4
    htoff = False
    sample = []
    errcount = 0
    sibling = None
    metricgroup = frozenset(['BvML', 'MemoryLat', 'Offcore'])
    maxval = None
    def compute(self, EV):
        try:
            self.val = ORO_DRD_Any_Cycles(self, EV, 4) / CLKS(self, EV, 4) - self.MEM_Bandwidth.compute(EV)
            self.thresh = (self.val > 0.1) and self.parent.thresh
        except ZeroDivisionError:
            handle_error(self, "MEM_Latency zero division")
        return self.val
    desc = """
This metric estimates fraction of cycles where the
performance was likely hurt due to latency from external
memory - DRAM ([SPR-HBM] and/or HBM).  This metric does not
aggregate requests from other Logical Processors/Physical
Cores/sockets (see Uncore counters for that).. Improve data
accesses or interleave them with compute. Examples: 1) Data
layout re-structuring, 2) Software Prefetches (also through
the compiler).."""


class Store_Bound:
    name = "Store_Bound"
    domain = "Stalls"
    area = "BE/Mem"
    level = 3
    htoff = False
    sample = ['MEM_UOPS_RETIRED.ALL_STORES:pp']
    errcount = 0
    sibling = None
    metricgroup = frozenset(['MemoryBound', 'TmaL3mem'])
    maxval = None
    def compute(self, EV):
        try:
            self.val = EV("RESOURCE_STALLS.SB", 3) / CLKS(self, EV, 3)
            self.thresh = (self.val > 0.2) and self.parent.thresh
        except ZeroDivisionError:
            handle_error(self, "Store_Bound zero division")
        return self.val
    desc = """
This metric estimates how often CPU was stalled  due to RFO
store memory accesses; RFO store issue a read-for-ownership
request before the write. Even though store accesses do not
typically stall out-of-order CPUs; there are few cases where
stores can lead to actual stalls. This metric will be
flagged should RFO stores be a bottleneck."""


class Core_Bound:
    name = "Core_Bound"
    domain = "Slots"
    area = "BE/Core"
    level = 2
    htoff = False
    sample = []
    errcount = 0
    sibling = None
    metricgroup = frozenset(['Backend', 'TmaL2', 'Compute'])
    maxval = None
    def compute(self, EV):
        try:
            self.val = self.Backend_Bound.compute(EV) - self.Memory_Bound.compute(EV)
            self.thresh = (self.val > 0.1) and self.parent.thresh
        except ZeroDivisionError:
            handle_error(self, "Core_Bound zero division")
        return self.val
    desc = """
This metric represents fraction of slots where Core non-
memory issues were of a bottleneck.  Shortage in hardware
compute resources; or dependencies in software's
instructions are both categorized under Core Bound. Hence it
may indicate the machine ran out of an out-of-order
resource; certain execution units are overloaded or
dependencies in program's data- or instruction-flow are
limiting the performance (e.g. FP-chained long-latency
arithmetic operations).. Tip: consider Port Saturation
analysis as next step."""


class Divider:
    name = "Divider"
    domain = "Clocks"
    area = "BE/Core"
    level = 3
    htoff = False
    sample = ['ARITH.FPU_DIV_ACTIVE']
    errcount = 0
    sibling = None
    metricgroup = frozenset(['BvCB'])
    maxval = 1.0
    def compute(self, EV):
        try:
            self.val = EV("ARITH.FPU_DIV_ACTIVE", 3) / CORE_CLKS(self, EV, 3)
            self.thresh = (self.val > 0.2) and self.parent.thresh
        except ZeroDivisionError:
            handle_error(self, "Divider zero division")
        return self.val
    desc = """
This metric represents fraction of cycles where the Divider
unit was active. Divide and square root instructions are
performed by the Divider unit and can take considerably
longer latency than integer or Floating Point addition;
subtraction; or multiplication."""


class Ports_Utilization:
    name = "Ports_Utilization"
    domain = "Clocks"
    area = "BE/Core"
    level = 3
    htoff = False
    sample = []
    errcount = 0
    sibling = None
    metricgroup = frozenset(['PortsUtil'])
    maxval = None
    def compute(self, EV):
        try:
            self.val = (Backend_Bound_Cycles(self, EV, 3) - EV("RESOURCE_STALLS.SB", 3) - STALLS_MEM_ANY(self, EV, 3)) / CLKS(self, EV, 3)
            self.thresh = (self.val > 0.15) and self.parent.thresh
        except ZeroDivisionError:
            handle_error(self, "Ports_Utilization zero division")
        return self.val
    desc = """
This metric estimates fraction of cycles the CPU performance
was potentially limited due to Core computation issues (non
divider-related).  Two distinct categories can be attributed
into this metric: (1) heavy data-dependency among contiguous
instructions would manifest in this metric - such cases are
often referred to as low Instruction Level Parallelism
(ILP). (2) Contention on some hardware execution unit other
than Divider. For example; when there are too many multiply
operations.. Loop Vectorization -most compilers feature
auto-Vectorization options today- reduces pressure on the
execution ports as multiple elements are calculated with
same uop."""


