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adl_grt_ratios.py
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adl_grt_ratios.py
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# -*- coding: latin-1 -*-
#
# auto generated TopDown/TMA 2.0 description for Intel 12th gen Core (code name Alderlake) with GraceMont
# 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
version = "2.0"
base_frequency = -1.0
Memory = 0
Average_Frequency = 0.0
use_aux = False
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
# Aux. formulas
# pipeline allocation width
def Pipeline_Width(self, EV, level):
return 5
def CLKS(self, EV, level):
return EV("CPU_CLK_UNHALTED.CORE", level)
def CLKS_P(self, EV, level):
return EV("CPU_CLK_UNHALTED.CORE_P", level)
def SLOTS(self, EV, level):
return Pipeline_Width(self, EV, level) * CLKS(self, EV, level)
# Instructions Per Cycle
def IPC(self, EV, level):
return EV("INST_RETIRED.ANY", level) / CLKS(self, EV, level)
# Cycles Per Instruction
def CPI(self, EV, level):
return CLKS(self, EV, level) / EV("INST_RETIRED.ANY", level)
# Uops Per Instruction
def UPI(self, EV, level):
return EV("UOPS_RETIRED.ALL", level) / EV("INST_RETIRED.ANY", level)
# Percentage of total non-speculative loads with a store forward or unknown store address block
def Store_Fwd_Blocks(self, EV, level):
return 100 * EV("LD_BLOCKS.DATA_UNKNOWN", level) / EV("MEM_UOPS_RETIRED.ALL_LOADS", level)
# Percentage of total non-speculative loads with a address aliasing block
def Address_Alias_Blocks(self, EV, level):
return 100 * EV("LD_BLOCKS.4K_ALIAS", level) / EV("MEM_UOPS_RETIRED.ALL_LOADS", level)
# Percentage of total non-speculative loads that are splits
def Load_Splits(self, EV, level):
return 100 * EV("MEM_UOPS_RETIRED.SPLIT_LOADS", level) / EV("MEM_UOPS_RETIRED.ALL_LOADS", level)
# Instructions per Branch (lower number means higher occurance rate)
def IpBranch(self, EV, level):
return EV("INST_RETIRED.ANY", level) / EV("BR_INST_RETIRED.ALL_BRANCHES", level)
# Instruction per (near) call (lower number means higher occurance rate)
def IpCall(self, EV, level):
return EV("INST_RETIRED.ANY", level) / EV("BR_INST_RETIRED.CALL", level)
# Instructions per Load
def IpLoad(self, EV, level):
return EV("INST_RETIRED.ANY", level) / EV("MEM_UOPS_RETIRED.ALL_LOADS", level)
# Instructions per Store
def IpStore(self, EV, level):
return EV("INST_RETIRED.ANY", level) / EV("MEM_UOPS_RETIRED.ALL_STORES", level)
# Number of Instructions per non-speculative Branch Misprediction
def IpMispredict(self, EV, level):
return EV("INST_RETIRED.ANY", level) / EV("BR_MISP_RETIRED.ALL_BRANCHES", level)
# Instructions per Far Branch
def IpFarBranch(self, EV, level):
return EV("INST_RETIRED.ANY", level) / (EV("BR_INST_RETIRED.FAR_BRANCH", level) / 2 )
# Ratio of all branches which mispredict
def Branch_Mispredict_Ratio(self, EV, level):
return EV("BR_MISP_RETIRED.ALL_BRANCHES", level) / EV("BR_INST_RETIRED.ALL_BRANCHES", level)
# Ratio between Mispredicted branches and unknown branches
def Branch_Mispredict_to_Unknown_Branch_Ratio(self, EV, level):
return EV("BR_MISP_RETIRED.ALL_BRANCHES", level) / EV("BACLEARS.ANY", level)
# Percentage of all uops which are ucode ops
def Microcode_Uop_Ratio(self, EV, level):
return 100 * EV("UOPS_RETIRED.MS", level) / EV("UOPS_RETIRED.ALL", level)
