snb_client_ratios.py

#
# auto generated TopDown/TMAM 3.14 description for Intel 2nd gen Core (code named SandyBridge)
# Please see http://ark.intel.com for more details on these CPUs.
#
# References:
# http://halobates.de/blog/p/262
# https://sites.google.com/site/analysismethods/yasin-pubs
# https://download.01.org/perfmon/
#

# Helpers

print_error = lambda msg: False
smt_enabled = False
version = "3.14"



# Constants

Pipeline_Width = 4
Mem_L3_Weight = 7
Mem_STLB_Hit_Cost = 7
MS_Switches_Cost = 3
OneMillion = 1000000
OneBillion = 1000000000

# Aux. formulas


# 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))

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

def ITLB_Miss_Cycles(self, EV, level):
    return (9 * EV("ITLB_MISSES.STLB_HIT", level) + EV("ITLB_MISSES.WALK_DURATION", level))

def Frontend_RS_Empty_Cycles(self, EV, level):
    EV("RS_EVENTS.EMPTY_CYCLES", level)
    return EV("RS_EVENTS.EMPTY_CYCLES", level) if(self.Frontend_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 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 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 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)

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 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 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_MISC_RETIRED.LLC_MISS", 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 Avg_RS_Empty_Period_Clears(self, EV, level):
    return (EV("RS_EVENTS.EMPTY_CYCLES", level) - ITLB_Miss_Cycles(self, EV, level)) / EV("RS_EVENTS.EMPTY_END", level)

def Retire_Uop_Fraction(self, EV, level):
    return EV("UOPS_RETIRED.RETIRE_SLOTS", level) / EV("UOPS_ISSUED.ANY", level)

def DurationTimeInSeconds(self, EV, level):
    return 0 if 0 > 0 else(EV("interval-ns", 0) / 1e+06 / 1000 )

def r2r_delta(self, EV, level):
    return max_delta_clk

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

# Uops Per Instruction
def UPI(self, EV, level):
    return EV("UOPS_RETIRED.RETIRE_SLOTS", level) / EV("INST_RETIRED.ANY", level)

# Rough Estimation of fraction of fetched lines bytes that were likely consumed by program instructions
def IFetch_Line_Utilization(self, EV, level):
    return min(1 , EV("UOPS_ISSUED.ANY", level) /(UPI(self, EV, level)* 32 *(EV("ICACHE.HIT", level) + EV("ICACHE.MISSES", level)) / 4))

# Fraction of Uops delivered by the DSB (decoded instructions cache)
def DSB_Coverage(self, EV, level):
    return (EV("IDQ.DSB_UOPS", level) + EV("LSD.UOPS", level)) /(EV("IDQ.DSB_UOPS", level) + EV("LSD.UOPS", level) + EV("IDQ.MITE_UOPS", level) + EV("IDQ.MS_UOPS", level))

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

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

# Total issue-pipeline slots
def SLOTS(self, EV, level):
    return Pipeline_Width * CORE_CLKS(self, EV, level)

# Instructions Per Cycle (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 at least 1 uop executed)
def ILP(self, EV, level):
    return EV("UOPS_DISPATCHED.THREAD", level) / Execute_Cycles(self, EV, level)

# Core actual clocks when any thread is active on the physical core
def CORE_CLKS(self, EV, level):
    return (EV("CPU_CLK_UNHALTED.THREAD_ANY", level) / 2) if smt_enabled else CLKS(self, EV, level)

# Average CPU Utilization
def CPU_Utilization(self, EV, level):
    return EV("CPU_CLK_UNHALTED.REF_TSC", level) / EV("msr/tsc/", 0)

# Giga Floating Point Operations Per Second
def GFLOPs(self, EV, level):
    return FLOP_Count(self, EV, level) / OneBillion / DurationTimeInSeconds(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 threads were active
def SMT_2T_Utilization(self, EV, level):
    return 1 - EV("CPU_CLK_THREAD_UNHALTED.ONE_THREAD_ACTIVE", level) /(EV("CPU_CLK_THREAD_UNHALTED.REF_XCLK_ANY", level) / 2) if smt_enabled else 0

# Fraction of cycles spent in Kernel mode
def Kernel_Utilization(self, EV, level):
    return EV("CPU_CLK_UNHALTED.REF_TSC:sup", level) / EV("CPU_CLK_UNHALTED.REF_TSC", level)

# Average external Memory Bandwidth Use for reads and writes [GB / sec]
def MEM_BW_GBs(self, EV, level):
    return 64 *(EV("UNC_ARB_TRK_REQUESTS.ALL", level) + EV("UNC_ARB_COH_TRK_REQUESTS.ALL", level)) / OneMillion / DurationTimeInSeconds(self, EV, level) / 1000

# Average latency of all requests to external memory (in Uncore cycles)
def MEM_Request_Latency(self, EV, level):
    return EV("UNC_ARB_TRK_OCCUPANCY.ALL", level) / EV("UNC_ARB_TRK_REQUESTS.ALL", level)