class Retiring:
    name = "Retiring"
    domain = "Slots"
    area = "RET"
    level = 1
    htoff = False
    sample = ['UOPS_RETIRED.RETIRE_SLOTS']
    errcount = 0
    sibling = None
    metricgroup = frozenset(['BvUW', 'TmaL1'])
    maxval = None
    def compute(self, EV):
        try:
            self.val = Retired_Slots(self, EV, 1) / SLOTS(self, EV, 1)
            self.thresh = (self.val > 0.7) or self.Heavy_Operations.thresh
        except ZeroDivisionError:
            handle_error(self, "Retiring zero division")
        return self.val
    desc = """
This category represents fraction of slots utilized by
useful work i.e. issued uops that eventually get retired.
Ideally; all pipeline slots would be attributed to the
Retiring category.  Retiring of 100% would indicate the
maximum Pipeline_Width throughput was achieved.  Maximizing
Retiring typically increases the Instructions-per-cycle (see
IPC metric). Note that a high Retiring value does not
necessary mean there is no room for more performance.  For
example; Heavy-operations or Microcode Assists are
categorized under Retiring. They often indicate suboptimal
performance and can often be optimized or avoided. . A high
Retiring value for non-vectorized code may be a good hint
for programmer to consider vectorizing his code.  Doing so
essentially lets more computations be done without
significantly increasing number of instructions thus
improving the performance."""


class Light_Operations:
    name = "Light_Operations"
    domain = "Slots"
    area = "RET"
    level = 2
    htoff = False
    sample = ['INST_RETIRED.PREC_DIST']
    errcount = 0
    sibling = None
    metricgroup = frozenset(['Retire', 'TmaL2'])
    maxval = None
    def compute(self, EV):
        try:
            self.val = self.Retiring.compute(EV) - self.Heavy_Operations.compute(EV)
            self.thresh = (self.val > 0.6)
        except ZeroDivisionError:
            handle_error(self, "Light_Operations zero division")
        return self.val
    desc = """
This metric represents fraction of slots where the CPU was
retiring light-weight operations -- instructions that
require no more than one uop (micro-operation). This
correlates with total number of instructions used by the
program. A uops-per-instruction (see UopPI metric) ratio of
1 or less should be expected for decently optimized code
running on Intel Core/Xeon products. While this often
indicates efficient X86 instructions were executed; high
value does not necessarily mean better performance cannot be
achieved. . Focus on techniques that reduce instruction
count or result in more efficient instructions generation
such as vectorization."""


class FP_Arith:
    name = "FP_Arith"
    domain = "Uops"
    area = "RET"
    level = 3
    htoff = False
    sample = []
    errcount = 0
    sibling = None
    metricgroup = frozenset(['HPC'])
    maxval = None
    def compute(self, EV):
        try:
            self.val = self.X87_Use.compute(EV) + self.FP_Scalar.compute(EV) + self.FP_Vector.compute(EV)
            self.thresh = (self.val > 0.2) and self.parent.thresh
        except ZeroDivisionError:
            handle_error(self, "FP_Arith zero division")
        return self.val
    desc = """
This metric represents overall arithmetic floating-point
(FP) operations fraction the CPU has executed (retired).
Note this metric's value may exceed its parent due to use of
\"Uops\" CountDomain and FMA double-counting."""


class X87_Use:
    name = "X87_Use"
    domain = "Uops"
    area = "RET"
    level = 4
    htoff = False
    sample = []
    errcount = 0
    sibling = None
    metricgroup = frozenset(['Compute'])
    maxval = None
    def compute(self, EV):
        try:
            self.val = Retired_Slots(self, EV, 4) * EV("FP_COMP_OPS_EXE.X87", 4) / EV("UOPS_DISPATCHED.THREAD", 4)
            self.thresh = (self.val > 0.1) and self.parent.thresh
        except ZeroDivisionError:
            handle_error(self, "X87_Use zero division")
        return self.val
    desc = """
This metric serves as an approximation of legacy x87 usage.
It accounts for instructions beyond X87 FP arithmetic
operations; hence may be used as a thermometer to avoid X87
high usage and preferably upgrade to modern ISA. See Tip
under Tuning Hint.. Tip: consider compiler flags to generate
newer AVX (or SSE) instruction sets; which typically perform
better and feature vectors."""


class FP_Scalar:
    name = "FP_Scalar"
    domain = "Uops"
    area = "RET"
    level = 4
    htoff = False
    sample = []
    errcount = 0
    sibling = None
    metricgroup = frozenset(['Compute', 'Flops'])
    maxval = None
    def compute(self, EV):
        try:
            self.val = FP_Arith_Scalar(self, EV, 4) / EV("UOPS_DISPATCHED.THREAD", 4)
            self.thresh = (self.val > 0.1) and self.parent.thresh
        except ZeroDivisionError:
            handle_error(self, "FP_Scalar zero division")
        return self.val
    desc = """
This metric approximates arithmetic floating-point (FP)
scalar uops fraction the CPU has retired. May overcount due
to FMA double counting.. Investigate what limits (compiler)
generation of vector code."""


