HPCToolkit Evaluation Report

Hans Sherburne,Adam LekoUPC Group

HCS Research LaboratoryUniversity of Florida

Color encoding key:

Blue: Information

Red: Negative note

Green: Positive note

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Basic Information Name: HPCToolkit Developer: Rice University Current versions:

HPCView: Website:

http://www.hipersoft.rice.edu/hpctoolkit/ Contact:

John Mellor-Crummey (johnmc@cs.rice.edu) Rob Fowler (rjf@cs.rice.edu)

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Introduction HPCToolkit - A suite of tools that aid the programmer in collecting,

organizing, and displaying profile data. hpcviewer

Sorts by any collected metric, from any processes displayed Displays samples at various levels in call hierarchy through “flattening” Allows user to focus in on interesting sections of the program through

“zooming” Hpcquick

Simplifies process by integrating hpcprof and hpcview hpcview

Creates “browsable” performance databases in html, or for use in hpcviewer

bloop Relate samples to loops, even in significant changes have been affected

by optimization hpcprof

Related samples to source lines. hpcrun

collects profiles by sampling hardware performance counters

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Available Metrics in HPCToolkit Metrics, obtained by sampling/profiling

PAPI Hardware counters Any other source for data profiles that can output data in “profile-

like input format” (not tested) Wallclock time (WALLCLK)

Can’t get PAPI metrics and Wallclock time in a single run Derived metrics

Combination of existing metrics created by specifying a mathematical formula in an XML configuration file.

Source Code Correlation Metrics reflect exclusive time spent in function based on counter

overflow events Metrics correlated at the source line level and the loop level Metrics are related back to source code loops (even if code has

been significantly altered by optimization) (“bloop”)

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Main Window in hpcviewer

Figure 1: Main window in hpcviewer

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HPCToolkit (hpcrun) – Overhead All programs executed correctly when instrumented < 20 % overhead on all benchmarks when recording just PAPI_TOT_CYC (default option)

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Notes on testing

Used lam, instead of mpich for testing When MPICH mpirun used with hpcrun, hpcrun complains

about a “– p” option, even though it was not given Needed to reduce size of message in big-message.c

because of LAM Unable to get NBP - LU to run using LAM

Major stumbling blocks of hpctoolkit bottleneck identification Since profile data is not related back to the callsite in the

user’s code, but rather the actual function, it is difficult to determine where in the user’s code the problem lies.

Profiling recording wallclock time was glitchy, some profiles contained very little useful information.

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Bottleneck Identification: Performance Tool Test Suite: CAMEL, LU Testing metric: what did profile data tell us? CAMEL: TOSS-UP

Profile showed work equally distributed across the processes Unable to determine communication costs from PAPI hardware counters

NAS LU: NOT TESTED Unable to get LU benchmark to run successfully using LAM needed to use LAM because could not get MPICH to work with hpcrun

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Bottleneck Identification: Performance Tool Test Suite: PPerfMark Big message: Fail

Profiling wallclock time didn’t produce a profile with information in it

Cycle count is misleading and doesn’t reveal time spent in communication

Diffuse procedure: PASSED Profile showed large amount of time spent in

bottleneck procedure Time is diffused across processes

Hot procedure: PASSED Profile showed large amount of time spent in

bottleneck procedure Intensive server: TOSS-UP

Profile showed large amount of time spent in waste_time() on on one process

The other processes show time spent in functions outside of user code, which is difficult to use for bottleneck identification

Ping pong: TOSS-UP From profile it’s clear that within user code, the time

is spent in two different loops Profile shows time spent in functions outside of user

code, which is difficult to use for bottleneck identification

Random barrier: TOSS-UP Profile shows lots of time spent in

waste_time() Profile does not show communication pattern

amongst processes Small messages: TOSS-UP

Profile reveals only one process spends time in Grecv_messages

Profile shows time spent in functions outside of user code, which is difficult to use for bottleneck identification