# Average number of parallel requests to external memory (in Uncore cycles). Accounts for all requests
def MEM_Parallel_Requests(self, EV, level):
    return EV("UNC_ARB_TRK_OCCUPANCY.ALL", level) / EV("UNC_ARB_TRK_OCCUPANCY.CYCLES_WITH_ANY_REQUEST", level)

# Run duration time in seconds
def Time(self, EV, level):
    return DurationTimeInSeconds(self, EV, level)

def Socket_CLKS(self, EV, level):
    return EV("UNC_CLOCK.SOCKET", level)

# Event groups


class Frontend_Bound:
    name = "Frontend_Bound"
    domain = "Slots"
    area = "FE"
    desc = """
This category represents slots fraction 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-ops (uops). Ideally the Frontend can
issue 4 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."""
    level = 1
    htoff = False
    sample = []
    errcount = 0
    sibling = None
    server = True
    def compute(self, EV):
        try:
            self.val = EV("IDQ_UOPS_NOT_DELIVERED.CORE", 1) / SLOTS(self, EV, 1 )
            self.thresh = (self.val > 0.2)
        except ZeroDivisionError:
            print_error("Frontend_Bound zero division")
            self.errcount += 1
            self.val = 0
            self.thresh = False
        return self.val

class Frontend_Latency:
    name = "Frontend_Latency"
    domain = "Slots"
    area = "FE"
    desc = """
This metric represents slots fraction 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."""
    level = 2
    htoff = False
    sample = ['RS_EVENTS.EMPTY_END']
    errcount = 0
    sibling = None
    server = True
    def compute(self, EV):
        try:
            self.val = Pipeline_Width * Frontend_Latency_Cycles(self, EV, 2) / SLOTS(self, EV, 2 )
            self.thresh = (self.val > 0.15) and self.parent.thresh
        except ZeroDivisionError:
            print_error("Frontend_Latency zero division")
            self.errcount += 1
            self.val = 0
            self.thresh = False
        return self.val

class ITLB_Misses:
    name = "ITLB_Misses"
    domain = "Clocks"
    area = "FE"
    desc = """
This metric represents cycles fraction the CPU was stalled
due to instruction TLB misses.. Using large code pages may
be considered here."""
    level = 3
    htoff = False
    sample = ['ITLB_MISSES.WALK_COMPLETED']
    errcount = 0
    sibling = None
    server = True
    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:
            print_error("ITLB_Misses zero division")
            self.errcount += 1
            self.val = 0
            self.thresh = False
        return self.val

class Branch_Resteers:
    name = "Branch_Resteers"
    domain = "Clocks"
    area = "FE"
    desc = """
This metric represents cycles fraction 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."""
    level = 3
    htoff = False
    sample = ['BR_MISP_RETIRED.ALL_BRANCHES:pp']
    errcount = 0
    sibling = None
    server = True
    def compute(self, EV):
        try:
            self.val = Avg_RS_Empty_Period_Clears(self, EV, 3)*(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:
            print_error("Branch_Resteers zero division")
            self.errcount += 1
            self.val = 0
            self.thresh = False
        return self.val

class DSB_Switches:
    name = "DSB_Switches"
    domain = "Clocks"
    area = "FE"
    desc = """
This metric represents cycles fraction the CPU was stalled
due to switches from DSB to MITE pipelines. The DSB (decoded
i-cache, introduced with the Sandy Bridge microarchitecture)
pipeline has shorter latency and delivered higher bandwidth
than the MITE (legacy instruction decode pipeline).
Switching between the two pipelines can cause penalties.
This metric estimates when such penalty can be exposed.
Optimizing for better DSB hit rate may be considered.. See
section \"Optimization for Decoded ICache\" in Optimization
Guide:. http://www.intel.com/content/www/us/en/architecture-
and-technology/64-ia-32-architectures-optimization-
manual.html"""
    level = 3
    htoff = False
    sample = []
    errcount = 0
    sibling = None
    server = True
    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:
            print_error("DSB_Switches zero division")
            self.errcount += 1
            self.val = 0
            self.thresh = False
        return self.val

class LCP:
    name = "LCP"
    domain = "Clocks"
    area = "FE"
    desc = """
This metric represents cycles fraction CPU was stalled due
to Length Changing Prefixes (LCPs). Using proper compiler
flags or Intel Compiler by default will certainly avoid
this."""
    level = 3
    htoff = False
    sample = []
    errcount = 0
    sibling = None
    server = False
    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:
            print_error("LCP zero division")
            self.errcount += 1
            self.val = 0
            self.thresh = False
        return self.val

class MS_Switches:
    name = "MS_Switches"
    domain = "Clocks"
    area = "FE"
    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 or MITE 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."""
    level = 3
    htoff = False
    sample = ['IDQ.MS_SWITCHES']
    errcount = 0
    sibling = None
    server = True
    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:
            print_error("MS_Switches zero division")
            self.errcount += 1
            self.val = 0
            self.thresh = False
        return self.val