class FP_Vector:
    name = "FP_Vector"
    domain = "Uops"
    area = "RET"
    level = 4
    htoff = False
    sample = []
    errcount = 0
    sibling = None
    metricgroup = frozenset(['Compute', 'Flops'])
    maxval = 1.0
    def compute(self, EV):
        try:
            self.val = FP_Arith_Vector(self, EV, 4) / EV("UOPS_DISPATCHED.THREAD", 4)
            self.thresh = (self.val > 0.1) and self.parent.thresh
        except ZeroDivisionError:
            handle_error(self, "FP_Vector zero division")
        return self.val
    desc = """
This metric approximates arithmetic floating-point (FP)
vector uops fraction the CPU has retired aggregated across
all vector widths. May overcount due to FMA double
counting.. Check if vector width is expected"""


class FP_Vector_128b:
    name = "FP_Vector_128b"
    domain = "Uops"
    area = "RET"
    level = 5
    htoff = False
    sample = []
    errcount = 0
    sibling = None
    metricgroup = frozenset(['Compute', 'Flops'])
    maxval = 1.0
    def compute(self, EV):
        try:
            self.val = (EV("FP_COMP_OPS_EXE.SSE_SCALAR_DOUBLE", 5) + EV("FP_COMP_OPS_EXE.SSE_PACKED_DOUBLE", 5)) / EV("UOPS_DISPATCHED.THREAD", 5)
            self.thresh = (self.val > 0.1) and self.parent.thresh
        except ZeroDivisionError:
            handle_error(self, "FP_Vector_128b zero division")
        return self.val
    desc = """
This metric approximates arithmetic FP vector uops fraction
the CPU has retired for 128-bit wide vectors. May overcount
due to FMA double counting.. Try to exploit wider vector
length"""


class FP_Vector_256b:
    name = "FP_Vector_256b"
    domain = "Uops"
    area = "RET"
    level = 5
    htoff = False
    sample = []
    errcount = 0
    sibling = None
    metricgroup = frozenset(['Compute', 'Flops'])
    maxval = 1.0
    def compute(self, EV):
        try:
            self.val = (EV("SIMD_FP_256.PACKED_DOUBLE", 5) + EV("SIMD_FP_256.PACKED_SINGLE", 5)) / EV("UOPS_DISPATCHED.THREAD", 5)
            self.thresh = (self.val > 0.1) and self.parent.thresh
        except ZeroDivisionError:
            handle_error(self, "FP_Vector_256b zero division")
        return self.val
    desc = """
This metric approximates arithmetic FP vector uops fraction
the CPU has retired for 256-bit wide vectors. May overcount
due to FMA double counting.. Try to exploit wider vector
length"""


class Heavy_Operations:
    name = "Heavy_Operations"
    domain = "Slots"
    area = "RET"
    level = 2
    htoff = False
    sample = []
    errcount = 0
    sibling = None
    metricgroup = frozenset(['Retire', 'TmaL2'])
    maxval = None
    def compute(self, EV):
        try:
            self.val = self.Microcode_Sequencer.compute(EV)
            self.thresh = (self.val > 0.1)
        except ZeroDivisionError:
            handle_error(self, "Heavy_Operations zero division")
        return self.val
    desc = """
This metric represents fraction of slots where the CPU was
retiring heavy-weight operations -- instructions that
require two or more uops or micro-coded sequences. This
highly-correlates with the uop length of these
instructions/sequences."""


class Microcode_Sequencer:
    name = "Microcode_Sequencer"
    domain = "Slots"
    area = "RET"
    level = 3
    htoff = False
    sample = ['IDQ.MS_UOPS']
    errcount = 0
    sibling = None
    metricgroup = frozenset(['MicroSeq'])
    maxval = None
    def compute(self, EV):
        try:
            self.val = Retire_Fraction(self, EV, 3) * EV("IDQ.MS_UOPS", 3) / SLOTS(self, EV, 3)
            self.thresh = (self.val > 0.05) and self.parent.thresh
        except ZeroDivisionError:
            handle_error(self, "Microcode_Sequencer zero division")
        return self.val
    desc = """
This metric represents fraction of slots the CPU was
retiring uops fetched by the Microcode Sequencer (MS) unit.
The MS is used for CISC instructions not supported by the
default decoders (like repeat move strings; or CPUID); or by
microcode assists used to address some operation modes (like
in Floating Point assists). These cases can often be
avoided.."""


class Metric_IPC:
    name = "IPC"
    domain = "Metric"
    maxval = Pipeline_Width + 2
    errcount = 0
    area = "Info.Thread"
    metricgroup = frozenset(['Ret', 'Summary'])
    sibling = None

    def compute(self, EV):
        try:
            self.val = IPC(self, EV, 0)
            self.thresh = True
        except ZeroDivisionError:
            handle_error_metric(self, "IPC zero division")
    desc = """
Instructions Per Cycle (per Logical Processor)"""


class Metric_UopPI:
    name = "UopPI"
    domain = "Metric"
    maxval = 2.0
    errcount = 0
    area = "Info.Thread"
    metricgroup = frozenset(['Pipeline', 'Ret', 'Retire'])
    sibling = None

    def compute(self, EV):
        try:
            self.val = UopPI(self, EV, 0)
            self.thresh = (self.val > 1.05)
        except ZeroDivisionError:
            handle_error_metric(self, "UopPI zero division")
    desc = """
Uops Per Instruction"""


