Package: pylikwid
pylikwidPython interface for the C API of LIKWID
(https://github.com/RRZE-HPC/likwid)InstallationI added a setup.py script for the installation. It builds the C module
and copies it to the proper destination.$ git clone https://github.com/RRZE-HPC/pylikwid.git
$ cd pylikwid
# Build C interface
$ python setup.py build_ext -I <include path for likwid> -L <library path for likwid> -R <library path for likwid>
# Install module to the proper location
$ python setup.py install (--prefix=<where to install>)
# Testing
$ python -c "import pylikwid"
$ ./testlib.pyFunctionsAfterimport pylikwidyou can call the following functions:Marker APIpylikwid.markerinit(): Initialize the Marker API of the LIKWID library.
Must be called previous to all other functions.pylikwid.markerthreadinit(): Add the current thread to the Marker API.
Since Python is commonly single-threaded simply call it directly
afterpylikwid.markerinit()rr = pylikwid.registerregion(regiontag): Register a region to the
Marker API. This is an optional function to reduce the overhead of
region registration atpylikwid.markerstartregion. If you don’t callpylikwid.registerregion(regiontag), the registration is done atpylikwid.markerstartregion(regiontag). On success, 0 is return. If you
havn’t calledpylikwid.markerinit(), a negative number is returned.err = pylikwid.markerstartregion(regiontag): Start measurements under
the nameregiontag. On success, 0 is return. If you havn’t calledpylikwid.markerinit(), a negative number is returned.err = pylikwid.markerstopregion(regiontag): Stop measurements under the
nameregiontagagain. On success, 0 is return. If you havn’t
calledpylikwid.markerinit(), a negative number is returned.num_events,events[],time, count = pylikwid.markergetregion(regiontag):
Get the intermediate results of the region identified byregiontag. On success, it returns the number of events in the
current group, a list with all the aggregated event results, the
measurement time for the region and the number of calls.pylikwid.nextgroup(): Switch to the next event set in a
round-robin fashion. If you have set only one event set on the
command line, this function performs no operation.pylikwid.markerreset(regiontag): Reset the values stored using the region
nameregiontag. On success, 0 is returned.pylikwid.markerclose(): Close the connection to the LIKWID Marker API
and write out measurement data to file. This file will be evaluated
bylikwid-perfctr.pylikwid.getprocessorid(): Returns the ID of the currently
executing CPUpylikwid.pinprocess(cpuid): Pins the current process to the CPU
given ascpuid.pylikwid.pinthread(cpuid): Pins the current thread to the CPU
given ascpuid.Topologypylikwid.inittopology(): Initialize the topology module (reads in
system topology)infodict = pylikwid.getcpuinfo(): Return a dict with general
information about the system (CPU model, CPU family, …)osname: Name of the CPU retrieved from the CPUID leafsname: Name of the micro architectureshort_name: Short name of the micro architecturefamily: ID of the CPU familymodel: Vendor-specific model number of the CPUstepping: Stepping (Revision) of the CPUperf_version: Version number of the hardware performance
monitoring capabilitiesperf_num_ctr: Amount of general-purpose counter registers per
hardware threadperf_num_fixed_ctr: Amount of fixed-purpose counter registers
per hardware threadperf_width_ctr: Bit length of the counter registersclock: CPU clock (only unequal to 0 if timer module is
initialized)turbo: Is turbo mode supported?isIntel: Is it an Intel CPU?supportUncore: Does the system have performance monitoring
counters in the Uncore?features: String with performance relevant CPU features (AVX,
SSE, …)featureFlags: Bitmask for all available CPU featurestopodict = pylikwid.getcputopology(): Return a dict with the
topology of the system. Here is a list of fields in the dict:numSockets: Number of CPU socketsnumHWThreads: Number of hardware threads (physical +
hyperthreading cores)activeHWThreads: Number of active/usable hardware threadsnumCoresPerSocket: Amount of hardware threads per CPU socketnumThreadsPerCore: Amount of hardware threads assembled in
every physical CPU corenumCacheLevels: Amount of levels in cacheing hierarchycacheLevels: Dict with information about the cache levels,
keys are the levels (1, 2, 3,…)level: Level of the cache in the hierarchylineSize: Size of a cache linesets: Amount of setsinclusive: Is the cache inclusive or exclusive?`threads: Amount of threads attached to the cacheassociativity: Associativity of the cachetype: data (= data cache), unified = (data + instruction
cache)size: Size of the cache in bytesthreadPool: Dict with information about the hardware threads.