class Frontend_Bandwidth:
    name = "Frontend_Bandwidth"
    domain = "Slots"
    area = "BAD"
    desc = """
This metric represents slots fraction the CPU was stalled
due to Frontend bandwidth issues.  For example,
inefficiencies at the instruction decoders, or code
restrictions for caching in the DSB (decoded uops cache) are
categorized under Frontend Bandwidth. In such cases, the
Frontend typically delivers non-optimal amount of uops to
the Backend (less than four)."""
    level = 2
    htoff = False
    sample = []
    errcount = 0
    sibling = None
    server = True
    def compute(self, EV):
        try:
            self.val = self.Frontend_Bound.compute(EV) - self.Frontend_Latency.compute(EV )
            self.thresh = (self.val > 0.1) & (IPC(self, EV, 2) > 2.0) and self.parent.thresh
        except ZeroDivisionError:
            print_error("Frontend_Bandwidth zero division")
            self.errcount += 1
            self.val = 0
            self.thresh = False
        return self.val

class Bad_Speculation:
    name = "Bad_Speculation"
    domain = "Slots"
    area = "BAD"
    desc = """
This category represents slots fraction 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."""
    level = 1
    htoff = False
    sample = []
    errcount = 0
    sibling = None
    server = True
    def compute(self, EV):
        try:
            self.val = (EV("UOPS_ISSUED.ANY", 1) - EV("UOPS_RETIRED.RETIRE_SLOTS", 1) + Pipeline_Width * Recovery_Cycles(self, EV, 1)) / SLOTS(self, EV, 1 )
            self.thresh = (self.val > 0.1)
        except ZeroDivisionError:
            print_error("Bad_Speculation zero division")
            self.errcount += 1
            self.val = 0
            self.thresh = False
        return self.val

class Branch_Mispredicts:
    name = "Branch_Mispredicts"
    domain = "Slots"
    area = "BAD"
    desc = """
This metric represents slots fraction 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"""
    level = 2
    htoff = False
    sample = ['BR_MISP_RETIRED.ALL_BRANCHES:pp']
    errcount = 0
    sibling = None
    server = True
    def compute(self, EV):
        try:
            self.val = Mispred_Clears_Fraction(self, EV, 2)* self.Bad_Speculation.compute(EV )
            self.thresh = (self.val > 0.05) and self.parent.thresh
        except ZeroDivisionError:
            print_error("Branch_Mispredicts zero division")
            self.errcount += 1
            self.val = 0
            self.thresh = False
        return self.val

class Machine_Clears:
    name = "Machine_Clears"
    domain = "Slots"
    area = "BAD"
    desc = """
This metric represents slots fraction 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
Guide and:. https://software.intel.com/sites/default/files/m
/d/4/1/d/8/sma.pdf"""
    level = 2
    htoff = False
    sample = ['MACHINE_CLEARS.COUNT']
    errcount = 0
    sibling = None
    server = True
    def compute(self, EV):
        try:
            self.val = self.Bad_Speculation.compute(EV) - self.Branch_Mispredicts.compute(EV )
            self.thresh = (self.val > 0.05) and self.parent.thresh
        except ZeroDivisionError:
            print_error("Machine_Clears zero division")
            self.errcount += 1
            self.val = 0
            self.thresh = False
        return self.val

class Backend_Bound:
    name = "Backend_Bound"
    domain = "Slots"
    area = "BE"
    desc = """
This category represents slots fraction 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."""
    level = 1
    htoff = False
    sample = []
    errcount = 0
    sibling = None
    server = True
    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:
            print_error("Backend_Bound zero division")
            self.errcount += 1
            self.val = 0
            self.thresh = False
        return self.val

class Memory_Bound:
    name = "Memory_Bound"
    domain = "Slots"
    area = "BE/Mem"
    desc = """
This metric represents slots fraction the Memory subsystem
within the Backend was a bottleneck.  Memory Bound estimates
slots fraction 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)."""
    level = 2
    htoff = False
    sample = []
    errcount = 0
    sibling = None
    server = True
    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:
            print_error("Memory_Bound zero division")
            self.errcount += 1
            self.val = 0
            self.thresh = False
        return self.val

class DTLB_Load:
    name = "DTLB_Load"
    domain = "Clocks"
    area = "BE/Mem"
    desc = """
This metric represents cycles fraction where the TLB was
missed by load instructions. 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
estimates the performance penalty paid by demand loads when
missing the first-level data TLB (DTLB). This includes
hitting in the second-level TLB (STLB) as well as performing
a hardware page walk on an STLB miss.."""
    level = 4
    htoff = False
    sample = ['MEM_UOPS_RETIRED.STLB_MISS_LOADS:pp']
    errcount = 0
    sibling = None
    server = True
    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)
        except ZeroDivisionError:
            print_error("DTLB_Load zero division")
            self.errcount += 1
            self.val = 0
            self.thresh = False
        return self.val

