Difference between revisions of "HPCToolkit by example"

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* [http://hpctoolkit.org/ Main website.]
* [http://hpctoolkit.org/ Main website.]
* [http://hpctoolkit.org/manual/HPCToolkit-users-manual.pdf Users manual.] (Much of this text was inspired by, or outright hoisted from it)
* [http://hpctoolkit.org/manual/HPCToolkit-users-manual.pdf Users manual.] (Much of this text was inspired by, or outright hoisted from it)
* [https://computing.llnl.gov/tutorials/HPCToolkit.MellorCrummey.2012.08.07.pdf John Mellor-Crummey, Gaining Insight into Parallel Program Performance using HPCToolkit]

Revision as of 14:40, 24 September 2014

hpctraceviewer displaying AMR running on a Cray XE6

HPCToolkit is a suite of tools for tracing, profiling and analyzing parallel programs. It can accurately measure a program's amount of work and resource consumption, as well as user-defined derived metrics such as FLOPS inefficiency and (lack of) scaling behavior. These metrics can then be correlated with source code to pinpoint hotspots.

Being exclusively based on statistical sampling (instead of annotating source code or intercepting MPI calls), it typically adds an overhead of only 1-3% for reasonable choices of sampling periods, and can work with fully optimized binaries. An optional (but recommended) pre-analysis of the static program structure accounts for compiler transformations such as inlining and pipelining, so that those performance metrics can be more accurately associated with loops and functions even in a highly-optimized binary.

Thanks to accurate callstack unwinding, it is possible to accurately determine which particular callpath leads to a given performance behavior, shifting blame from symptoms to causes. It also enables fine-grained tracing to help identify e.g. load imbalance, understand program behavior across nodes and time, etc.


The toolkit may be downloaded from the official website, as well as its dependencies, which, for user convenience, have been packaged in a single tarball, denoted hpctoolkit-externals. Binaries for the visualization tools hpcviewer (for profiling), and hpctraceviewer (for tracing) may also be downloaded from the same page; these need a reasonably recent version of the java runtime (1.5.* will suffice).

In this tutorial, we will be using all four packages from the website above. Furthermore, we will make use of PAPI 4.4.0 (see the section Hardware counters below).[1]

First, build the externals package:

   % tar xzf hpctoolkit-externals-5.3.2-r3950.tar.gz
   % cd hpctoolkit-externals-5.3.2-r3950
   % mkdir build && cd build
   % ../configure --with-mpi=$MPI_PATH
   % make install

Here we assume that MPI was installed in $MPI_PATH.[2]

Finally we will build the main toolkit. Let $EXT_PATH be the path to the external's build folder, and $PAPI_PATH

The procedure is then similar to before:

   % tar xzf hpctoolkit-5.3.2-r3950.tar.gz
   % cd hpctoolkit-5.3.2-r3950
   % mkdir build && cd build
   % ../configure --with-papi=$PAPI_PATH [--with-mpi=$MPI_PATH]
   % make install

If MPICH2 is not in the $PATH, the additional flag --with-mpi=$MPI_PATH must be passed (otherwise HPCToolkit will not be built with MPI support). Before continuing, ensure that the following line is in the configuration summary:

   configure:   mpi support?: yes

Optionally the two visualizers may also be installed (on development machines). In either case, the following command should be executed in the uncompressed folder:

   % ./install $HPCTOOLKIT_PATH

where, as expected, $HPCTOOLKIT_PATH is the folder where the main toolkit above was installed (TODO: which is the default?).

More information, especially concerning different platforms, may be found in the official instructions page.

Numerical integration

This very short program estimates \pi by using the method of trapezoids with the following identity:

\int_0^1\frac{4}{1+x^2}\ dx = \pi

It is part of the MPICH2 source code distribution, and may be found at examples/cpi.c.

As HPCToolkit is based on sampling, there is no need for manual source code instrumentation. Compilation remains mostly unchanged; more importantly, it is highly recommended to compile the target program with debugging information and optimization turned on:

   % mpicc -g -O3 cpi.c -o cpi -lm

However, should the application be statically linked (such as on Compute Node Linux or BlueGene/P), there is the extra step of linking with hpclink.