Keys are the os-generated ID of the hardware threadcoreId: ID of the corresponding physical coreapicId: ID set by the operating systemthreadId: ID of the hardware thread in the physical corepackageId: ID of the CPU socket hosting the hardware threadpylikwid.printsupportedcpus(): Prints all supported micro
architecture names to stdoutpylikwid.finalizetopology(): Delete all information in the
topology moduleNUMAnumadict = pylikwid.initnuma(): Initialize the NUMA module and
return the gathered valuesnumberOfNodes: Amount of NUMA nodes in the systemnodes: Dict holding the information about the NUMA domains.
Keys are the NUMA domain IDsid: ID of the NUMA domain (should be equal to dict key)numberOfProcessors: Number of hardware threads attached to
the NUMA domainprocessors: List of all CPU IDs attached to the NUMA domainfreeMemory: Amount of free memory in the NUMA domain (in
Kbytes)totalMemory: Amount of total memory in the NUMA domain (in
Kbytes)numberOfDistances: How many distances to self/other NUMA
domainsdistances: List with distances, NUMA domain IDs are the
destination indexes in the listpylikwid.finalizenuma(): Delete all information in the NUMA
moduleAffinityaffdict = pylikwid.initaffinity(): Initialize the affinity domain
module and return the gathered valuesnumberOfAffinityDomains: Amount of affinity domainsnumberOfSocketDomains: Amount of CPU socket related affinity
domainsnumberOfNumaDomains: Amount of NUMA related affinity domainsnumberOfCacheDomains: Amount of last level cache related
affinity domainsnumberOfProcessorsPerSocket: Amount of hardware threads per
CPU socketnumberOfCoresPerCache: Amount of physical CPU cores per last
level cachenumberOfProcessorsPerCache: Amount of hardware threads per
last level cachedomains: Dict holding the information about the affinity
domainstag: Name of the affinity domain (N = node, SX = socket X,
CY = cache Y, MZ = memory domain Z)numberOfProcessors: Amount of hardware threads in the
domainnumberOfCores: Amount of physical CPU cores in the domainprocessorList: List holding the CPU IDs in the domainpylikwid.finalizeaffinity(): Delete all information in the
affinity domain modulepylikwid.cpustr_to_cpulist(): Transform a valid cpu string in
LIKWID syntax into a list of CPU IDsTimerpylikwid.getcpuclock(): Return the CPU clockt_start = pylikwid.startclock(): Start the clock and return the
current timestampt_end = pylikwid.stopclock(): Stop the clock and return the
current timestampt = pylikwid.getclock(t_start, t_end): Return the time in seconds
betweent_startandt_endc = pylikwid.getclockcycles(t_start, t_end): Return the amount of
CPU cycles betweent_startandt_endTemperaturepylikwid.inittemp(cpu): Initialize the temperature module for CPUcpupylikwid.readtemp(cpu): Read the current temperature of CPUcpuEnergypinfo = pylikwid.getpowerinfo(): Initializes the energy module
and returns gathered information. If it returnsNone, there is no
energy supportminFrequency: Minimal possible frequency of a CPU corebaseFrequency: Base frequency of a CPU corehasRAPL: Are energy reading supported?timeUnit: Time unitpowerUnit: Power unitdomains: Dict holding the information about the energy
domains. Keys are PKG, PP0, PP1, DRAMID: ID of the energy domainenergyUnit: Unit to derive raw register counts to uJsupportInfo: Is the information register available?tdp: TDP of the domain (only if supportInfo == True)minPower: Minimal power consumption by the domain (only if
supportInfo == True)maxPower: Maximal power consumption by the domain (only if
supportInfo == True)maxTimeWindow: Maximal time window between updates of the
energy registerssupportStatus: Are energy readings from the domain are
possible?supportPerf: Is power capping etc. available?supportPolicy: Can we set a power policy for the domain?e_start = pylikwid.startpower(cpu, domainid): Return the start
value for a cpu for the domain withdomainid. Thedomainidcan be found inpinfo["domains"][domainname]["ID"]e_stop = pylikwid.stoppower(cpu, domainid): Return the stop value
for a cpu for the domain withdomainid. Thedomainidcan be
found inpinfo["domains"][domainname]["ID"]e = pylikwid.getpower(e_start, e_stop, domainid): Calculate the