class L3_Bound:
    name = "L3_Bound"
    domain = "Clocks"
    area = "BE/Mem"
    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."""
    level = 3
    htoff = False
    sample = ['MEM_LOAD_UOPS_RETIRED.LLC_HIT:pp']
    errcount = 0
    sibling = None
    server = True
    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 > 0.1) and self.parent.thresh
        except ZeroDivisionError:
            print_error("L3_Bound zero division")
            self.errcount += 1
            self.val = 0
            self.thresh = False
        return self.val

class MEM_Bound:
    name = "MEM_Bound"
    domain = "Clocks"
    area = "BE/Mem"
    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."""
    level = 3
    htoff = False
    sample = ['MEM_LOAD_UOPS_MISC_RETIRED.LLC_MISS:pp']
    errcount = 0
    sibling = None
    server = True
    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:
            print_error("MEM_Bound zero division")
            self.errcount += 1
            self.val = 0
            self.thresh = False
        return self.val

class MEM_Bandwidth:
    name = "MEM_Bandwidth"
    domain = "Clocks"
    area = "BE/Mem"
    desc = """
This metric estimates cycles fraction where the core's
performance was likely hurt due to approaching bandwidth
limits of external memory (DRAM).  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 thread, requests from other IA
threads/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.."""
    level = 4
    htoff = False
    sample = []
    errcount = 0
    sibling = None
    server = True
    def compute(self, EV):
        try:
            self.val = ORO_DRD_BW_Cycles(self, EV, 4) / CLKS(self, EV, 4 )
            self.thresh = (self.val > 0.1) and self.parent.thresh
        except ZeroDivisionError:
            print_error("MEM_Bandwidth zero division")
            self.errcount += 1
            self.val = 0
            self.thresh = False
        return self.val

class MEM_Latency:
    name = "MEM_Latency"
    domain = "Clocks"
    area = "BE/Mem"
    desc = """
This metric estimates cycles fraction where the performance
was likely hurt due to latency from external memory (DRAM).
This metric does not aggregate requests from other
threads/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).."""
    level = 4
    htoff = False
    sample = []
    errcount = 0
    sibling = None
    server = True
    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:
            print_error("MEM_Latency zero division")
            self.errcount += 1
            self.val = 0
            self.thresh = False
        return self.val

class Store_Bound:
    name = "Store_Bound"
    domain = "Clocks"
    area = "BE/Mem"
    desc = """
This metric estimates how often CPU was stalled  due to
store memory accesses. 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 any of these cases be a bottleneck."""
    level = 3
    htoff = False
    sample = ['MEM_UOPS_RETIRED.ALL_STORES:pp']
    errcount = 0
    sibling = None
    server = True
    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:
            print_error("Store_Bound zero division")
            self.errcount += 1
            self.val = 0
            self.thresh = False
        return self.val

class Core_Bound:
    name = "Core_Bound"
    domain = "Slots"
    area = "BE/Core"
    desc = """
This metric represents slots fraction 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."""
    level = 2
    htoff = False
    sample = []
    errcount = 0
    sibling = None
    server = True
    def compute(self, EV):
        try:
            self.val = self.Backend_Bound.compute(EV) - self.Memory_Bound.compute(EV )
            self.thresh = (self.val > 0.2) and self.parent.thresh
        except ZeroDivisionError:
            print_error("Core_Bound zero division")
            self.errcount += 1
            self.val = 0
            self.thresh = False
        return self.val

class Divider:
    name = "Divider"
    domain = "Clocks"
    area = "BE/Core"
    desc = """
This metric represents cycles fraction 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."""
    level = 3
    htoff = False
    sample = ['ARITH.FPU_DIV_ACTIVE']
    errcount = 0
    sibling = None
    server = True
    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:
            print_error("Divider zero division")
            self.errcount += 1
            self.val = 0
            self.thresh = False
        return self.val

class Ports_Utilization:
    name = "Ports_Utilization"
    domain = "Clocks"
    area = "BE/Core"
    desc = """
This metric estimates cycles fraction 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."""
    level = 3
    htoff = False
    sample = []
    errcount = 0
    sibling = None
    server = True
    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 > 0.2) and self.parent.thresh
        except ZeroDivisionError:
            print_error("Ports_Utilization zero division")
            self.errcount += 1
            self.val = 0
            self.thresh = False
        return self.val

class Retiring:
    name = "Retiring"
    domain = "Slots"
    area = "RET"
    desc = """
This category represents slots fraction 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 4
uops retired per cycle has been achieved.  Maximizing
Retiring typically increases the Instruction-Per-Cycle
metric. Note that a high Retiring value does not necessary
mean there is no room for more performance.  For example,
Microcode assists are categorized under Retiring. They hurt
performance and can often be 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."""
    level = 1
    htoff = False
    sample = []
    errcount = 0
    sibling = None
    server = True
    def compute(self, EV):
        try:
            self.val = EV("UOPS_RETIRED.RETIRE_SLOTS", 1) / SLOTS(self, EV, 1 )
            self.thresh = (self.val > 0.7) | self.Microcode_Sequencer.thresh
        except ZeroDivisionError:
            print_error("Retiring zero division")
            self.errcount += 1
            self.val = 0
            self.thresh = False
        return self.val