   % hpclink <regular-linker> <regular-linker-arguments>
   % ## For example:
   % hpclink mpicc -o cpi cpi.o -lm

(More information about static linking can be found in chapter 9 of the users manual.)

Next, we must recover the static program structure from the linked binary, for which there is a tool named hpcstruct, typically launched with no extra arguments:

   % hpcstruct ./cpi 

This will build a representation of the program's structure in cpi.hpcstruct (e.g. loop nesting, inlining) to be used later when profiling/tracing, so that performance metrics may accurately be associated with the correct code construct (be it a loop or a procedure).

Overview of HPCToolkit's workflow (from the official website)

Execution differs in that hpcrun should be used to launch the executable (in addition to mpirun):

   % mpirun -np 8 hpcrun <hpcrun-args> ./cpi

The argument for hpcrun will define which measurements will be made, and how often. By default, HPCToolkit comes with a handful of events; a list may be obtained via the following command:

   % hpcrun -L ./cpi

Events are passed as arguments of the form --event e_i@p_i, where:

  • e_i: event identifier (WALLCLOCK, MEMLEAK, etc);
  • p_i: period in units meaningful to the event: microseconds for WALLCLOCK, cycles for PAPI_TOT_CYC, cache misses for PAPI_L2_DCA, etc.

(Statically linked applications set the environment variable HPCRUN_EVENT_LIST instead, which uses the same format, and separes events by a ';'. Please check the chapter referred to above.)

Now we pick a random metric to use, such as the performance of single precision division:

   % mpirun -np 12 hpcrun --event WALLCLOCK@400000 --event PAPI_FDV_INS@10000 ./cpi

This will create a folder named hpctoolkit-cpi-measurements, with entries for every rank used during runtime.

Finally, we combine the measurements with the program structure, obtaining the final profiling database, using the command hpcprof:

   % hpcprof -S cpi.hpcstruct -I ./'*' hpctoolkit-cpi-measurements

The parameter -I should point to the folder containing the program's source code. If it is distributed among several directories, the * wildcard indicates that the directory should be searched recursively for the code. Note the single quotes around *: that is used to prevent the shell from expanding it into something else.[3]

Much more information about any of these topics can be found in chapter 3 of the users manual.

Hardware counters

If compiled with PAPI support, HPCToolkit can record low-level events such as the number of mispredicted branches, cache hits/misses and so on (to the extent supported by the hardware), on a function/loop granularity. This information can help developers find subtle, possibly architecture-specific performance bugs. Additionally, HPCToolkit supports creating derived metrics from those provided by PAPI, such as the difference between the FLOPS/cycle ratio of a given loop, and the peak one seen during execution.

No modifications of the code (nor of the build process) are required to support this; like any other event, those from PAPI are enabled at run-time through the command-line argument --event COUNTER_NAME@PERIOD to hpcrun. The command papi_avail lists all available hardware counters on the processor, but only those listed using the hpcrun -L command above can be used, as some of these are derived hardware metrics (i.e. exposed as a convenience by PAPI, but not part of the processor's counter interface).[4]

For example, to sample floating point operations once every 400000 operations, as well as the number of L2 cache misses once every 100000 misses, when spawning a job with 12 ranks, the following command could be used:

   mpirun -np 12 hpcrun --event PAPI_FP_OPS@400000 --event PAPI_L2_TCM@100000 ./program

Derived metrics are defined at analysis time (i.e. using the visualizer hpcviewer).

Matrix-Matrix multiplication

In this example, we show how PAPI can be used to find an inefficient use of cache in the context of matrix-matrix multiplications. The attached code implements the naïve textbook algorithm, which is inefficient in the following sense. Since C/C++ are row-major, when calculating X = AB, although we conceptually use B column-wise, the memory system is in fact fetching a rectangular block (whose height is that of the column, and whose length is determined by the cache block size), of which we only use the first column. Since this column is reused for every row of A, both of which may be very large, by the time we actually start using the rest of said block it will likely have been evicted to make space for A's rows.