uJ from the values retrieved bystartpowerandstoppower.Configurationpylikwid.initconfiguration(): Read in config file from different
places. Default is/etc/likwid.cfgconfig = pylikwid.getconfiguration(): Get the dict with the
configuration optionsconfigFileName: Path to the config filetopologyCfgFileName: If a topology file was created withlikwid-genTopoCfgand found byinitconfiguration()daemonPath: Path to the access daemon executablegroupPath: Path to the base directory with the performance
group filesdaemonMode: Configured access mode (0=direct, 1=accessDaemon)maxNumThreads: Maximal amount of hardware threads that can be
handled by LIKWIDmaxNumNodes: Maximal amount of CPU sockets that can be handled
by LIKWIDpylikwid.destroyconfiguration(): Destroy all information about
the configurationAccess modulepylikwid.hpmmode(mode): Set access mode. For x86 there are two
modes:mode = 0: Access the MSR and PCI devices directly. May require
root accessmode = 1: Access the MSR and PCI devices through access daemon
instancespylikwid.hpminit(): Initialize the access functions according to
the access modepylikwid.hpmaddthread(cpu): Add CPUcputo the access layer
(opens devices files or connection to an access daemon)pylikwid.hpmfinalize(): Unregister all CPUs from the access layer
and close files/connectionsPerformance Monitoringpylikwid.init(cpus): Initialize the perfmon module for the CPUs
given in listcpuspylikwid.getnumberofthreads(): Return the number of threads
initialized in the perfmon modulepylikwid.getnumberofgroups(): Return the number of groups
currently registered in the perfmon modulepylikwid.getgroups(): Return a list of all available groups. Each
list entry is a dict:Name: Name of the performance groupShort: Short information about the performance groupLong: Long description of the performance groupgid = pylikwid.addeventset(estr): Add a performance group or a
custom event set to the perfmon module. Thegidis required to
specify the event set laterpylikwid.getnameofgroup(gid): Return the name of the group
identified bygid. If it is a custom event set, the name is set
toCustompylikwid.getshortinfoofgroup(gid): Return the short information
about a performance grouppylikwid.getlonginfoofgroup(gid): Return the description of a
performance grouppylikwid.getnumberofevents(gid): Return the amount of events in
the grouppylikwid.getnumberofmetrics(gid): Return the amount of derived
metrics in the group. Always 0 for custom event sets.pylikwid.getnameofevent(gid, eidx): Return the name of the event
identified bygidand the index in the list of eventspylikwid.getnameofcounter(gid, eidx): Return the name of the
counter register identified bygidand the index in the list of
eventspylikwid.getnameofmetric(gid, midx): Return the name of a derived
metric identified bygidand the index in the list of metricspylikwid.setup(gid): Program the counter registers to measure all
events in groupgidpylikwid.start(): Start the counter registerspylikwid.stop(): Stop the counter registerspylikwid.read(): Read the counter registers (stop->read->start)pylikwid.switch(gid): Switch to groupgid(stop->setup(gid)->start)pylikwid.getidofactivegroup()Return thegidof the currently
configured grouppylikwid.getresult(gid, eidx, tidx): Return the raw counter
register result of all measurements identified by groupgidand
the indices for eventeidxand threadtidxpylikwid.getlastresult(gid, eidx, tidx): Return the raw counter
register result of the last measurement cycle identified by groupgidand the indices for eventeidxand threadtidxpylikwid.getmetric(gid, midx, tidx): Return the derived metric
result of all measurements identified by groupgidand the
indices for metricmidxand threadtidxpylikwid.getlastmetric(gid, midx, tidx): Return the derived
metric result of the last measurement cycle identified by groupgidand the indices for metricmidxand threadtidxpylikwid.gettimeofgroup(gid): Return the measurement time for
group identified bygidpylikwid.finalize(): Reset all used registers and delete internal
measurement resultsMarker API result file readerpylikwid.markerreadfile(filename): Reads in the result file of an
application run instrumented by the LIKWID Marker APIpylikwid.markernumregions(): Return the number of regions in an
application runpylikwid.markerregiontag(rid): Return the region tag for the