# Percentage of all uops which are FPDiv uops
def FPDiv_Uop_Ratio(self, EV, level):
return 100 * EV("UOPS_RETIRED.FPDIV", level) / EV("UOPS_RETIRED.ALL", level)
# Percentage of all uops which are IDiv uops
def IDiv_Uop_Ratio(self, EV, level):
return 100 * EV("UOPS_RETIRED.IDIV", level) / EV("UOPS_RETIRED.ALL", level)
# Percentage of all uops which are x87 uops
def X87_Uop_Ratio(self, EV, level):
return 100 * EV("UOPS_RETIRED.X87", level) / EV("UOPS_RETIRED.ALL", 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 spent in Kernel mode
def Kernel_Utilization(self, EV, level):
return EV("CPU_CLK_UNHALTED.CORE:sup", level) / EV("CPU_CLK_UNHALTED.CORE", level)
# Average CPU Utilization
def CPU_Utilization(self, EV, level):
return EV("CPU_CLK_UNHALTED.REF_TSC", level) / EV("msr/tsc/", 0)
# Estimated Pause cost. In percent
def Estimated_Pause_Cost(self, EV, level):
return 100 * EV("SERIALIZATION.NON_C01_MS_SCB", level) / SLOTS(self, EV, level)
# Cycle cost per L2 hit
def Cycles_per_Demand_Load_L2_Hit(self, EV, level):
return EV("MEM_BOUND_STALLS.LOAD_L2_HIT", level) / EV("MEM_LOAD_UOPS_RETIRED.L2_HIT", level)
# Cycle cost per LLC hit
def Cycles_per_Demand_Load_L3_Hit(self, EV, level):
return EV("MEM_BOUND_STALLS.LOAD_LLC_HIT", level) / EV("MEM_LOAD_UOPS_RETIRED.L3_HIT", level)
# Cycle cost per DRAM hit
def Cycles_per_Demand_Load_DRAM_Hit(self, EV, level):
return EV("MEM_BOUND_STALLS.LOAD_DRAM_HIT", level) / EV("MEM_LOAD_UOPS_RETIRED.DRAM_HIT", level)
# Percent of instruction miss cost that hit in the L2
def Inst_Miss_Cost_L2Hit_Percent(self, EV, level):
return 100 * EV("MEM_BOUND_STALLS.IFETCH_L2_HIT", level) / (EV("MEM_BOUND_STALLS.IFETCH", level))
# Percent of instruction miss cost that hit in the L3
def Inst_Miss_Cost_L3Hit_Percent(self, EV, level):
return 100 * EV("MEM_BOUND_STALLS.IFETCH_LLC_HIT", level) / (EV("MEM_BOUND_STALLS.IFETCH", level))
# Percent of instruction miss cost that hit in DRAM
def Inst_Miss_Cost_DRAMHit_Percent(self, EV, level):
return 100 * EV("MEM_BOUND_STALLS.IFETCH_DRAM_HIT", level) / (EV("MEM_BOUND_STALLS.IFETCH", level))
# load ops retired per 1000 instruction
def MemLoadPKI(self, EV, level):
return 1000 * EV("MEM_UOPS_RETIRED.ALL_LOADS", level) / EV("INST_RETIRED.ANY", level)
# Event groups
class Frontend_Bound:
name = "Frontend_Bound"
domain = "Slots"
area = "FE"
level = 1
htoff = False
sample = []
errcount = 0
sibling = None
server = False
metricgroup = []
def compute(self, EV):
try:
self.val = EV("TOPDOWN_FE_BOUND.ALL", 1) / SLOTS(self, EV, 1)
self.thresh = (self.val > 0.20)
except ZeroDivisionError:
handle_error(self, "Frontend_Bound zero division")
return self.val
desc = """
Counts the number of issue slots that were not consumed by
the backend due to frontend stalls."""
class Frontend_Latency:
name = "Frontend_Latency"
domain = "Slots"
area = "FE"
level = 2
htoff = False
sample = []
errcount = 0
sibling = None
server = False
metricgroup = []
def compute(self, EV):
try:
self.val = EV("TOPDOWN_FE_BOUND.FRONTEND_LATENCY", 2) / SLOTS(self, EV, 2)
self.thresh = (self.val > 0.15)
except ZeroDivisionError:
handle_error(self, "Frontend_Latency zero division")
return self.val
desc = """
Counts the number of issue slots that were not delivered by
the frontend due to frontend bandwidth restrictions due to
decode, predecode, cisc, and other limitations."""