To measure this effect, the following counters will be used:[5]

Counter name Description Period
PAPI_L2_TCM L2 cache misses 2500
PAPI_L2_DCA L2 data cache accesses 2500

The period was chosen somewhat arbitrarily; short runs should use small periods, whereas longer runs may increase it (though that might add blindspots). We now compile, analyse, and execute the code as described above:

   % mpicc -g -O3 -std=c99 -lm -o mmult mmult.c
   % hpcstruct ./mmult
   % mpirun -np 4 hpcrun --event PAPI_L2_TCM@10000 --event PAPI_L2_DCA@10000 ./mmult 32 64 128

Main hpcviewer screen with counter information.

This will randomly generate two double matrices, one of dimension 32x64, and the other of dimension 64x128. It will then multiply them using the naïve algorithm, where each rank is responsible for a range of rows of the final product, and gather the result on rank 0.

The measurements made by hpcrun must now be combined with the source code analysis to make the database, as described above. We then launch the visualizer on the database.

   % hpcprof -S mmult.hpcstruct -I ./'*' hpctoolkit-mmult-measurements
   % hpcviewer hpctoolkit-mmult-database

Adding a new derived metric.

We are interested in a new custom metric, the L2 cache miss rate, which may be added via the following highlighted button:

The (somewhat hidden) derived metric button.

The menu is (literally) self-explanatory; we then add the desired metric[6]:

    \text{miss rate} = \frac{\text{number of L2 misses}}{\text{number of L2 accesses}}.

As we ran with very small matrices, which may fit even in L1, the cache miss rate is very small (5.56%). Increasing the size to something larger, such as 2048x2048, clearly shows the behavior described in the beginning of this section: the miss rate is 94.62%.

So we should now try something slightly less naive:

  1. For every element a_{ij} of A:
    1. Load row B_j of B;
    2. Store product a_{ij}B_j in row C_i.
Lower cache miss with a better implementation; it is easy to test hypotheses using counters from PAPI.

This makes better use of the cache because the rows of B are stored (and accessed) sequentially, which is corroborated by the lower cache miss rate of 54.83%.[7]

Thanks to HPCToolkit's support for profiling hardware counters, such techniques can easily be evaluated with no manual bookkeeping whatsoever by the developer. Further, it can also help determine the impact of these changes on different architectures.

Weak/strong scaling

Another useful application of HPCToolkit is in determining scalability of programs (and assigning blame on a function to function basis). We reuse the more efficient matrix multiplication code above, first to investigate slow scaling, and then strong scaling. Since now we are only interested in execution time and number of cycles taken, we use the following counters instead:

Counter name Description Period
PAPI_TOT_CYC Total cycles 10000
WALLCLOCK Wall clock time used by the process in microseconds 100000

The mathematics of the problem presents no obvious scaling (both weak and strong) constraint for the algorithm, but HPCToolkit provides a very straightforward way of verifying (or, in this case, refuting) intuition.

Starting with weak scaling (with 2 and 8 cores), we now define the following derived metric[8] for every context/function:

\frac{\text{number of cycles taken for 8 cores} - \text{number of cycles taken for 2 cores}}{\text{total number of cycles taken for 8 cores}}

This metric associates with every scope its contribution to the overall scaling loss, i.e. how much longer the execution takes on 8-cores than on 2-cores (despite the fact that every core does exactly the same amount of work).

   % mpirun -np 2 hpcrun --event PAPI_TOT_CYC@10000 --event WALLCLOCK@100000 ./mmult 1024 1024 1024
   % hpcprof -S mmult.hpcstruct -I ./'*' hpctoolkit-mmult-measurements

After running mmult and hpcprof a second time (with 8 ranks), a folder named hpctoolkit-mmult-database-<PID> will be created, where PID is hpcprof's second run's PID.

Databases for 2- and 8-cores, and their merged version.