class Metric_CPI:
    name = "CPI"
    domain = "Metric"
    maxval = 0
    errcount = 0
    area = "Info.Thread"
    metricgroup = frozenset(['Pipeline', 'Mem'])
    sibling = None

    def compute(self, EV):
        try:
            self.val = CPI(self, EV, 0)
            self.thresh = True
        except ZeroDivisionError:
            handle_error_metric(self, "CPI zero division")
    desc = """
Cycles Per Instruction (per Logical Processor)"""


class Metric_CLKS:
    name = "CLKS"
    domain = "Count"
    maxval = 0
    errcount = 0
    area = "Info.Thread"
    metricgroup = frozenset(['Pipeline'])
    sibling = None

    def compute(self, EV):
        try:
            self.val = CLKS(self, EV, 0)
            self.thresh = True
        except ZeroDivisionError:
            handle_error_metric(self, "CLKS zero division")
    desc = """
Per-Logical Processor actual clocks when the Logical
Processor is active."""


class Metric_SLOTS:
    name = "SLOTS"
    domain = "Count"
    maxval = 0
    errcount = 0
    area = "Info.Thread"
    metricgroup = frozenset(['TmaL1'])
    sibling = None

    def compute(self, EV):
        try:
            self.val = SLOTS(self, EV, 0)
            self.thresh = True
        except ZeroDivisionError:
            handle_error_metric(self, "SLOTS zero division")
    desc = """
Total issue-pipeline slots (per-Physical Core till ICL; per-
Logical Processor ICL onward)"""


class Metric_Execute_per_Issue:
    name = "Execute_per_Issue"
    domain = "Metric"
    maxval = 0
    errcount = 0
    area = "Info.Thread"
    metricgroup = frozenset(['Cor', 'Pipeline'])
    sibling = None

    def compute(self, EV):
        try:
            self.val = Execute_per_Issue(self, EV, 0)
            self.thresh = True
        except ZeroDivisionError:
            handle_error_metric(self, "Execute_per_Issue zero division")
    desc = """
The ratio of Executed- by Issued-Uops. Ratio > 1 suggests
high rate of uop micro-fusions. Ratio < 1 suggest high rate
of \"execute\" at rename stage."""


class Metric_CoreIPC:
    name = "CoreIPC"
    domain = "Core_Metric"
    maxval = Pipeline_Width + 2
    errcount = 0
    area = "Info.Core"
    metricgroup = frozenset(['Ret', 'SMT', 'TmaL1'])
    sibling = None

    def compute(self, EV):
        try:
            self.val = CoreIPC(self, EV, 0)
            self.thresh = True
        except ZeroDivisionError:
            handle_error_metric(self, "CoreIPC zero division")
    desc = """
Instructions Per Cycle across hyper-threads (per physical
core)"""


class Metric_FLOPc:
    name = "FLOPc"
    domain = "Core_Metric"
    maxval = 10.0
    errcount = 0
    area = "Info.Core"
    metricgroup = frozenset(['Ret', 'Flops'])
    sibling = None

    def compute(self, EV):
        try:
            self.val = FLOPc(self, EV, 0)
            self.thresh = True
        except ZeroDivisionError:
            handle_error_metric(self, "FLOPc zero division")
    desc = """
Floating Point Operations Per Cycle"""


class Metric_ILP:
    name = "ILP"
    domain = "Metric"
    maxval = Exe_Ports
    errcount = 0
    area = "Info.Core"
    metricgroup = frozenset(['Backend', 'Cor', 'Pipeline', 'PortsUtil'])
    sibling = None

    def compute(self, EV):
        try:
            self.val = ILP(self, EV, 0)
            self.thresh = True
        except ZeroDivisionError:
            handle_error_metric(self, "ILP zero division")
    desc = """
Instruction-Level-Parallelism (average number of uops
executed when there is execution) per thread (logical-
processor)"""


class Metric_CORE_CLKS:
    name = "CORE_CLKS"
    domain = "Count"
    maxval = 0
    errcount = 0
    area = "Info.Core"
    metricgroup = frozenset(['SMT'])
    sibling = None

    def compute(self, EV):
        try:
            self.val = CORE_CLKS(self, EV, 0)
            self.thresh = True
        except ZeroDivisionError:
            handle_error_metric(self, "CORE_CLKS zero division")
    desc = """
Core actual clocks when any Logical Processor is active on
the Physical Core"""


class Metric_Instructions:
    name = "Instructions"
    domain = "Count"
    maxval = 0
    errcount = 0
    area = "Info.Inst_Mix"
    metricgroup = frozenset(['Summary', 'TmaL1'])
    sibling = None

    def compute(self, EV):
        try:
            self.val = Instructions(self, EV, 0)
            self.thresh = True
        except ZeroDivisionError:
            handle_error_metric(self, "Instructions zero division")
    desc = """
Total number of retired Instructions"""


class Metric_Retire:
    name = "Retire"
    domain = "Metric"
    maxval = 0
    errcount = 0
    area = "Info.Pipeline"
    metricgroup = frozenset(['Pipeline', 'Ret'])
    sibling = None

    def compute(self, EV):
        try:
            self.val = Retire(self, EV, 0)
            self.thresh = True
        except ZeroDivisionError:
            handle_error_metric(self, "Retire zero division")
    desc = """
Average number of Uops retired in cycles where at least one
uop has retired."""