class Icache:
name = "Icache"
domain = "Slots"
area = "FE"
level = 3
htoff = False
sample = []
errcount = 0
sibling = None
server = False
metricgroup = []
def compute(self, EV):
try:
self.val = EV("TOPDOWN_FE_BOUND.ICACHE", 3) / SLOTS(self, EV, 3)
self.thresh = (self.val > 0.05)
except ZeroDivisionError:
handle_error(self, "Icache zero division")
return self.val
desc = """
Counts the number of issue slots that were not delivered by
the frontend due to instruction cache misses."""
class ITLB:
name = "ITLB"
domain = "Slots"
area = "FE"
level = 3
htoff = False
sample = []
errcount = 0
sibling = None
server = False
metricgroup = []
def compute(self, EV):
try:
self.val = EV("TOPDOWN_FE_BOUND.ITLB", 3) / SLOTS(self, EV, 3)
self.thresh = (self.val > 0.05)
except ZeroDivisionError:
handle_error(self, "ITLB zero division")
return self.val
desc = """
Counts the number of issue slots that were not delivered by
the frontend due to Instruction Table Lookaside Buffer
(ITLB) misses."""
class Branch_Detect:
name = "Branch_Detect"
domain = "Slots"
area = "FE"
level = 3
htoff = False
sample = []
errcount = 0
sibling = None
server = False
metricgroup = []
def compute(self, EV):
try:
self.val = EV("TOPDOWN_FE_BOUND.BRANCH_DETECT", 3) / SLOTS(self, EV, 3)
self.thresh = (self.val > 0.05)
except ZeroDivisionError:
handle_error(self, "Branch_Detect zero division")
return self.val
desc = """
Counts the number of issue slots that were not delivered by
the frontend due to BACLEARS, which occurs when the Branch
Target Buffer (BTB) prediction or lack thereof, was
corrected by a later branch predictor in the frontend.
Includes BACLEARS due to all branch types including
conditional and unconditional jumps, returns, and indirect
branches."""
class Branch_Resteer:
name = "Branch_Resteer"
domain = "Slots"
area = "FE"
level = 3
htoff = False
sample = []
errcount = 0
sibling = None
server = False
metricgroup = []
def compute(self, EV):
try:
self.val = EV("TOPDOWN_FE_BOUND.BRANCH_RESTEER", 3) / SLOTS(self, EV, 3)
self.thresh = (self.val > 0.05)
except ZeroDivisionError:
handle_error(self, "Branch_Resteer zero division")
return self.val
desc = """
Counts the number of issue slots that were not delivered by
the frontend due to BTCLEARS, which occurs when the Branch
Target Buffer (BTB) predicts a taken branch."""
class Frontend_Bandwidth:
name = "Frontend_Bandwidth"
domain = "Slots"
area = "FE"
level = 2
htoff = False
sample = []
errcount = 0
sibling = None
server = False
metricgroup = []
def compute(self, EV):
try:
self.val = EV("TOPDOWN_FE_BOUND.FRONTEND_BANDWIDTH", 2) / SLOTS(self, EV, 2)
self.thresh = (self.val > 0.10)
except ZeroDivisionError:
handle_error(self, "Frontend_Bandwidth zero division")
return self.val
desc = """
Counts the number of issue slots that were not delivered by
the frontend due to frontend bandwidth restrictions due to
decode, predecode, cisc, and other limitations."""
class Cisc:
name = "Cisc"
domain = "Slots"
area = "FE"
level = 3
htoff = False
sample = []
errcount = 0
sibling = None
server = False
metricgroup = []
def compute(self, EV):
try:
self.val = EV("TOPDOWN_FE_BOUND.CISC", 3) / SLOTS(self, EV, 3)
self.thresh = (self.val > 0.05)
except ZeroDivisionError:
handle_error(self, "Cisc zero division")
return self.val
desc = """
Counts the number of issue slots that were not delivered by
the frontend due to the microcode sequencer (MS)."""