class Base:
    name = "Base"
    domain = "Slots"
    area = "RET"
    desc = """
This metric represents slots fraction where the CPU was
retiring regular uops (ones not originated from the
microcode-sequencer). This correlates with total number of
instructions used by the program. A uops-per-instruction
ratio of 1 should be expected. While this is the most
desirable of the top 4 categories, high values does not
necessarily mean there no room for performance
optimizations.. Focus on techniques that reduce instruction
count or result in more efficient instructions generation
such as vectorization."""
    level = 2
    htoff = False
    sample = ['INST_RETIRED.PREC_DIST']
    errcount = 0
    sibling = None
    server = True
    def compute(self, EV):
        try:
            self.val = self.Retiring.compute(EV) - self.Microcode_Sequencer.compute(EV )
            self.thresh = (self.val > 0.6) and self.parent.thresh
        except ZeroDivisionError:
            print_error("Base zero division")
            self.errcount += 1
            self.val = 0
            self.thresh = False
        return self.val

class FP_Arith:
    name = "FP_Arith"
    domain = "Uops"
    area = "RET"
    desc = """
This metric represents overall arithmetic floating-point
(FP) uops fraction the CPU has executed (retired)"""
    level = 3
    htoff = False
    sample = []
    errcount = 0
    sibling = None
    server = True
    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:
            print_error("FP_Arith zero division")
            self.errcount += 1
            self.val = 0
            self.thresh = False
        return self.val

class X87_Use:
    name = "X87_Use"
    domain = "Uops"
    area = "RET"
    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. Tip:
consider compiler flags to generate newer AVX (or SSE)
instruction sets, which typically perform better and feature
vectors."""
    level = 4
    htoff = False
    sample = []
    errcount = 0
    sibling = None
    server = True
    def compute(self, EV):
        try:
            self.val = EV("FP_COMP_OPS_EXE.X87", 4) / EV("UOPS_DISPATCHED.THREAD", 4 )
            self.thresh = (self.val > 0.1) and self.parent.thresh
        except ZeroDivisionError:
            print_error("X87_Use zero division")
            self.errcount += 1
            self.val = 0
            self.thresh = False
        return self.val

class FP_Scalar:
    name = "FP_Scalar"
    domain = "Uops"
    area = "RET"
    desc = """
This metric represents arithmetic floating-point (FP) scalar
uops fraction the CPU has executed (retired).. Investigate
what limits (compiler) generation of vector code."""
    level = 4
    htoff = False
    sample = []
    errcount = 0
    sibling = None
    server = True
    def compute(self, EV):
        try:
            self.val = (EV("FP_COMP_OPS_EXE.SSE_SCALAR_SINGLE", 4) + EV("FP_COMP_OPS_EXE.SSE_SCALAR_DOUBLE", 4)) / EV("UOPS_DISPATCHED.THREAD", 4 )
            self.thresh = (self.val > 0.1) and self.parent.thresh
        except ZeroDivisionError:
            print_error("FP_Scalar zero division")
            self.errcount += 1
            self.val = 0
            self.thresh = False
        return self.val

class FP_Vector:
    name = "FP_Vector"
    domain = "Uops"
    area = "RET"
    desc = """
This metric represents arithmetic floating-point (FP) vector
uops fraction the CPU has executed (retired) aggregated
across all vector widths.. Check if vector width is expected"""
    level = 4
    htoff = False
    sample = []
    errcount = 0
    sibling = None
    server = True
    def compute(self, EV):
        try:
            self.val = (EV("FP_COMP_OPS_EXE.SSE_PACKED_DOUBLE", 4) + EV("FP_COMP_OPS_EXE.SSE_PACKED_SINGLE", 4) + EV("SIMD_FP_256.PACKED_SINGLE", 4) + EV("SIMD_FP_256.PACKED_DOUBLE", 4)) / EV("UOPS_DISPATCHED.THREAD", 4 )
            self.thresh = (self.val > 0.2) and self.parent.thresh
        except ZeroDivisionError:
            print_error("FP_Vector zero division")
            self.errcount += 1
            self.val = 0
            self.thresh = False
        return self.val

class Other:
    name = "Other"
    domain = "Uops"
    area = "RET"
    desc = """
This metric represents non-floating-point (FP) uop fraction
the CPU has executed. If you application has no FP
operations and performs with decent IPC (Instructions Per
Cycle), this node will likely be biggest fraction."""
    level = 3
    htoff = False
    sample = []
    errcount = 0
    sibling = None
    server = True
    def compute(self, EV):
        try:
            self.val = 1 - self.FP_Arith.compute(EV )
            self.thresh = (self.val > 0.3) and self.parent.thresh
        except ZeroDivisionError:
            print_error("Other zero division")
            self.errcount += 1
            self.val = 0
            self.thresh = False
        return self.val