class Metric_DSB_Coverage:
    name = "DSB_Coverage"
    domain = "Metric"
    maxval = 1.0
    errcount = 0
    area = "Info.Frontend"
    metricgroup = frozenset(['DSB', 'Fed', 'FetchBW'])
    sibling = None

    def compute(self, EV):
        try:
            self.val = DSB_Coverage(self, EV, 0)
            self.thresh = (self.val < 0.7) and HighIPC(self, EV, 1)
        except ZeroDivisionError:
            handle_error_metric(self, "DSB_Coverage zero division")
    desc = """
Fraction of Uops delivered by the DSB (aka Decoded ICache;
or Uop Cache). See section 'Decoded ICache' in Optimization
Manual. http://www.intel.com/content/www/us/en/architecture-
and-technology/64-ia-32-architectures-optimization-
manual.html"""


class Metric_CPU_Utilization:
    name = "CPU_Utilization"
    domain = "Metric"
    maxval = 1
    errcount = 0
    area = "Info.System"
    metricgroup = frozenset(['HPC', 'Summary'])
    sibling = None

    def compute(self, EV):
        try:
            self.val = CPU_Utilization(self, EV, 0)
            self.thresh = True
        except ZeroDivisionError:
            handle_error_metric(self, "CPU_Utilization zero division")
    desc = """
Average CPU Utilization (percentage)"""


class Metric_CPUs_Utilized:
    name = "CPUs_Utilized"
    domain = "Metric"
    maxval = 300
    errcount = 0
    area = "Info.System"
    metricgroup = frozenset(['Summary'])
    sibling = None

    def compute(self, EV):
        try:
            self.val = CPUs_Utilized(self, EV, 0)
            self.thresh = True
        except ZeroDivisionError:
            handle_error_metric(self, "CPUs_Utilized zero division")
    desc = """
Average number of utilized CPUs"""


class Metric_Core_Frequency:
    name = "Core_Frequency"
    domain = "SystemMetric"
    maxval = 0
    errcount = 0
    area = "Info.System"
    metricgroup = frozenset(['Summary', 'Power'])
    sibling = None

    def compute(self, EV):
        try:
            self.val = Core_Frequency(self, EV, 0)
            self.thresh = True
        except ZeroDivisionError:
            handle_error_metric(self, "Core_Frequency zero division")
    desc = """
Measured Average Core Frequency for unhalted processors
[GHz]"""


class Metric_Uncore_Frequency:
    name = "Uncore_Frequency"
    domain = "SystemMetric"
    maxval = 0
    errcount = 0
    area = "Info.System"
    metricgroup = frozenset(['SoC'])
    sibling = None

    def compute(self, EV):
        try:
            self.val = Uncore_Frequency(self, EV, 0)
            self.thresh = True
        except ZeroDivisionError:
            handle_error_metric(self, "Uncore_Frequency zero division")
    desc = """
Measured Average Uncore Frequency for the SoC [GHz]"""


class Metric_GFLOPs:
    name = "GFLOPs"
    domain = "Metric"
    maxval = 200
    errcount = 0
    area = "Info.System"
    metricgroup = frozenset(['Cor', 'Flops', 'HPC'])
    sibling = None

    def compute(self, EV):
        try:
            self.val = GFLOPs(self, EV, 0)
            self.thresh = True
        except ZeroDivisionError:
            handle_error_metric(self, "GFLOPs zero division")
    desc = """
Giga Floating Point Operations Per Second. Aggregate across
all supported options of: FP precisions, scalar and vector
instructions, vector-width"""


class Metric_Turbo_Utilization:
    name = "Turbo_Utilization"
    domain = "Core_Metric"
    maxval = 10.0
    errcount = 0
    area = "Info.System"
    metricgroup = frozenset(['Power'])
    sibling = None

    def compute(self, EV):
        try:
            self.val = Turbo_Utilization(self, EV, 0)
            self.thresh = True
        except ZeroDivisionError:
            handle_error_metric(self, "Turbo_Utilization zero division")
    desc = """
Average Frequency Utilization relative nominal frequency"""


class Metric_SMT_2T_Utilization:
    name = "SMT_2T_Utilization"
    domain = "Core_Metric"
    maxval = 1.0
    errcount = 0
    area = "Info.System"
    metricgroup = frozenset(['SMT'])
    sibling = None

    def compute(self, EV):
        try:
            self.val = SMT_2T_Utilization(self, EV, 0)
            self.thresh = True
        except ZeroDivisionError:
            handle_error_metric(self, "SMT_2T_Utilization zero division")
    desc = """
Fraction of cycles where both hardware Logical Processors
were active"""


class Metric_Kernel_Utilization:
    name = "Kernel_Utilization"
    domain = "Metric"
    maxval = 1.0
    errcount = 0
    area = "Info.System"
    metricgroup = frozenset(['OS'])
    sibling = None

    def compute(self, EV):
        try:
            self.val = Kernel_Utilization(self, EV, 0)
            self.thresh = (self.val > 0.05)
        except ZeroDivisionError:
            handle_error_metric(self, "Kernel_Utilization zero division")
    desc = """
Fraction of cycles spent in the Operating System (OS) Kernel
mode"""