class Decode:
name = "Decode"
domain = "Slots"
area = "FE"
level = 3
htoff = False
sample = []
errcount = 0
sibling = None
server = False
metricgroup = []
def compute(self, EV):
try:
self.val = EV("TOPDOWN_FE_BOUND.DECODE", 3) / SLOTS(self, EV, 3)
self.thresh = (self.val > 0.05)
except ZeroDivisionError:
handle_error(self, "Decode zero division")
return self.val
desc = """
Counts the number of issue slots that were not delivered by
the frontend due to decode stalls."""
class Predecode:
name = "Predecode"
domain = "Slots"
area = "FE"
level = 3
htoff = False
sample = []
errcount = 0
sibling = None
server = False
metricgroup = []
def compute(self, EV):
try:
self.val = EV("TOPDOWN_FE_BOUND.PREDECODE", 3) / SLOTS(self, EV, 3)
self.thresh = (self.val > 0.05)
except ZeroDivisionError:
handle_error(self, "Predecode zero division")
return self.val
desc = """
Counts the number of issue slots that were not delivered by
the frontend due to wrong predecodes."""
class Other_FB:
name = "Other_FB"
domain = "Slots"
area = "FE"
level = 3
htoff = False
sample = []
errcount = 0
sibling = None
server = False
metricgroup = []
def compute(self, EV):
try:
self.val = EV("TOPDOWN_FE_BOUND.OTHER", 3) / SLOTS(self, EV, 3)
self.thresh = (self.val > 0.05)
except ZeroDivisionError:
handle_error(self, "Other_FB zero division")
return self.val
desc = """
Counts the number of issue slots that were not delivered by
the frontend due to other common frontend stalls not
categorized."""
class Bad_Speculation:
name = "Bad_Speculation"
domain = "Slots"
area = "BAD"
level = 1
htoff = False
sample = []
errcount = 0
sibling = None
server = False
metricgroup = []
def compute(self, EV):
try:
self.val = EV("TOPDOWN_BAD_SPECULATION.ALL", 1) / SLOTS(self, EV, 1)
self.thresh = (self.val > 0.15)
except ZeroDivisionError:
handle_error(self, "Bad_Speculation zero division")
return self.val
desc = """
Counts the total number of issue slots that were not
consumed by the backend because allocation is stalled due to
a mispredicted jump or a machine clear. Only issue slots
wasted due to fast nukes such as memory ordering nukes are
counted. Other nukes are not accounted for. Counts all issue
slots blocked during this recovery window including relevant
microcode flows and while uops are not yet available in the
instruction queue (IQ). Also includes the issue slots that
were consumed by the backend but were thrown away because
they were younger than the mispredict or machine clear."""
class Branch_Mispredicts:
name = "Branch_Mispredicts"
domain = "Slots"
area = "BAD"
level = 2
htoff = False
sample = []
errcount = 0
sibling = None
server = False
metricgroup = []
def compute(self, EV):
try:
self.val = EV("TOPDOWN_BAD_SPECULATION.MISPREDICT", 2) / SLOTS(self, EV, 2)
self.thresh = (self.val > 0.05)
except ZeroDivisionError:
handle_error(self, "Branch_Mispredicts zero division")
return self.val
desc = """
Counts the number of issue slots that were not consumed by
the backend due to branch mispredicts."""
class Machine_Clears:
name = "Machine_Clears"
domain = "Slots"
area = "BAD"
level = 2
htoff = False
sample = []
errcount = 0
sibling = None
server = False
metricgroup = []
def compute(self, EV):
try:
self.val = EV("TOPDOWN_BAD_SPECULATION.MACHINE_CLEARS", 2) / SLOTS(self, EV, 2)
self.thresh = (self.val > 0.05)
except ZeroDivisionError:
handle_error(self, "Machine_Clears zero division")
return self.val
desc = """
Counts the total number of issue slots that were not
consumed by the backend because allocation is stalled due to
a machine clear (nuke) of any kind including memory ordering
and memory disambiguation."""
class Nuke:
name = "Nuke"
domain = "Slots"
area = "BAD"
level = 3
htoff = False
sample = []
errcount = 0
sibling = None
server = False
metricgroup = []
def compute(self, EV):
try:
self.val = EV("TOPDOWN_BAD_SPECULATION.NUKE", 3) / SLOTS(self, EV, 3)
self.thresh = (self.val > 0.05)
except ZeroDivisionError:
handle_error(self, "Nuke zero division")
return self.val
desc = """
Counts the number of issue slots that were not consumed by
the backend due to a machine clear (slow nuke)."""