class Microcode_Sequencer:
    name = "Microcode_Sequencer"
    domain = "Slots"
    area = "RET"
    desc = """
This metric represents slots fraction 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.."""
    level = 2
    htoff = False
    sample = ['IDQ.MS_UOPS']
    errcount = 0
    sibling = None
    server = True
    def compute(self, EV):
        try:
            self.val = Retire_Uop_Fraction(self, EV, 2)* EV("IDQ.MS_UOPS", 2) / SLOTS(self, EV, 2 )
            self.thresh = (self.val > 0.05)
        except ZeroDivisionError:
            print_error("Microcode_Sequencer zero division")
            self.errcount += 1
            self.val = 0
            self.thresh = False
        return self.val

class Metric_IPC:
    name = "IPC"
    desc = """
Instructions Per Cycle (per logical thread)"""
    domain = "Metric"
    maxval = 5
    server = True
    errcount = 0

    def compute(self, EV):
        try:
	    self.val = IPC(self, EV, 0)
        except ZeroDivisionError:
            print_error("IPC zero division")
            self.errcount += 1
	    self.val = 0

class Metric_UPI:
    name = "UPI"
    desc = """
Uops Per Instruction"""
    domain = "Metric"
    maxval = 2
    server = True
    errcount = 0

    def compute(self, EV):
        try:
	    self.val = UPI(self, EV, 0)
        except ZeroDivisionError:
            print_error("UPI zero division")
            self.errcount += 1
	    self.val = 0

class Metric_IFetch_Line_Utilization:
    name = "IFetch_Line_Utilization"
    desc = """
Rough Estimation of fraction of fetched lines bytes that
were likely consumed by program instructions"""
    domain = "Metric"
    maxval = 1
    server = True
    errcount = 0

    def compute(self, EV):
        try:
	    self.val = IFetch_Line_Utilization(self, EV, 0)
        except ZeroDivisionError:
            print_error("IFetch_Line_Utilization zero division")
            self.errcount += 1
	    self.val = 0

class Metric_DSB_Coverage:
    name = "DSB_Coverage"
    desc = """
Fraction of Uops delivered by the DSB (decoded instructions
cache)"""
    domain = "Metric"
    maxval = 1
    server = True
    errcount = 0

    def compute(self, EV):
        try:
	    self.val = DSB_Coverage(self, EV, 0)
        except ZeroDivisionError:
            print_error("DSB_Coverage zero division")
            self.errcount += 1
	    self.val = 0

class Metric_CPI:
    name = "CPI"
    desc = """
Cycles Per Instruction (threaded)"""
    domain = "Metric"
    maxval = 0
    server = True
    errcount = 0

    def compute(self, EV):
        try:
	    self.val = CPI(self, EV, 0)
        except ZeroDivisionError:
            print_error("CPI zero division")
            self.errcount += 1
	    self.val = 0

class Metric_CLKS:
    name = "CLKS"
    desc = """
Per-thread actual clocks when the thread is active"""
    domain = "Count"
    maxval = 0
    server = True
    errcount = 0

    def compute(self, EV):
        try:
	    self.val = CLKS(self, EV, 0)
        except ZeroDivisionError:
            print_error("CLKS zero division")
            self.errcount += 1
	    self.val = 0

class Metric_SLOTS:
    name = "SLOTS"
    desc = """
Total issue-pipeline slots"""
    domain = "Count"
    maxval = 0
    server = True
    errcount = 0

    def compute(self, EV):
        try:
	    self.val = SLOTS(self, EV, 0)
        except ZeroDivisionError:
            print_error("SLOTS zero division")
            self.errcount += 1
	    self.val = 0

class Metric_CoreIPC:
    name = "CoreIPC"
    desc = """
Instructions Per Cycle (per physical core)"""
    domain = "CoreMetric"
    maxval = 5
    server = True
    errcount = 0

    def compute(self, EV):
        try:
	    self.val = CoreIPC(self, EV, 0)
        except ZeroDivisionError:
            print_error("CoreIPC zero division")
            self.errcount += 1
	    self.val = 0

class Metric_FLOPc:
    name = "FLOPc"
    desc = """
Floating Point Operations Per Cycle"""
    domain = "CoreMetric"
    maxval = 10
    server = True
    errcount = 0

    def compute(self, EV):
        try:
	    self.val = FLOPc(self, EV, 0)
        except ZeroDivisionError:
            print_error("FLOPc zero division")
            self.errcount += 1
	    self.val = 0

class Metric_ILP:
    name = "ILP"
    desc = """
Instruction-Level-Parallelism (average number of uops
executed when there is at least 1 uop executed)"""
    domain = "CoreMetric"
    maxval = 10
    server = True
    errcount = 0

