2013-05-21 09:29:50 +00:00
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Using the glibc microbenchmark suite
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====================================
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The glibc microbenchmark suite automatically generates code for specified
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functions, builds and calls them repeatedly for given inputs to give some
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basic performance properties of the function.
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Running the benchmark:
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=====================
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2013-12-06 08:21:09 +00:00
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The benchmark needs python 2.7 or later in addition to the
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dependencies required to build the GNU C Library. One may run the
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benchmark by invoking make as follows:
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2013-05-21 09:29:50 +00:00
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$ make bench
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This runs each function for 10 seconds and appends its output to
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benchtests/bench.out. To ensure that the tests are rebuilt, one could run:
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$ make bench-clean
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The duration of each test can be configured setting the BENCH_DURATION variable
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in the call to make. One should run `make bench-clean' before changing
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BENCH_DURATION.
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$ make BENCH_DURATION=1 bench
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The benchmark suite does function call measurements using architecture-specific
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high precision timing instructions whenever available. When such support is
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not available, it uses clock_gettime (CLOCK_PROCESS_CPUTIME_ID). One can force
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the benchmark to use clock_gettime by invoking make as follows:
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$ make USE_CLOCK_GETTIME=1 bench
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Again, one must run `make bench-clean' before changing the measurement method.
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2016-04-20 05:28:20 +00:00
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Running benchmarks on another target:
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====================================
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If the target where you want to run benchmarks is not capable of building the
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code or you're cross-building, you could build and execute the benchmark in
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separate steps. On the build system run:
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$ make bench-build
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and then copy the source and build directories to the target and run the
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benchmarks from the build directory as usual:
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$ make bench
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make sure the copy preserves timestamps by using either rsync or scp -p
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2016-04-20 07:49:01 +00:00
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otherwise the above command may try to build the benchmark again. Benchmarks
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that require generated code to be executed during the build are skipped when
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cross-building.
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2016-04-20 05:28:20 +00:00
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2017-11-28 14:27:46 +00:00
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Running subsets of benchmarks:
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==============================
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To run only a subset of benchmarks, one may invoke make as follows
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$ make bench BENCHSET="bench-pthread bench-math malloc-thread"
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where BENCHSET may be a space-separated list of the following values:
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bench-math
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bench-pthread
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bench-string
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string-benchset
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wcsmbs-benchset
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stdlib-benchset
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stdio-common-benchset
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math-benchset
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malloc-thread
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2013-05-21 09:29:50 +00:00
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Adding a function to benchtests:
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===============================
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If the name of the function is `foo', then the following procedure should allow
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one to add `foo' to the bench tests:
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- Append the function name to the bench variable in the Makefile.
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2013-10-07 06:21:24 +00:00
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- Make a file called `foo-inputs` to provide the definition and input for the
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function. The file should have some directives telling the parser script
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about the function and then one input per line. Directives are lines that
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have a special meaning for the parser and they begin with two hashes '##'.
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The following directives are recognized:
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- args: This should be assigned a colon separated list of types of the input
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arguments. This directive may be skipped if the function does not take any
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Accept output arguments to benchmark functions
This patch adds the ability to accept output arguments to functions
being benchmarked, by nesting the argument type in <> in the args
directive. It includes the sincos implementation as an example, where
the function would have the following args directive:
## args: double:<double *>:<double *>
This simply adds a definition for a static variable whose pointer gets
passed into the function, so it's not yet possible to pass something
more complicated like a pre-allocated string or array. That would be
a good feature to add if a function needs it.
The values in the input file will map only to the input arguments. So
if I had a directive like this for a function foo:
## args: int:<int *>:int:<int *>
and I have a value list like this:
1, 2
3, 4
5, 6
then the function calls generated would be:
foo (1, &out1, 2, &out2);
foo (3, &out1, 4, &out2);
foo (5, &out1, 6, &out2);
2013-12-05 04:42:59 +00:00
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inputs. One may identify output arguments by nesting them in <>. The
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generator will create variables to get outputs from the calling function.
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2013-10-07 06:21:24 +00:00
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- ret: This should be assigned the type that the function returns. This
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directive may be skipped if the function does not return a value.
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2013-10-08 11:04:10 +00:00
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- includes: This should be assigned a comma-separated list of headers that
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2013-10-07 06:21:24 +00:00
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need to be included to provide declarations for the function and types it
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2013-10-08 11:04:10 +00:00
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may need (specifically, this includes using "#include <header>").
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- include-sources: This should be assigned a comma-separated list of source
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files that need to be included to provide definitions of global variables
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and functions (specifically, this includes using "#include "source").
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2013-10-08 11:17:01 +00:00
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See pthread_once-inputs and pthreads_once-source.c for an example of how
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to use this to benchmark a function that needs state across several calls.
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2014-02-22 04:39:27 +00:00
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- init: Name of an initializer function to call to initialize the benchtest.
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2013-10-07 06:21:24 +00:00
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- name: See following section for instructions on how to use this directive.
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Lines beginning with a single hash '#' are treated as comments. See
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pow-inputs for an example of an input file.
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2013-05-21 09:29:50 +00:00
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Multiple execution units per function:
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=====================================
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Some functions have distinct performance characteristics for different input
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domains and it may be necessary to measure those separately. For example, some
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math functions perform computations at different levels of precision (64-bit vs
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240-bit vs 768-bit) and mixing them does not give a very useful picture of the
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performance of these functions. One could separate inputs for these domains in
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the same file by using the `name' directive that looks something like this:
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##name: 240bit
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See the pow-inputs file for an example of what such a partitioned input file
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would look like.
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2013-04-16 12:07:24 +00:00
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2017-06-20 15:26:26 +00:00
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It is also possible to measure throughput of a (partial) trace extracted from
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a real workload. In this case the whole trace is iterated over multiple times
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rather than repeating every input multiple times. This can be done via:
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##name: workload-<name>
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2013-04-16 12:07:24 +00:00
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Benchmark Sets:
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==============
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In addition to standard benchmarking of functions, one may also generate
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custom outputs for a set of functions. This is currently used by string
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function benchmarks where the aim is to compare performance between
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implementations at various alignments and for various sizes.
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To add a benchset for `foo':
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- Add `foo' to the benchset variable.
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- Write your bench-foo.c that prints out the measurements to stdout.
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- On execution, a bench-foo.out is created in $(objpfx) with the contents of
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stdout.
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2017-09-16 06:17:32 +00:00
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Reading String Benchmark Results:
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================================
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Some of the string benchmark results are now in JSON to make it easier to read
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in scripts. Use the benchtests/compare_strings.py script to show the results
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in a tabular format, generate graphs and more. Run
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benchtests/scripts/compare_strings.py -h
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for usage information.
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