class Metric_Kernel_CPI:
    name = "Kernel_CPI"
    domain = "Metric"
    maxval = 0
    errcount = 0
    area = "Info.System"
    metricgroup = frozenset(['OS'])
    sibling = None

    def compute(self, EV):
        try:
            self.val = Kernel_CPI(self, EV, 0)
            self.thresh = True
        except ZeroDivisionError:
            handle_error_metric(self, "Kernel_CPI zero division")
    desc = """
Cycles Per Instruction for the Operating System (OS) Kernel
mode"""


class Metric_DRAM_BW_Use:
    name = "DRAM_BW_Use"
    domain = "GB/sec"
    maxval = 200
    errcount = 0
    area = "Info.System"
    metricgroup = frozenset(['HPC', 'MemOffcore', 'MemoryBW', 'SoC'])
    sibling = None

    def compute(self, EV):
        try:
            self.val = DRAM_BW_Use(self, EV, 0)
            self.thresh = True
        except ZeroDivisionError:
            handle_error_metric(self, "DRAM_BW_Use zero division")
    desc = """
Average external Memory Bandwidth Use for reads and writes
[GB / sec]"""


class Metric_MEM_Read_Latency:
    name = "MEM_Read_Latency"
    domain = "NanoSeconds"
    maxval = 1000
    errcount = 0
    area = "Info.System"
    metricgroup = frozenset(['Mem', 'MemoryLat', 'SoC'])
    sibling = None

    def compute(self, EV):
        try:
            self.val = MEM_Read_Latency(self, EV, 0)
            self.thresh = True
        except ZeroDivisionError:
            handle_error_metric(self, "MEM_Read_Latency zero division")
    desc = """
Average latency of data read request to external memory (in
nanoseconds). Accounts for demand loads and L1/L2
prefetches."""


class Metric_MEM_Parallel_Reads:
    name = "MEM_Parallel_Reads"
    domain = "SystemMetric"
    maxval = 100
    errcount = 0
    area = "Info.System"
    metricgroup = frozenset(['Mem', 'MemoryBW', 'SoC'])
    sibling = None

    def compute(self, EV):
        try:
            self.val = MEM_Parallel_Reads(self, EV, 0)
            self.thresh = True
        except ZeroDivisionError:
            handle_error_metric(self, "MEM_Parallel_Reads zero division")
    desc = """
Average number of parallel data read requests to external
memory. Accounts for demand loads and L1/L2 prefetches"""


class Metric_Time:
    name = "Time"
    domain = "Seconds"
    maxval = 0
    errcount = 0
    area = "Info.System"
    metricgroup = frozenset(['Summary'])
    sibling = None

    def compute(self, EV):
        try:
            self.val = Time(self, EV, 0)
            self.thresh = (self.val < 1)
        except ZeroDivisionError:
            handle_error_metric(self, "Time zero division")
    desc = """
Run duration time in seconds"""


class Metric_Socket_CLKS:
    name = "Socket_CLKS"
    domain = "Count"
    maxval = 0
    errcount = 0
    area = "Info.System"
    metricgroup = frozenset(['SoC'])
    sibling = None

    def compute(self, EV):
        try:
            self.val = Socket_CLKS(self, EV, 0)
            self.thresh = True
        except ZeroDivisionError:
            handle_error_metric(self, "Socket_CLKS zero division")
    desc = """
Socket actual clocks when any core is active on that socket"""


class Metric_IpFarBranch:
    name = "IpFarBranch"
    domain = "Inst_Metric"
    maxval = 0
    errcount = 0
    area = "Info.System"
    metricgroup = frozenset(['Branches', 'OS'])
    sibling = None

    def compute(self, EV):
        try:
            self.val = IpFarBranch(self, EV, 0)
            self.thresh = (self.val < 1000000)
        except ZeroDivisionError:
            handle_error_metric(self, "IpFarBranch zero division")
    desc = """
Instructions per Far Branch ( Far Branches apply upon
transition from application to operating system, handling
interrupts, exceptions) [lower number means higher
occurrence rate]"""