    def compute(self, EV):
        try:
	    self.val = ILP(self, EV, 0)
        except ZeroDivisionError:
            print_error("ILP zero division")
            self.errcount += 1
	    self.val = 0

class Metric_CORE_CLKS:
    name = "CORE_CLKS"
    desc = """
Core actual clocks when any thread is active on the physical
core"""
    domain = "Count"
    maxval = 0
    server = True
    errcount = 0

    def compute(self, EV):
        try:
	    self.val = CORE_CLKS(self, EV, 0)
        except ZeroDivisionError:
            print_error("CORE_CLKS zero division")
            self.errcount += 1
	    self.val = 0

class Metric_CPU_Utilization:
    name = "CPU_Utilization"
    desc = """
Average CPU Utilization"""
    domain = "Metric"
    maxval = 100
    server = True
    errcount = 0

    def compute(self, EV):
        try:
	    self.val = CPU_Utilization(self, EV, 0)
        except ZeroDivisionError:
            print_error("CPU_Utilization zero division")
            self.errcount += 1
	    self.val = 0

class Metric_GFLOPs:
    name = "GFLOPs"
    desc = """
Giga Floating Point Operations Per Second"""
    domain = "Metric"
    maxval = 100
    server = True
    errcount = 0

    def compute(self, EV):
        try:
	    self.val = GFLOPs(self, EV, 0)
        except ZeroDivisionError:
            print_error("GFLOPs zero division")
            self.errcount += 1
	    self.val = 0

class Metric_Turbo_Utilization:
    name = "Turbo_Utilization"
    desc = """
Average Frequency Utilization relative nominal frequency"""
    domain = "CoreMetric"
    maxval = 10
    server = True
    errcount = 0

    def compute(self, EV):
        try:
	    self.val = Turbo_Utilization(self, EV, 0)
        except ZeroDivisionError:
            print_error("Turbo_Utilization zero division")
            self.errcount += 1
	    self.val = 0

class Metric_SMT_2T_Utilization:
    name = "SMT_2T_Utilization"
    desc = """
Fraction of cycles where both hardware threads were active"""
    domain = "CoreMetric"
    maxval = 1
    server = True
    errcount = 0

    def compute(self, EV):
        try:
	    self.val = SMT_2T_Utilization(self, EV, 0)
        except ZeroDivisionError:
            print_error("SMT_2T_Utilization zero division")
            self.errcount += 1
	    self.val = 0

class Metric_Kernel_Utilization:
    name = "Kernel_Utilization"
    desc = """
Fraction of cycles spent in Kernel mode"""
    domain = "Metric"
    maxval = 1
    server = True
    errcount = 0

    def compute(self, EV):
        try:
	    self.val = Kernel_Utilization(self, EV, 0)
        except ZeroDivisionError:
            print_error("Kernel_Utilization zero division")
            self.errcount += 1
	    self.val = 0

class Metric_MEM_BW_GBs:
    name = "MEM_BW_GBs"
    desc = """
Average external Memory Bandwidth Use for reads and writes
[GB / sec]"""
    domain = "Metric"
    maxval = 100
    server = True
    errcount = 0

    def compute(self, EV):
        try:
	    self.val = MEM_BW_GBs(self, EV, 0)
        except ZeroDivisionError:
            print_error("MEM_BW_GBs zero division")
            self.errcount += 1
	    self.val = 0

class Metric_MEM_Request_Latency:
    name = "MEM_Request_Latency"
    desc = """
Average latency of all requests to external memory (in
Uncore cycles)"""
    domain = "Metric"
    maxval = 1000
    server = False
    errcount = 0

    def compute(self, EV):
        try:
	    self.val = MEM_Request_Latency(self, EV, 0)
        except ZeroDivisionError:
            print_error("MEM_Request_Latency zero division")
            self.errcount += 1
	    self.val = 0

class Metric_MEM_Parallel_Requests:
    name = "MEM_Parallel_Requests"
    desc = """
Average number of parallel requests to external memory (in
Uncore cycles). Accounts for all requests"""
    domain = "Metric"
    maxval = 100
    server = False
    errcount = 0

    def compute(self, EV):
        try:
	    self.val = MEM_Parallel_Requests(self, EV, 0)
        except ZeroDivisionError:
            print_error("MEM_Parallel_Requests zero division")
            self.errcount += 1
	    self.val = 0

class Metric_Time:
    name = "Time"
    desc = """
Run duration time in seconds"""
    domain = "Count"
    maxval = 0
    server = True
    errcount = 0

    def compute(self, EV):
        try:
	    self.val = Time(self, EV, 0)
        except ZeroDivisionError:
            print_error("Time zero division")
            self.errcount += 1
	    self.val = 0

class Metric_Socket_CLKS:
    name = "Socket_CLKS"
    desc = """
"""
    domain = "Count"
    maxval = 0
    server = True
    errcount = 0

    def compute(self, EV):
        try:
	    self.val = Socket_CLKS(self, EV, 0)
        except ZeroDivisionError:
            print_error("Socket_CLKS zero division")
            self.errcount += 1
	    self.val = 0

