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|
set test "optim_stats"
# This test is a literal benchmark that asserts a speedup in various modes of
# the runtime. Let's consider two stap scripts. Script A:
#
# global a
# a <<< 3
# a <<< 4
# a <<< 5
# println(@count(a))
#
# And script B:
#
# global b
# b <<< 3
# b <<< 4
# b <<< 5
# println(@variance(b))
#
# These two scripts take various run times t(A) and t(B). This test
# essentially tries to make sure that t(A) < t(B). That's is because @count()
# is computionally cheaper compared to @variance(). Both @count() and
# @variance cause some incremental run time computation at every element
# addition time. The stap runtime should optimize out unneeded computations,
# so that at the script A run time no @variance() is computed.
#
# This similarly works with stats whose elements are arrays, e.g. a[1] <<< 3
#
# This test tries to make sure that the above timing assumptions are true.
if {![installtest_p]} {
untested $test
return
}
foreach runtime [get_runtime_list] {
if {$runtime != ""} {
spawn stap --runtime=$runtime -g --suppress-time-limits $srcdir/$subdir/$test.stp
} else {
spawn stap -g --suppress-time-limits $srcdir/$subdir/$test.stp
}
expect {
-timeout 300
{TEST_PASS} { pass "$test $runtime" }
{TEST_FAIL} { fail "$test $runtime" }
timeout {fail "$test $runtime: unexpected timeout"}
eof { }
}
catch {close}; catch {wait}
}
|