System time: TOSS-UP Profile show lots of time spent in kill, and

execlp It’s difficult to relate this information back to

the call site in waste-time Wrong way: FAIL

Profile does not show communication pattern amongst processes

Profile shows time spent in functions outside of user code, which is difficult to use for bottleneck identification

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Evaluation (1) Available metrics: 3/5

Use PAPI hardware counters (or others on New metrics can be derived from existing ones No statistics regarding communication are provided In theory could use profile from any source if formatted properly

Cost: 5/5 HPCToolkit is freely available

Documentation quality: 2.5/5 Documentation is in the form of a ppt presentation, and man pages One comprehensive user manual would be helpful

Extendibility: 3.5/5 HPCToolkit source code is freely available No tracing support Requires the use of PAPI for hpcrun (profile creation)

Filtering and aggregation: 2.5/5 Only hardware counter values are recorded

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Evaluation (2) Hardware support: 4/5

IA32, Opteron, Itaniun + Linux w/PAPI, MIPS+Irix, Alpha+Tru64 Heterogeneity support: ?/5 (not tested) Installation:4/5

Installation on Linux platform not bad Requires PAPI to be installed

Interoperability: 3.5/5 Profile data stored in XML format Works with SGI’s ssrun, and Compaq’s uprofile on MPS and Alpha respectively

Learning curve: 3.5/5 The interface is fairly intuitive, but takes some use to get comfortable with the notion of

“flattening” The separation of the tools for platform support causes increase user overhead

Manual overhead: 3.5/5 It is fairly straightforward to measure at the source line and loop level It is not possible to turn on and off sampling for selected parts of the source code Specifying derived functions in XML is awkward

Measurement accuracy: 3.5/5 Overhead is less than 20% when recording a single PAPI hardware counter

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Evaluation (3) Multiple analyses: 1/5

Comparison and ordering of hardware counter values is the only form of analysis Multiple executions: 2.5/5

Comparison of metrics from multiple runs is possible There is not built-in scalability, or optimization comparison

Multiple views: 1.5/5 A single view of profile data correlated with source code is provided Only profile data (not trace data) is viewable

Performance bottleneck identification: 2/5 All metrics can be sorted in increasing or decreasing order “Flattening” approach increases ease of comparison some Bottleneck identification requires significant user insight when selecting which

hardware counters to use, and in locating points for improvement Profiling/tracing support: 1.5/5

Only profiling is supported Hardware counters must be used Profiling is done on source line, and loop level Communication profiling is not available

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Evaluation (4) Response time: 2/5

Data is not available in HPCToolkit until after execution completes and performance data is processed

Software support: 4/5 Supports sequential and parallel programs Difficulty is running with MPICH, though it is mentioned in tutorial presentation

Source code correlation: 4/5 Source code correlation of profile data is the main view offered

System stability: 3.5/5 Hpcviewer works well Unable to obtain useful performance data for some of the pperf benchmarks

Technical support: ?/5 Tech support not requested

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Conclusions The components of HPCToolkit work well for sequential code. Have access to available (native event) PAPI counters on the system. Can derive new metrics from sampled metrics using hpcview Data is correlated with source code Only simple display of profiled metrics and source code correlation is

provided Whether a metric should be created, hidden, or shown in hpcviewer must be

specified before it is run. Collection of multiple metrics may require multiple runs Parallel code may be difficult to analyze

Different methods for launching parallel programs achieve varying levels of ease and usefulness with hpcrun

Requires that line mapping information be present in all executables/libraries to be analyzed (“-g” option in many compilers)

The ability to display inclusive time spent at callsites in user code, rather than exclusive time spent in all functions would increase the usefulness of the tool tremendously.

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References

1. HPCToolkit website http://www.hipersoft.rice.edu/hpctoolkit/

2. HPCToolkit SC Tutorial Presenation http://www.hipersoft.rice.edu/hpctoolkit/sc04/index.html