# Schedule



class Setup:
    def __init__(self, r):
	o = dict()
        n = Frontend_Bound() ; r.run(n) ; o["Frontend_Bound"] = n
        n = Frontend_Latency() ; r.run(n) ; o["Frontend_Latency"] = n
        n = ITLB_Misses() ; r.run(n) ; o["ITLB_Misses"] = n
        n = Branch_Resteers() ; r.run(n) ; o["Branch_Resteers"] = n
        n = DSB_Switches() ; r.run(n) ; o["DSB_Switches"] = n
        n = LCP() ; r.run(n) ; o["LCP"] = n
        n = MS_Switches() ; r.run(n) ; o["MS_Switches"] = n
        n = Frontend_Bandwidth() ; r.run(n) ; o["Frontend_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 = MEM_Bound() ; r.run(n) ; o["MEM_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 = Base() ; r.run(n) ; o["Base"] = 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 = Other() ; r.run(n) ; o["Other"] = n
        n = Microcode_Sequencer() ; r.run(n) ; o["Microcode_Sequencer"] = n

        # parents

        o["Frontend_Latency"].parent = o["Frontend_Bound"]
        o["ITLB_Misses"].parent = o["Frontend_Latency"]
        o["Branch_Resteers"].parent = o["Frontend_Latency"]
        o["DSB_Switches"].parent = o["Frontend_Latency"]
        o["LCP"].parent = o["Frontend_Latency"]
        o["MS_Switches"].parent = o["Frontend_Latency"]
        o["Frontend_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["MEM_Bound"].parent = o["Memory_Bound"]
        o["MEM_Bandwidth"].parent = o["MEM_Bound"]
        o["MEM_Latency"].parent = o["MEM_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["Base"].parent = o["Retiring"]
        o["FP_Arith"].parent = o["Base"]
        o["X87_Use"].parent = o["FP_Arith"]
        o["FP_Scalar"].parent = o["FP_Arith"]
        o["FP_Vector"].parent = o["FP_Arith"]
        o["Other"].parent = o["Base"]
        o["Microcode_Sequencer"].parent = o["Retiring"]

        # references between groups

        o["Frontend_Bandwidth"].Frontend_Bound = o["Frontend_Bound"]
        o["Frontend_Bandwidth"].Frontend_Latency = o["Frontend_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"].Backend_Bound = o["Backend_Bound"]
        o["Memory_Bound"].Frontend_Latency = o["Frontend_Latency"]
        o["MEM_Latency"].MEM_Bandwidth = o["MEM_Bandwidth"]
        o["Core_Bound"].Memory_Bound = o["Memory_Bound"]
        o["Core_Bound"].Backend_Bound = o["Backend_Bound"]
        o["Ports_Utilization"].Frontend_Latency = o["Frontend_Latency"]
        o["Retiring"].Microcode_Sequencer = o["Microcode_Sequencer"]
        o["Base"].Retiring = o["Retiring"]
        o["Base"].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["Other"].FP_Arith = o["FP_Arith"]

        # siblings cross-tree

	o["Branch_Resteers"].sibling = o["Bad_Speculation"]
	o["MS_Switches"].sibling = o["Microcode_Sequencer"]
	o["Bad_Speculation"].sibling = o["Branch_Resteers"]
	o["Microcode_Sequencer"].sibling = o["MS_Switches"]

        # user visible metrics

        n = Metric_IPC() ; r.metric(n)
        n = Metric_UPI() ; r.metric(n)
        n = Metric_IFetch_Line_Utilization() ; r.metric(n)
        n = Metric_DSB_Coverage() ; r.metric(n)
        n = Metric_CPI() ; r.metric(n)
        n = Metric_CLKS() ; r.metric(n)
        n = Metric_SLOTS() ; r.metric(n)
        n = Metric_CoreIPC() ; r.metric(n)
        n = Metric_FLOPc() ; r.metric(n)
        n = Metric_ILP() ; r.metric(n)
        n = Metric_CORE_CLKS() ; r.metric(n)
        n = Metric_CPU_Utilization() ; r.metric(n)
        n = Metric_GFLOPs() ; r.metric(n)
        n = Metric_Turbo_Utilization() ; r.metric(n)
        n = Metric_SMT_2T_Utilization() ; r.metric(n)
        n = Metric_Kernel_Utilization() ; r.metric(n)
        n = Metric_MEM_BW_GBs() ; r.metric(n)
        n = Metric_MEM_Request_Latency() ; r.metric(n)
        n = Metric_MEM_Parallel_Requests() ; r.metric(n)
        n = Metric_Time() ; r.metric(n)
        n = Metric_Socket_CLKS() ; r.metric(n)