region identified byridpylikwid.markerregiongroup(rid): Return the group name for the
region identified byridpylikwid.markerregionevents(rid): Return the amount of events for
the region identified byridpylikwid.markerregionthreads(rid): Return the amount of threads
that executed the region identified byridpylikwid.markerregiontime(rid, tidx): Return the accumulated
measurement time for the region identified byridand the thread
indextidxpylikwid.markerregioncount(rid, tidx): Return the call count for
the region identified byridand the thread indextidxpylikwid.markerregionresult(rid, eidx, tidx): Return the call
count for the region identified byrid, the event indexeidxand the thread indextidxpylikwid.markerregionmetric(rid, midx, tidx): Return the call
count for the region identified byrid, the metric indexmidxand the thread indextidxGPU Topology (if LIKWID is built with Nvidia interface)pylikwid.initgputopology(): Initialize the topology module (reads in
system topology)topolist = pylikwid.getgputopology(): Return a list with the
GPU topology of the system. Each GPU is represented by a dict. The entries in
the dicts are:devid: Device identifier for the GPUnumaNode: The NUMA node identifier the GPU is attached atname: Name of the devicemem: Memory capacity of the deviceccapMajor: Major number of the compute capabilityccapMinor: Minor number of the compute capabilitymaxThreadsDim[3]: Maximum sizes of each dimension of a blockmaxGridSize[3]: Maximum sizes of each dimension of a gridmaxThreadsPerBlock: Maximam number of thread per blocksharedMemPerBlock: Total amount of shared memory available per blocktotalConstantMemory: Total amount of constant memory available on the devicesimdWidth: SIMD width of arithmetic units = warp sizememPitch: Maximum pitch allowed by the memory copy functions that involve memory regions allocated through cuMemAllocPitch()regsPerBlock: Total number of registers available per blockclockRatekHz: Clock frequency in kilohertztextureAlign: Alignment requirementsurfaceAlign: Alignment requirement for surfacesl2Size: L2 cache in bytes. 0 if the device doesn’t have L2 cachememClockRatekHz: Peak memory clock frequency in kilohertzpciBus: PCI bus identifier of the devicepciDev: PCI device (also known as slot) identifier of the devicepciDom: PCI domain identifier of the devicemaxBlockRegs: Maximum number of 32-bit registers available to a thread blocknumMultiProcs: Number of multiprocessors on the devicemaxThreadPerMultiProc: Maximum resident threads per multiprocessormemBusWidth: Global memory bus width in bitsunifiedAddrSpace: 1 if the device shares a unified address space with the host, or 0 if notecc: 1 if error correction is enabled on the device, 0 if error correction is disabled or not supported by the deviceasyncEngines: Number of asynchronous enginesmapHostMem: 1 if the device can map host memory into the CUDA address spaceintegrated: 1 if the device is an integrated (motherboard) GPU and 0 if it is a discrete (card) componentpylikwid.finalizegputopology(): Delete all information in the
topology modulePerformance Monitoring for Nvidia GPUs (if LIKWID is built with Nvidia interface)pylikwid.nvinit(gpus): Initialize the nvmon module for the GPUs
given in listgpuspylikwid.nvgetnumberofgpus(): Return the number of GPUs
initialized in the nvmon modulepylikwid.nvgetnumberofgroups(): Return the number of groups
currently registered in the nvmon modulepylikwid.nvgetgroups(): Return a list of all available groups. Each
list entry is a dict:Name: Name of the performance groupShort: Short information about the performance groupLong: Long description of the performance groupgid = pylikwid.nvaddeventset(estr): Add a performance group or a
custom event set to the perfmon module. Thegidis required to
specify the event set laterpylikwid.nvgetnameofgroup(gid): Return the name of the group
identified bygid. If it is a custom event set, the name is set
toCustompylikwid.nvgetshortinfoofgroup(gid): Return the short information
about a performance grouppylikwid.nvgetlonginfoofgroup(gid): Return the description of a
performance grouppylikwid.nvgetnumberofevents(gid): Return the amount of events in
the grouppylikwid.nvgetnumberofmetrics(gid): Return the amount of derived