# Schedule



class Setup:
    def __init__(self, r):
        o = dict()
        n = Frontend_Bound() ; r.run(n) ; o["Frontend_Bound"] = n
        n = Fetch_Latency() ; r.run(n) ; o["Fetch_Latency"] = n
        n = ITLB_Misses() ; r.run(n) ; o["ITLB_Misses"] = n
        n = Branch_Resteers() ; r.run(n) ; o["Branch_Resteers"] = n
        n = MS_Switches() ; r.run(n) ; o["MS_Switches"] = n
        n = LCP() ; r.run(n) ; o["LCP"] = n
        n = DSB_Switches() ; r.run(n) ; o["DSB_Switches"] = n
        n = Fetch_Bandwidth() ; r.run(n) ; o["Fetch_Bandwidth"] = n
        n = Bad_Speculation() ; r.run(n) ; o["Bad_Speculation"] = n
        n = Branch_Mispredicts() ; r.run(n) ; o["Branch_Mispredicts"] = n
        n = Machine_Clears() ; r.run(n) ; o["Machine_Clears"] = n
        n = Backend_Bound() ; r.run(n) ; o["Backend_Bound"] = n
        n = Memory_Bound() ; r.run(n) ; o["Memory_Bound"] = n
        n = DTLB_Load() ; r.run(n) ; o["DTLB_Load"] = n
        n = L3_Bound() ; r.run(n) ; o["L3_Bound"] = n
        n = DRAM_Bound() ; r.run(n) ; o["DRAM_Bound"] = n
        n = MEM_Bandwidth() ; r.run(n) ; o["MEM_Bandwidth"] = n
        n = MEM_Latency() ; r.run(n) ; o["MEM_Latency"] = n
        n = Store_Bound() ; r.run(n) ; o["Store_Bound"] = n
        n = Core_Bound() ; r.run(n) ; o["Core_Bound"] = n
        n = Divider() ; r.run(n) ; o["Divider"] = n
        n = Ports_Utilization() ; r.run(n) ; o["Ports_Utilization"] = n
        n = Retiring() ; r.run(n) ; o["Retiring"] = n
        n = Light_Operations() ; r.run(n) ; o["Light_Operations"] = n
        n = FP_Arith() ; r.run(n) ; o["FP_Arith"] = n
        n = X87_Use() ; r.run(n) ; o["X87_Use"] = n
        n = FP_Scalar() ; r.run(n) ; o["FP_Scalar"] = n
        n = FP_Vector() ; r.run(n) ; o["FP_Vector"] = n
        n = FP_Vector_128b() ; r.run(n) ; o["FP_Vector_128b"] = n
        n = FP_Vector_256b() ; r.run(n) ; o["FP_Vector_256b"] = n
        n = Heavy_Operations() ; r.run(n) ; o["Heavy_Operations"] = n
        n = Microcode_Sequencer() ; r.run(n) ; o["Microcode_Sequencer"] = n

        # parents

        o["Fetch_Latency"].parent = o["Frontend_Bound"]
        o["ITLB_Misses"].parent = o["Fetch_Latency"]
        o["Branch_Resteers"].parent = o["Fetch_Latency"]
        o["MS_Switches"].parent = o["Fetch_Latency"]
        o["LCP"].parent = o["Fetch_Latency"]
        o["DSB_Switches"].parent = o["Fetch_Latency"]
        o["Fetch_Bandwidth"].parent = o["Frontend_Bound"]
        o["Branch_Mispredicts"].parent = o["Bad_Speculation"]
        o["Machine_Clears"].parent = o["Bad_Speculation"]
        o["Memory_Bound"].parent = o["Backend_Bound"]
        o["DTLB_Load"].parent = o["Memory_Bound"]
        o["L3_Bound"].parent = o["Memory_Bound"]
        o["DRAM_Bound"].parent = o["Memory_Bound"]
        o["MEM_Bandwidth"].parent = o["DRAM_Bound"]
        o["MEM_Latency"].parent = o["DRAM_Bound"]
        o["Store_Bound"].parent = o["Memory_Bound"]
        o["Core_Bound"].parent = o["Backend_Bound"]
        o["Divider"].parent = o["Core_Bound"]
        o["Ports_Utilization"].parent = o["Core_Bound"]
        o["Light_Operations"].parent = o["Retiring"]
        o["FP_Arith"].parent = o["Light_Operations"]
        o["X87_Use"].parent = o["FP_Arith"]
        o["FP_Scalar"].parent = o["FP_Arith"]
        o["FP_Vector"].parent = o["FP_Arith"]
        o["FP_Vector_128b"].parent = o["FP_Vector"]
        o["FP_Vector_256b"].parent = o["FP_Vector"]
        o["Heavy_Operations"].parent = o["Retiring"]
        o["Microcode_Sequencer"].parent = o["Heavy_Operations"]

        # user visible metrics

        n = Metric_IPC() ; r.metric(n) ; o["IPC"] = n
        n = Metric_UopPI() ; r.metric(n) ; o["UopPI"] = n
        n = Metric_CPI() ; r.metric(n) ; o["CPI"] = n
        n = Metric_CLKS() ; r.metric(n) ; o["CLKS"] = n
        n = Metric_SLOTS() ; r.metric(n) ; o["SLOTS"] = n
        n = Metric_Execute_per_Issue() ; r.metric(n) ; o["Execute_per_Issue"] = n
        n = Metric_CoreIPC() ; r.metric(n) ; o["CoreIPC"] = n
        n = Metric_FLOPc() ; r.metric(n) ; o["FLOPc"] = n
        n = Metric_ILP() ; r.metric(n) ; o["ILP"] = n
        n = Metric_CORE_CLKS() ; r.metric(n) ; o["CORE_CLKS"] = n
        n = Metric_Instructions() ; r.metric(n) ; o["Instructions"] = n
        n = Metric_Retire() ; r.metric(n) ; o["Retire"] = n
        n = Metric_DSB_Coverage() ; r.metric(n) ; o["DSB_Coverage"] = n
        n = Metric_CPU_Utilization() ; r.metric(n) ; o["CPU_Utilization"] = n
        n = Metric_CPUs_Utilized() ; r.metric(n) ; o["CPUs_Utilized"] = n
        n = Metric_Core_Frequency() ; r.metric(n) ; o["Core_Frequency"] = n
        n = Metric_Uncore_Frequency() ; r.metric(n) ; o["Uncore_Frequency"] = n
        n = Metric_GFLOPs() ; r.metric(n) ; o["GFLOPs"] = n
        n = Metric_Turbo_Utilization() ; r.metric(n) ; o["Turbo_Utilization"] = n
        n = Metric_SMT_2T_Utilization() ; r.metric(n) ; o["SMT_2T_Utilization"] = n
        n = Metric_Kernel_Utilization() ; r.metric(n) ; o["Kernel_Utilization"] = n
        n = Metric_Kernel_CPI() ; r.metric(n) ; o["Kernel_CPI"] = n
        n = Metric_DRAM_BW_Use() ; r.metric(n) ; o["DRAM_BW_Use"] = n
        n = Metric_MEM_Read_Latency() ; r.metric(n) ; o["MEM_Read_Latency"] = n
        n = Metric_MEM_Parallel_Reads() ; r.metric(n) ; o["MEM_Parallel_Reads"] = n
        n = Metric_Time() ; r.metric(n) ; o["Time"] = n
        n = Metric_Socket_CLKS() ; r.metric(n) ; o["Socket_CLKS"] = n
        n = Metric_IpFarBranch() ; r.metric(n) ; o["IpFarBranch"] = n

