pylikwid 0.2.4

A Python module to access the function of the LIKWID library

pylikwid

Python interface for the C API of LIKWID (https://github.com/RRZE-HPC/likwid)

Installation

I 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.py

Functions

After import pylikwid you can call the following functions:

Marker API

• pylikwid.init(): Initialize the Marker API of the LIKWID library. Must be called previous to all other functions.

• pylikwid.threadinit(): Add the current thread to the Marker API. Since Python is commonly single-threaded simply call it directly after pylikwid.init()

• rr = pylikwid.registerregion(regiontag): Register a region to the Marker API. This is an optional function to reduce the overhead of region registration at pylikwid.startregion. If you don’t call pylikwid.registerregion(regiontag), the registration is done at pylikwid.startregion(regiontag). On success, 0 is return. If you havn’t called pylikwid.init(), a negative number is returned.

• err = pylikwid.startregion(regiontag): Start measurements under the name regiontag. On success, 0 is return. If you havn’t called pylikwid.init(), a negative number is returned.

• err = pylikwid.stopregion(regiontag): Stop measurements under the name regiontag again. On success, 0 is return. If you havn’t called pylikwid.init(), a negative number is returned.

• num_events, events[], time, count = pylikwid.getregion(regiontag): Get the intermediate results of the region identified by regiontag. 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.close(): Close the connection to the LIKWID Marker API and write out measurement data to file. This file will be evaluated by likwid-perfctr.

• pylikwid.getprocessorid(): Returns the ID of the currently executing CPU

• pylikwid.pinprocess(cpuid): Pins the current process to the CPU given as cpuid.

• pylikwid.pinthread(cpuid): Pins the current thread to the CPU given as cpuid.

Topology

• pylikwid.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 leafs

  • name: Name of the micro architecture

  • short_name: Short name of the micro architecture

  • family: ID of the CPU family

  • model: Vendor-specific model number of the CPU

  • stepping: Stepping (Revision) of the CPU

  • perf_version: Version number of the hardware performance monitoring capabilities

  • perf_num_ctr: Amount of general-purpose counter registers per hardware thread

  • perf_num_fixed_ctr: Amount of fixed-purpose counter registers per hardware thread

  • perf_width_ctr: Bit length of the counter registers

  • clock: 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 features

• topodict = pylikwid.getcputopology(): Return a dict with the topology of the system. Here is a list of fields in the dict:

  • numSockets: Number of CPU sockets

  • numHWThreads: Number of hardware threads (physical + hyperthreading cores)

  • activeHWThreads: Number of active/usable hardware threads

  • numCoresPerSocket: Amount of hardware threads per CPU socket

  • numThreadsPerCore: Amount of hardware threads assembled in every physical CPU core

  • numCacheLevels: Amount of levels in cacheing hierarchy

  • cacheLevels: Dict with information about the cache levels, keys are the levels (1, 2, 3,…)

    • level: Level of the cache in the hierarchy

    • lineSize: Size of a cache line

    • sets: Amount of sets

    • inclusive: Is the cache inclusive or exclusive?`

    • threads: Amount of threads attached to the cache

    • associativity: Associativity of the cache

    • type: data (= data cache), unified = (data + instruction cache)

    • size: Size of the cache in bytes

  • threadPool: Dict with information about the hardware threads. Keys are the os-generated ID of the hardware thread

    • coreId: ID of the corresponding physical core

    • apicId: ID set by the operating system

    • threadId: ID of the hardware thread in the physical core

    • packageId: ID of the CPU socket hosting the hardware thread

• pylikwid.printsupportedcpus(): Prints all supported micro architecture names to stdout

• pylikwid.finalizetopology(): Delete all information in the topology module

NUMA

• numadict = pylikwid.initnuma(): Initialize the NUMA module and return the gathered values

  • numberOfNodes: Amount of NUMA nodes in the system

  • nodes: Dict holding the information about the NUMA domains. Keys are the NUMA domain IDs

    • id: ID of the NUMA domain (should be equal to dict key)

    • numberOfProcessors: Number of hardware threads attached to the NUMA domain

    • processors: List of all CPU IDs attached to the NUMA domain

    • freeMemory: 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 domains

    • distances: List with distances, NUMA domain IDs are the destination indexes in the list

• pylikwid.finalizenuma(): Delete all information in the NUMA module

Affinity

• affdict = pylikwid.initaffinity(): Initialize the affinity domain module and return the gathered values