class SMC:
name = "SMC"
domain = "Count"
area = "BAD"
level = 4
htoff = False
sample = []
errcount = 0
sibling = None
server = False
metricgroup = []
def compute(self, EV):
try:
self.val = self.Nuke.compute(EV) * (EV("MACHINE_CLEARS.SMC", 4) / EV("MACHINE_CLEARS.SLOW", 4))
self.thresh = (self.val > 0.02)
except ZeroDivisionError:
handle_error(self, "SMC zero division")
return self.val
desc = """
Counts the number of machine clears relative to the number
of nuke slots due to SMC."""
class Memory_Ordering:
name = "Memory_Ordering"
domain = "Count"
area = "BAD"
level = 4
htoff = False
sample = []
errcount = 0
sibling = None
server = False
metricgroup = []
def compute(self, EV):
try:
self.val = self.Nuke.compute(EV) * (EV("MACHINE_CLEARS.MEMORY_ORDERING", 4) / EV("MACHINE_CLEARS.SLOW", 4))
self.thresh = (self.val > 0.02)
except ZeroDivisionError:
handle_error(self, "Memory_Ordering zero division")
return self.val
desc = """
Counts the number of machine clears relative to the number
of nuke slots due to memory ordering."""
class FP_Assist:
name = "FP_Assist"
domain = "Count"
area = "BAD"
level = 4
htoff = False
sample = []
errcount = 0
sibling = None
server = False
metricgroup = []
def compute(self, EV):
try:
self.val = self.Nuke.compute(EV) * (EV("MACHINE_CLEARS.FP_ASSIST", 4) / EV("MACHINE_CLEARS.SLOW", 4))
self.thresh = (self.val > 0.02)
except ZeroDivisionError:
handle_error(self, "FP_Assist zero division")
return self.val
desc = """
Counts the number of machine clears relative to the number
of nuke slots due to FP assists."""
class Disambiguation:
name = "Disambiguation"
domain = "Count"
area = "BAD"
level = 4
htoff = False
sample = []
errcount = 0
sibling = None
server = False
metricgroup = []
def compute(self, EV):
try:
self.val = self.Nuke.compute(EV) * (EV("MACHINE_CLEARS.DISAMBIGUATION", 4) / EV("MACHINE_CLEARS.SLOW", 4))
self.thresh = (self.val > 0.02)
except ZeroDivisionError:
handle_error(self, "Disambiguation zero division")
return self.val
desc = """
Counts the number of machine clears relative to the number
of nuke slots due to memory disambiguation."""
class Page_Fault:
name = "Page_Fault"
domain = "Count"
area = "BAD"
level = 4
htoff = False
sample = []
errcount = 0
sibling = None
server = False
metricgroup = []
def compute(self, EV):
try:
self.val = self.Nuke.compute(EV) * (EV("MACHINE_CLEARS.PAGE_FAULT", 4) / EV("MACHINE_CLEARS.SLOW", 4))
self.thresh = (self.val > 0.02)
except ZeroDivisionError:
handle_error(self, "Page_Fault zero division")
return self.val
desc = """
Counts the number of machine clears relative to the number
of nuke slots due to page faults."""
class Fast_Nuke:
name = "Fast_Nuke"
domain = "Slots"
area = "BAD"
level = 3
htoff = False
sample = []
errcount = 0
sibling = None
server = False
metricgroup = []
def compute(self, EV):
try:
self.val = EV("TOPDOWN_BAD_SPECULATION.FASTNUKE", 3) / SLOTS(self, EV, 3)
self.thresh = (self.val > 0.05)
except ZeroDivisionError:
handle_error(self, "Fast_Nuke zero division")
return self.val
desc = """
Counts the number of issue slots that were not consumed by
the backend due to a machine clear classified as a fast nuke
due to memory ordering, memory disambiguation and memory
renaming."""