metrics in the group. Always 0 for custom event sets.pylikwid.nvgetnameofevent(gid, eidx): Return the name of the event
identified bygidand the index in the list of eventspylikwid.nvgetnameofcounter(gid, eidx): Return the name of the
counter register identified bygidand the index in the list of
eventspylikwid.nvgetnameofmetric(gid, midx): Return the name of a derived
metric identified bygidand the index in the list of metricspylikwid.nvsetup(gid): Program the counter registers to measure all
events in groupgidpylikwid.nvstart(): Start the counter registerspylikwid.nvstop(): Stop the counter registerspylikwid.nvread(): Read the counter registers (stop->read->start)pylikwid.nvswitch(gid): Switch to groupgid(stop->setup(gid)->start)pylikwid.nvgetidofactivegroup()Return thegidof the currently
configured grouppylikwid.nvgetresult(gid, eidx, tidx): Return the raw counter
register result of all measurements identified by groupgidand
the indices for eventeidxand threadtidxpylikwid.nvgetlastresult(gid, eidx, tidx): Return the raw counter
register result of the last measurement cycle identified by groupgidand the indices for eventeidxand threadtidxpylikwid.nvgetmetric(gid, midx, tidx): Return the derived metric
result of all measurements identified by groupgidand the
indices for metricmidxand threadtidxpylikwid.nvgetlastmetric(gid, midx, tidx): Return the derived
metric result of the last measurement cycle identified by groupgidand the indices for metricmidxand threadtidxpylikwid.nvgettimeofgroup(gid): Return the measurement time for
group identified bygidpylikwid.nvfinalize(): Reset all used registers and delete internal
measurement resultsNvmon Marker API (if LIKWID is built with Nvidia interface)pylikwid.gpumarkerinit(): Initialize the Nvmon Marker API of the LIKWID library.
Must be called previous to all other functions.rr = pylikwid.gpuregisterregion(regiontag): Register a region to the
Nvmon Marker API. This is an optional function to reduce the overhead of
region registration atpylikwid.markerstartregion. If you don’t callpylikwid.gpumarkerregisterregion(regiontag), the registration is done atpylikwid.gpumarkerstartregion(regiontag). On success, 0 is return. If you
havn’t calledpylikwid.gpumarkerinit(), a negative number is returned.err = pylikwid.gpumarkerstartregion(regiontag): Start measurements under
the nameregiontag. On success, 0 is return. If you havn’t calledpylikwid.gpumarkerinit(), a negative number is returned.err = pylikwid.gpumarkerstopregion(regiontag): Stop measurements under the
nameregiontagagain. On success, 0 is return. If you havn’t
calledpylikwid.gpumarkerinit(), a negative number is returned.num_gpus, num_events,events[][],time[],count[] = pylikwid.gpumarkergetregion(regiontag):
Get the intermediate results of the region identified byregiontag. On success, it returns the number of events in the
current group, a list with all the aggregated event results per GPU, the
measurement time for the region and the number of calls.pylikwid.gpunextgroup(): Switch to the next event set in a
round-robin fashion. If you have set only one event set on the
command line, this function performs no operation.pylikwid.gpumarkerreset(regiontag): Reset the values stored using the region
nameregiontag. On success, 0 is returned.pylikwid.gpumarkerclose(): Close the connection to the LIKWID Nvmon Marker API
and write out measurement data to file. This file will be evaluated
bylikwid-perfctr.UsageMarker APICodeHere is a small example Python script how to use the LIKWID Marker API
in Python:#!/usr/bin/env python

import pylikwid

pylikwid.markerinit()
pylikwid.markerthreadinit()
liste = []
pylikwid.markerstartregion("listappend")
for i in range(0,1000000):
    liste.append(i)
pylikwid.markerstopregion("listappend")
nr_events, eventlist, time, count = pylikwid.markergetregion("listappend")
for i, e in enumerate(eventlist):
    print(i, e)
pylikwid.markerclose()This code simply measures the hardware performance counters for
appending 1000000 elements to a list. First the API is initialized withlikwid.init()andlikwid.threadinit(). Afterwards it creates an
empty list, starts the measurements withlikwid.startregion("listappend")and executes the appending loop.