  • numberOfAffinityDomains: Amount of affinity domains

  • numberOfSocketDomains: Amount of CPU socket related affinity domains

  • numberOfNumaDomains: Amount of NUMA related affinity domains

  • numberOfCacheDomains: Amount of last level cache related affinity domains

  • numberOfProcessorsPerSocket: Amount of hardware threads per CPU socket

  • numberOfCoresPerCache: Amount of physical CPU cores per last level cache

  • numberOfProcessorsPerCache: Amount of hardware threads per last level cache

  • domains: Dict holding the information about the affinity domains

    • tag: Name of the affinity domain (N = node, SX = socket X, CY = cache Y, MZ = memory domain Z)

    • numberOfProcessors: Amount of hardware threads in the domain

    • numberOfCores: Amount of physical CPU cores in the domain

    • processorList: List holding the CPU IDs in the domain

• pylikwid.finalizeaffinity(): Delete all information in the affinity domain module

• pylikwid.cpustr_to_cpulist(): Transform a valid cpu string in LIKWID syntax into a list of CPU IDs

Timer

• pylikwid.getcpuclock(): Return the CPU clock

• t_start = pylikwid.startclock(): Start the clock and return the current timestamp

• t_end = pylikwid.stopclock(): Stop the clock and return the current timestamp

• t = pylikwid.getclock(t_start, t_end): Return the time in seconds between t_start and t_end

• c = pylikwid.getclockcycles(t_start, t_end): Return the amount of CPU cycles between t_start and t_end

Temperature

• pylikwid.inittemp(cpu): Initialize the temperature module for CPU cpu

• pylikwid.readtemp(cpu): Read the current temperature of CPU cpu

Energy

• pinfo = pylikwid.getpowerinfo(): Initializes the energy module and returns gathered information. If it returns None, there is no energy support

  • minFrequency: Minimal possible frequency of a CPU core

  • baseFrequency: Base frequency of a CPU core

  • hasRAPL: Are energy reading supported?

  • timeUnit: Time unit

  • powerUnit: Power unit

  • domains: Dict holding the information about the energy domains. Keys are PKG, PP0, PP1, DRAM

    • ID: ID of the energy domain

    • energyUnit: Unit to derive raw register counts to uJ

    • supportInfo: 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 registers

    • supportStatus: 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 with domainid. The domainid can be found in pinfo["domains"][domainname]["ID"]

• e_stop = pylikwid.stoppower(cpu, domainid): Return the stop value for a cpu for the domain with domainid. The domainid can be found in pinfo["domains"][domainname]["ID"]

• e = pylikwid.getpower(e_start, e_stop, domainid): Calculate the uJ from the values retrieved by startpower and stoppower.

Configuration

• pylikwid.initconfiguration(): Read in config file from different places. Default is /etc/likwid.cfg

• config = pylikwid.getconfiguration(): Get the dict with the configuration options

  • configFileName: Path to the config file

  • topologyCfgFileName: If a topology file was created with likwid-genTopoCfg and found by initconfiguration()

  • daemonPath: Path to the access daemon executable

  • groupPath: Path to the base directory with the performance group files

  • daemonMode: Configured access mode (0=direct, 1=accessDaemon)

  • maxNumThreads: Maximal amount of hardware threads that can be handled by LIKWID

  • maxNumNodes: Maximal amount of CPU sockets that can be handled by LIKWID

• pylikwid.destroyconfiguration(): Destroy all information about the configuration

Access module

• pylikwid.hpmmode(mode): Set access mode. For x86 there are two modes:

  • mode = 0: Access the MSR and PCI devices directly. May require root access

  • mode = 1: Access the MSR and PCI devices through access daemon instances

• pylikwid.hpminit(): Initialize the access functions according to the access mode

• pylikwid.hpmaddthread(cpu): Add CPU cpu to the access layer (opens devices files or connection to an access daemon)

• pylikwid.hpmfinalize(): Unregister all CPUs from the access layer and close files/connections

Performance Monitoring

• pylikwid.init(cpus): Initialize the perfmon module for the CPUs given in list cpus

• pylikwid.getnumberofthreads(): Return the number of threads initialized in the perfmon module

• pylikwid.getnumberofgroups(): Return the number of groups currently registered in the perfmon module

• pylikwid.getgroups(): Return a list of all available groups. Each list entry is a dict:

  • Name: Name of the performance group

  • Short: Short information about the performance group

  • Long: Long description of the performance group

• gid = pylikwid.addeventset(estr): Add a performance group or a custom event set to the perfmon module. The gid is required to specify the event set later