Both databases for the 2- and 8-core runs must now be merged so that their components may be used to make new metrics. This is done by opening them with the File menu, and then, on the same menu, pressing Merge databases. Meanwhile, the main hpcviewer screen should look as on the right. The rightmost view is of the merged databases, so the previous two may safely be closed. We now proceed as usual.

The new metric will use what is called an aggregate metric, which is the sum of all elements in the column of a given existing metric, (PAPI_TOT_CYC in our case); it is obtained, as described in the menu, by replacing the $ preceding the metric ID by a @.

As seen on the left, the program is not scaling well on a single node: running a problem 4 times as big on 8 cores takes 3x as long. We suspect the problem may lie on memory access contention among the cores.[9]


To get tracing information, the flag -t should be passed to hpcrun. To illustrate this, we use the example trace data from the official website (which is much richer than any of our examples here). We now point hpctraceviewer to the uncompressed folder:

   % hpctraceviewer hpctoolkit-chombo-crayxe6-1024pe-trace

The interface is relatively straightforward:

Trace for the AMR chombo example on HPCToolkit's website
  1. Trace view: a regular timeline; whereas in jumpshot the nested functions are represented as nested rectangles, here they are merely represented with different colors, depending on the level selected on the...
  2. Call path: this indicates the maximum depth of the call path for every point in the timeline. Notice that, unlike jumpshop's legend window, this is not index by function but merely by callstack depth; in fact, some functions may appear more than once, with different colors. The callstack is determined by the position of the crosshead.
  3. Depth view: this is a plot of the above call path, per unit of time.
  4. Summary view: a collapsed view of the trace view, column-wise; this illustrates how much time is spent on each callpath depth.
  5. Mini map: a faster way of moving around/zooming in and out in the trace view.



  • install hpctoolkit
# install papi
./configure --prefix=$PWD/install-{blues,fusion} 2>&1 | tee config.txt
make -j8 2>&1 | tee m.txt
make install

# install hpctoolkit-externals
mkdir build-{blues,fusion} ; cd build-{blues,fusion}
../configure --prefix=$PWD/install 2>&1 | tee config.txt
make -j8 2>&1 | tee m.txt
make install

# install hpctoolkit
mkdir build-{blues,fusion} ; cd build-{blues,fusion}
../configure --prefix=$PWD/install --with-externals=PATH --with-papi=PATH 2>&1 | tee config.txt
make -j8 2>&1 | tee m.txt
make install
  • use hpctoolkit
# Dynamically linked applications
[mpi-launcher] hpcrun [hpcrun-options] app [app-arguments]

# recover static program structure
hpcstruct app

# analyze measurements
hpcprof -S app.hpcstruct -I <app-src>/'*' hpctoolkit-app-measurements1 [hpctoolkit-app-measurements2 ...]


<mpi-launcher> hpcprof-mpi -S app.hpcstruct -I <app-src>/'*' hpctoolkit-app-measurements1 [hpctoolkit-app-measurements2 ...]

hpcviewer hpctoolkit-app-database




^  We haven't thoroughly tested HPCToolkit 2.21.4 with PAPI-V; use 4.4.0 if possible.

^  This may be found by typing which mpicc and removing bin/mpicc from the resulting string.

^  If you're wondering why HPCToolkit doesn't use -R instead, it is because it already has another meaning; check chapter 3 of the users guide.

^  For some reason, HPCToolkit cannot use derived metrics.

^  The machine used to develop this example (with a core i7-2630QM) cannot provide information on L1 cache accesses.

^  Notice that we count the total number of L2 cache misses, but only the L2 data cache accesses. HPCToolkit unfortunately cannot profile using derived metrics (such as L2 data cache misses); these can be found at papi_avail. Thankfully, the most time-consuming computation in this case is a very short loop, so the L2 instruction cache constitutes around 1% of the total L2 cache misses.

^  Arguably, this is still high, but the point is to illustrate how to go about measuring these things.

^  Taken from the HPCToolkit users manual.

^  Unfortunately the machine on which this was tested did not support PAPI_MEM_SCY, which measures the number of cycles stalled waiting for memory accesses.

External links