class Backend_Bound:
name = "Backend_Bound"
domain = "Slots"
area = "BE"
level = 1
htoff = False
sample = []
errcount = 0
sibling = None
server = False
metricgroup = []
def compute(self, EV):
try:
self.val = EV("TOPDOWN_BE_BOUND.ALL", 1) / SLOTS(self, EV, 1)
self.thresh = (self.val > 0.10)
except ZeroDivisionError:
handle_error(self, "Backend_Bound zero division")
return self.val
desc = """
Counts the total number of issue slots that were not
consumed by the backend due to backend stalls. Note that
uops must be available for consumption in order for this
event to count. If a uop is not available (IQ is empty),
this event will not count. The rest of these subevents
count backend stalls, in cycles, due to an outstanding
request which is memory bound vs core bound. The subevents
are not slot based events and therefore can not be precisely
added or subtracted from the Backend_Bound_Aux subevents
which are slot based."""
class Core_Bound:
name = "Core_Bound"
domain = "Cycles"
area = "BE"
level = 2
htoff = False
sample = []
errcount = 0
sibling = None
server = False
metricgroup = []
def compute(self, EV):
try:
self.val = max(0 , self.Backend_Bound.compute(EV) - self.Load_Store_Bound.compute(EV))
self.thresh = (self.val > 0.10)
except ZeroDivisionError:
handle_error(self, "Core_Bound zero division")
return self.val
desc = """
Counts the number of cycles due to backend bound stalls that
are core execution bound and not attributed to outstanding
demand load or store stalls."""
class Load_Store_Bound:
name = "Load_Store_Bound"
domain = "Cycles"
area = "BE"
level = 2
htoff = False
sample = []
errcount = 0
sibling = None
server = False
metricgroup = []
def compute(self, EV):
try:
self.val = min((EV("TOPDOWN_BE_BOUND.ALL", 2) / SLOTS(self, EV, 2)) , (EV("LD_HEAD.ANY_AT_RET", 2) / CLKS(self, EV, 2)) + self.Store_Bound.compute(EV))
self.thresh = (self.val > 0.20)
except ZeroDivisionError:
handle_error(self, "Load_Store_Bound zero division")
return self.val
desc = """
Counts the number of cycles the core is stalled due to
stores or loads."""
class Store_Bound:
name = "Store_Bound"
domain = "Cycles"
area = "BE"
level = 3
htoff = False
sample = []
errcount = 0
sibling = None
server = False
metricgroup = []
def compute(self, EV):
try:
self.val = self.Mem_Scheduler.compute(EV) * (EV("MEM_SCHEDULER_BLOCK.ST_BUF", 3) / EV("MEM_SCHEDULER_BLOCK.ALL", 3))
self.thresh = (self.val > 0.10)
except ZeroDivisionError:
handle_error(self, "Store_Bound zero division")
return self.val
desc = """
Counts the number of cycles the core is stalled due to store
buffer full."""
class L1_Bound:
name = "L1_Bound"
domain = "Cycles"
area = "BE"
level = 3
htoff = False
sample = []
errcount = 0
sibling = None
server = False
metricgroup = []
def compute(self, EV):
try:
self.val = EV("LD_HEAD.L1_BOUND_AT_RET", 3) / CLKS(self, EV, 3)
self.thresh = (self.val > 0.10)
except ZeroDivisionError:
handle_error(self, "L1_Bound zero division")
return self.val
desc = """
Counts the number of cycles that the oldest load of the load
buffer is stalled at retirement due to a load block."""
class Store_Fwd:
name = "Store_Fwd"
domain = "Cycles"
area = "BE"
level = 4
htoff = False
sample = []
errcount = 0
sibling = None
server = False
metricgroup = []
def compute(self, EV):
try:
self.val = EV("LD_HEAD.ST_ADDR_AT_RET", 4) / CLKS(self, EV, 4)
self.thresh = (self.val > 0.05)
except ZeroDivisionError:
handle_error(self, "Store_Fwd zero division")
return self.val
desc = """
Counts the number of cycles that the oldest load of the load
buffer is stalled at retirement due to a store forward
block."""
class STLB_Hit:
name = "STLB_Hit"
domain = "Cycles"
area = "BE"
level = 4
htoff = False
sample = []
errcount = 0
sibling = None
server = False
metricgroup = []
def compute(self, EV):
try:
self.val = EV("LD_HEAD.DTLB_MISS_AT_RET", 4) / CLKS(self, EV, 4)
self.thresh = (self.val > 0.05)
except ZeroDivisionError:
handle_error(self, "STLB_Hit zero division")
return self.val
desc = """
Counts the number of cycles that the oldest load of the load
buffer is stalled at retirement due to a first level TLB
miss."""