• pylikwid.getnameofgroup(gid): Return the name of the group identified by gid. If it is a custom event set, the name is set to Custom

• pylikwid.getshortinfoofgroup(gid): Return the short information about a performance group

• pylikwid.getlonginfoofgroup(gid): Return the description of a performance group

• pylikwid.getnumberofevents(gid): Return the amount of events in the group

• pylikwid.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 by gid and the index in the list of events

• pylikwid.getnameofcounter(gid, eidx): Return the name of the counter register identified by gid and the index in the list of events

• pylikwid.getnameofmetric(gid, midx): Return the name of a derived metric identified by gid and the index in the list of metrics

• pylikwid.setup(gid): Program the counter registers to measure all events in group gid

• pylikwid.start(): Start the counter registers

• pylikwid.stop(): Stop the counter registers

• pylikwid.read(): Read the counter registers (stop->read->start)

• pylikwid.switch(gid): Switch to group gid (stop->setup(gid)->start)

• pylikwid.getidofactivegroup() Return the gid of the currently configured group

• pylikwid.getresult(gid, eidx, tidx): Return the raw counter register result of all measurements identified by group gid and the indices for event eidx and thread tidx

• pylikwid.getlastresult(gid, eidx, tidx): Return the raw counter register result of the last measurement cycle identified by group gid and the indices for event eidx and thread tidx

• pylikwid.getmetric(gid, midx, tidx): Return the derived metric result of all measurements identified by group gid and the indices for metric midx and thread tidx

• pylikwid.getlastmetric(gid, midx, tidx): Return the derived metric result of the last measurement cycle identified by group gid and the indices for metric midx and thread tidx

• pylikwid.gettimeofgroup(gid): Return the measurement time for group identified by gid

• pylikwid.finalize(): Reset all used registers and delete internal measurement results

Marker API result file reader

• pylikwid.markerreadfile(filename): Reads in the result file of an application run instrumented by the LIKWID Marker API

• pylikwid.markernumregions(): Return the number of regions in an application run

• pylikwid.markerregiontag(rid): Return the region tag for the region identified by rid

• pylikwid.markerregiongroup(rid): Return the group name for the region identified by rid

• pylikwid.markerregionevents(rid): Return the amount of events for the region identified by rid

• pylikwid.markerregionthreads(rid): Return the amount of threads that executed the region identified by rid

• pylikwid.markerregiontime(rid, tidx): Return the accumulated measurement time for the region identified by rid and the thread index tidx

• pylikwid.markerregioncount(rid, tidx): Return the call count for the region identified by rid and the thread index tidx

• pylikwid.markerregionresult(rid, eidx, tidx): Return the call count for the region identified by rid, the event index eidx and the thread index tidx

• pylikwid.markerregionmetric(rid, midx, tidx): Return the call count for the region identified by rid, the metric index midx and the thread index tidx

Usage

Marker API

Code

Here is a small example Python script how to use the LIKWID Marker API in Python:

#!/usr/bin/env python

import pylikwid

pylikwid.init()
pylikwid.threadinit()
liste = []
pylikwid.startregion("listappend")
for i in range(0,1000000):
    liste.append(i)
pylikwid.stopregion("listappend")
nr_events, eventlist, time, count = pylikwid.getregion("listappend")
for i, e in enumerate(eventlist):
    print(i, e)
pylikwid.close()

This code simply measures the hardware performance counters for appending 1000000 elements to a list. First the API is initialized with likwid.init() and likwid.threadinit(). Afterwards it creates an empty list, starts the measurements with likwid.startregion("listappend") and executes the appending loop. When the loop has finished, we stop the measurements again using likwid.stopregion("listappend"). Just for the example, we get the values inside our script using likwid.getregion("listappend") and print out the results. Finally, we close the connection to the LIKWID Marker API.

You always have to use likwid-perfctr to 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 -C 0 -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 use likwid-perfctr. Link: https://github.com/rrze-likwid/likwid

Example

Using 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 with likwid.getregion. The next output is the region results evaluated by likwid-perfctr and prints at first a headline stating the measured eventset, here L3 and the region name listappend. 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 with likwid.getregion. If you have set an performance group (here L3) instead of a custom event set, the raw results are derived to commonly used metrics, here the CPI (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 API

Code

#!/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 : 5222188.000000

Further comments

Please be aware that Python is a high-level language and your simple code is translated to a lot of Assembly instructions. The CPI value 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, the CPI will be worse for them but the performance will be higher as no type-checking and transformations need to be done.