When the loop has finished, we stop the measurements again usinglikwid.stopregion("listappend"). Just for the example, we get the
values inside our script usinglikwid.getregion("listappend")and
print out the results. Finally, we close the connection to the LIKWID
Marker API.You always have to uselikwid-perfctrto program the hardware
performance counters and specify the CPUs that should be measured. Since
Python is commonly single-threaded, the cpu set only contains one entry:likwid-perfctr-C0-g<EVENTSET>-m<PYTHONSCRIPT>This pins the
Python interpreter to CPU 0 and measures<EVENTSET>for all regions
in the Python script. You can set multiple event sets by adding multiple-g<EVENTSET>to the command line. Please see the LIKWID page for
further information how to uselikwid-perfctr. Link:https://github.com/rrze-likwid/likwidExampleUsing the above Python script we can measure the L2 to L3 cache data
volume:$ likwid-perfctr -C 0 -g L3 -m ./test.py
--------------------------------------------------------------------------------
CPU name:   Intel(R) Core(TM) i7-4770 CPU @ 3.40GHz
CPU type:   Intel Core Haswell processor
CPU clock:  3.39 GHz
--------------------------------------------------------------------------------
(0, 926208305.0)
(1, 325539316.0)
(2, 284626172.0)
(3, 1219118.0)
(4, 918368.0)
Wrote LIKWID Marker API output to file /tmp/likwid_17275.txt
--------------------------------------------------------------------------------
================================================================================
Group 1 L3: Region listappend
================================================================================
+-------------------+----------+
|    Region Info    |  Core 0  |
+-------------------+----------+
| RDTSC Runtime [s] | 0.091028 |
|     call count    |     1    |
+-------------------+----------+

+-----------------------+---------+--------------+
|         Event         | Counter |    Core 0    |
+-----------------------+---------+--------------+
|   INSTR_RETIRED_ANY   |  FIXC0  | 9.262083e+08 |
| CPU_CLK_UNHALTED_CORE |  FIXC1  | 3.255393e+08 |
|  CPU_CLK_UNHALTED_REF |  FIXC2  | 2.846262e+08 |
|    L2_LINES_IN_ALL    |   PMC0  | 1.219118e+06 |
|     L2_TRANS_L2_WB    |   PMC1  | 9.183680e+05 |
+-----------------------+---------+--------------+

+-------------------------------+--------------+
|             Metric            |    Core 0    |
+-------------------------------+--------------+
|      Runtime (RDTSC) [s]      |  0.09102752  |
|      Runtime unhalted [s]     | 9.596737e-02 |
|          Clock [MHz]          | 3.879792e+03 |
|              CPI              | 3.514753e-01 |
|  L3 load bandwidth [MBytes/s] | 8.571425e+02 |
|  L3 load data volume [GBytes] |  0.078023552 |
| L3 evict bandwidth [MBytes/s] | 6.456899e+02 |
| L3 evict data volume [GBytes] |  0.058775552 |
|    L3 bandwidth [MBytes/s]    | 1.502832e+03 |
|    L3 data volume [GBytes]    |  0.136799104 |
+-------------------------------+--------------+At first a header with the current system type and clock is printed.
Afterwards the output of the Python script lists the results of the
measurements we got internally withlikwid.getregion. The next
output is the region results evaluated bylikwid-perfctrand prints
at first a headline stating the measured eventset, hereL3and the
region namelistappend. Afterwards 2 or 3 tables are printed. At
first some basic information about the region like run time (or better
measurement time) and the number of calls of the region. The next table
contains the raw values for each event in the eventset. These numbers
are similar to the ones we got internally withlikwid.getregion. If
you have set an performance group (hereL3) instead of a custom
event set, the raw results are derived to commonly used metrics, here
theCPI(Cycles per instruction, lower is better) and different
bandwidths and data volumes. You can see, that the load bandwidth for
the small loop is 857 MByte/s and the evict (write) bandwidth is 645
MByte/s. In total we have a bandwidth of 1502 MByte/s.Full APICode#!/usr/bin/env python

import pylikwid

liste = []
cpus = [0,1]

pylikwid.init(cpus)
group = pylikwid.addeventset("INSTR_RETIRED_ANY:FIXC0")
pylikwid.setup(group)
pylikwid.start()
for i in range(0,1000000):
    liste.append(i)
pylikwid.stop()
for thread in range(0,len(cpus)):
    print("Result CPU %d : %f" % (cpus[thread], pylikwid.getresult(group,0,thread)))
pylikwid.finalize()Example$ ./test.py
Result CPU 0 : 87335.000000
Result CPU 1 : 5222188.000000Further commentsPlease be aware that Python is a high-level language and your simple
code is translated to a lot of Assembly instructions. TheCPIvalue
is commonly low (=> good) for high-level languages because they have to
perform type-checking and similar stuff that can be executed fast in
comparison to the CPU clock. If you would compare the results to a lower
level language like C or Fortran, theCPIwill be worse for them but
the performance will be higher as no type-checking and transformations
need to be done.