advanced modular software performance monitoring

Advanced Modular Software Performance Monitoring

CPU profiling with Intel® VTune™ Amplifier XE

Alexander MazurovFerrara University, CERN

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I. Event Processing Software II. Profilers III. Intel® VTune™ Amplifier XE IV. Gaudi Framework V. Gaudi Intel Profiler Auditor VI. Profiling examples

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Physics events

The Higgs Boson

Simulation * Trigger * Analysis

I. Event Processing Software

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Detectorevents

Events to storage

106 events/sec 4500 events/sec

LHCb High Level Trigger (HLT) Software

Moore

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II. Profilers

Collect information related to how an application or

system perform.

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Measure frequency and duration of functions calls and/or code

instructions.

CPU Profiler

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Profiling Techniques

- Hardware counters- Instrumenting the code

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Hardware countersExploit hardware performance counters from Performance Monitoring Unit (PMU)

Counters: - Translation lookaside buffer (TLB) misses - Cache misses - Stall cycles - Memory access latency - ...

Perfmon2 * Intel VTune Amplifier

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Instrumenting the code

- Statically: * Change code manually / automatically * Compiler assisted (gcc -pg)

- Dynamically (at runtime): * Change code in runtime - Valgrind - Google Performance Tools - Intel VTune Amplifier

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III. VTune™ Amplifier XEPerformance Profiling Tool

- x86 (32 and 64-bit)- GUI and CLI

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VTune™ FeaturesRuntime instrumenting profiler

- User-mode sampling- Hardware-based sampling- Concurrency and locks and waits analysis- Threading timeline- Attach to a running process- Source view

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1) Interupts a process2) Collect samples of all active instruction addresses

3) Restore a call sequence upon each sample.

How user-mode sampling works?

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User-mode analysis types

- Hotspots- Concurrency- Locks and Waits

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User-mode samplingHotspots analysis:

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Group results

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Call Stack

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Filter by timeline

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CPU time by code line

Debug mode (-g)

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User-mode sampling is a statistical method and does not provide a 100% accurate results.

Accuracy depends on:- Duration of the collection- Speed of processor- Amount of software activity- Sampling interval * recommended value is 10 ms * profiling is only 5% slower

Sampling Accuracy

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Integrating VTune™ Amplifierto Event Processing Framework

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IV. GaudiEvent processing framework

MooreTrigger

GaussSimulation

BrunelReconstruction

OnlineMonitoring

and commissioningDaVinci

Physicsanalysis

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Gaudi Architecture

Algorithms * Services * Tools

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Moore Event LoopHlt1DiMuonHighMassFilterSequence Hlt1DiMuonHighMassStreamer FastVeloHlt MuonRec Velo2CandidatesDiMuonHighMass GECLooseUnit createITLiteClusters createVeloLiteClusters

Algorithms Sequence

How to profile algorithms?

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V. Gaudi Intel Profiling Auditor

VTune™ User API +

Gaudi Auditors API

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VTune™ User API

- Start/Pause profiling- Mark profiling regions

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Gaudi Auditors API

Algorithm

Start event End event

Callback functions

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Algorithms profiling (I)

CPU time per sequence branch

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Algorithms profiling (II)

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Gaudi configuration

from Configurables import IntelProfilerAuditorprofiler = IntelProfilerAuditor()profiler.StartFromEventN = 5000 profiler.StopAtEventN = 15000AuditorSvc().Auditors +=  [profiler]

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Run: $> intelprofiler -o /collected/data job.py

Analyze (GUI): $> amplxe-gui /collecter/data/r001hs

Analyze (CLI): $> amplxe-cl -reports hotspots -r /collecter/data/r001hs

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VI. Profiling examples

1. Memory allocation functions2. Measuring profiling accuracy3. Custom reports

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1. Memory allocation functionsoperatornew from libstdc++ library:

tc_new from tcmalloc library:

tc_new uses twice less time then operatornew

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2. Measuring profiling accuracy

Intel Profiling Auditorvs .

Timing AuditorMeasures the absolute time of

algorithm's run1000 events

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3. Custom reportsBuild reports using CSV files exported

from VTune Amplifier

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Conclusions

Intel® VTune™ Amplifier XE:

+ Various analysis types and reports + Rich User API + Reasonable overhead time