class STLB_Miss:
name = "STLB_Miss"
domain = "Cycles"
area = "BE"
level = 4
htoff = False
sample = []
errcount = 0
sibling = None
server = False
metricgroup = []
def compute(self, EV):
try:
self.val = EV("LD_HEAD.PGWALK_AT_RET", 4) / CLKS(self, EV, 4)
self.thresh = (self.val > 0.05)
except ZeroDivisionError:
handle_error(self, "STLB_Miss zero division")
return self.val
desc = """
Counts the number of cycles that the oldest load of the load
buffer is stalled at retirement due to a second level TLB
miss requiring a page walk."""
class Other_L1:
name = "Other_L1"
domain = "Cycles"
area = "BE"
level = 4
htoff = False
sample = []
errcount = 0
sibling = None
server = False
metricgroup = []
def compute(self, EV):
try:
self.val = EV("LD_HEAD.OTHER_AT_RET", 4) / CLKS(self, EV, 4)
self.thresh = (self.val > 0.05)
except ZeroDivisionError:
handle_error(self, "Other_L1 zero division")
return self.val
desc = """
Counts the number of cycles that the oldest load of the load
buffer is stalled at retirement due to a number of other
load blocks."""
class L2_Bound:
name = "L2_Bound"
domain = "Cycles"
area = "BE"
level = 3
htoff = False
sample = []
errcount = 0
sibling = None
server = False
metricgroup = []
def compute(self, EV):
try:
self.val = EV("MEM_BOUND_STALLS.LOAD_L2_HIT", 3) / CLKS(self, EV, 3)
self.thresh = (self.val > 0.10)
except ZeroDivisionError:
handle_error(self, "L2_Bound zero division")
return self.val
desc = """
Counts the number of cycles a core is stalled due to a
demand load which hit in the L2 Cache."""
class L3_Bound:
name = "L3_Bound"
domain = "Cycles"
area = "BE"
level = 3
htoff = False
sample = []
errcount = 0
sibling = None
server = False
metricgroup = []
def compute(self, EV):
try:
self.val = EV("MEM_BOUND_STALLS.LOAD_LLC_HIT", 3) / CLKS(self, EV, 3)
self.thresh = (self.val > 0.10)
except ZeroDivisionError:
handle_error(self, "L3_Bound zero division")
return self.val
desc = """
Counts the number of cycles a core is stalled due to a
demand load which hit in the Last Level Cache (LLC) or other
core with HITE/F/M."""
class DRAM_Bound:
name = "DRAM_Bound"
domain = "Cycles"
area = "BE"
level = 3
htoff = False
sample = []
errcount = 0
sibling = None
server = False
metricgroup = []
def compute(self, EV):
try:
self.val = EV("MEM_BOUND_STALLS.LOAD_DRAM_HIT", 3) / CLKS(self, EV, 3)
self.thresh = (self.val > 0.10)
except ZeroDivisionError:
handle_error(self, "DRAM_Bound zero division")
return self.val
desc = """
Counts the number of cycles the core is stalled due to a
demand load miss which hit in DRAM or MMIO (Non-DRAM)."""
class Backend_Bound_Aux:
name = "Backend_Bound_Aux"
domain = "Slots"
area = "BE_aux"
level = 1
htoff = False
sample = []
errcount = 0
sibling = None
server = False
metricgroup = []
def compute(self, EV):
try:
self.val = self.Backend_Bound.compute(EV)
self.thresh = (self.val > 0.20)
except ZeroDivisionError:
handle_error(self, "Backend_Bound_Aux zero division")
return self.val
desc = """
Counts the total number of issue slots that were not
consumed by the backend due to backend stalls. Note that
UOPS must be available for consumption in order for this
event to count. If a uop is not available (IQ is empty),
this event will not count. All of these subevents count
backend stalls, in slots, due to a resource limitation.
These are not cycle based events and therefore can not be
precisely added or subtracted from the Backend_Bound
subevents which are cycle based. These subevents are
supplementary to Backend_Bound and can be used to analyze
results from a resource perspective at allocation."""