        # references between groups

        o["Fetch_Bandwidth"].Frontend_Bound = o["Frontend_Bound"]
        o["Fetch_Bandwidth"].Fetch_Latency = o["Fetch_Latency"]
        o["Branch_Mispredicts"].Bad_Speculation = o["Bad_Speculation"]
        o["Machine_Clears"].Bad_Speculation = o["Bad_Speculation"]
        o["Machine_Clears"].Branch_Mispredicts = o["Branch_Mispredicts"]
        o["Backend_Bound"].Retiring = o["Retiring"]
        o["Backend_Bound"].Bad_Speculation = o["Bad_Speculation"]
        o["Backend_Bound"].Frontend_Bound = o["Frontend_Bound"]
        o["Memory_Bound"].Retiring = o["Retiring"]
        o["Memory_Bound"].Bad_Speculation = o["Bad_Speculation"]
        o["Memory_Bound"].Frontend_Bound = o["Frontend_Bound"]
        o["Memory_Bound"].Backend_Bound = o["Backend_Bound"]
        o["Memory_Bound"].Fetch_Latency = o["Fetch_Latency"]
        o["MEM_Latency"].MEM_Bandwidth = o["MEM_Bandwidth"]
        o["Core_Bound"].Retiring = o["Retiring"]
        o["Core_Bound"].Frontend_Bound = o["Frontend_Bound"]
        o["Core_Bound"].Memory_Bound = o["Memory_Bound"]
        o["Core_Bound"].Backend_Bound = o["Backend_Bound"]
        o["Core_Bound"].Bad_Speculation = o["Bad_Speculation"]
        o["Core_Bound"].Fetch_Latency = o["Fetch_Latency"]
        o["Ports_Utilization"].Fetch_Latency = o["Fetch_Latency"]
        o["Retiring"].Heavy_Operations = o["Heavy_Operations"]
        o["Light_Operations"].Retiring = o["Retiring"]
        o["Light_Operations"].Heavy_Operations = o["Heavy_Operations"]
        o["Light_Operations"].Microcode_Sequencer = o["Microcode_Sequencer"]
        o["FP_Arith"].FP_Scalar = o["FP_Scalar"]
        o["FP_Arith"].X87_Use = o["X87_Use"]
        o["FP_Arith"].FP_Vector = o["FP_Vector"]
        o["Heavy_Operations"].Microcode_Sequencer = o["Microcode_Sequencer"]

        # siblings cross-tree

        o["MS_Switches"].sibling = (o["Machine_Clears"], o["Microcode_Sequencer"],)
        o["LCP"].sibling = (o["DSB_Switches"], o["Fetch_Bandwidth"],)
        o["DSB_Switches"].sibling = (o["LCP"], o["Fetch_Bandwidth"],)
        o["Fetch_Bandwidth"].sibling = (o["LCP"], o["DSB_Switches"],)
        o["Machine_Clears"].sibling = (o["MS_Switches"], o["Microcode_Sequencer"],)
        o["FP_Scalar"].sibling = (o["FP_Vector"], o["FP_Vector_128b"], o["FP_Vector_256b"],)
        o["FP_Vector"].sibling = (o["FP_Scalar"], o["FP_Vector_128b"], o["FP_Vector_256b"],)
        o["FP_Vector_128b"].sibling = (o["FP_Scalar"], o["FP_Vector"], o["FP_Vector_256b"],)
        o["FP_Vector_256b"].sibling = (o["FP_Scalar"], o["FP_Vector"], o["FP_Vector_128b"],)
        o["Microcode_Sequencer"].sibling = (o["MS_Switches"], o["Machine_Clears"],)
        o["DSB_Coverage"].sibling = (o["LCP"], o["DSB_Switches"], o["Fetch_Bandwidth"],)
        o["DRAM_BW_Use"].sibling = (o["MEM_Bandwidth"],)