DM+nanobench: print both current and max RSS.
out/Debug/dm:
( 360/380 MB 2112) 396ms gpu gm xfermodes
out/Release/nanobench:
32/33 MB 1659 2.46µs 2.55µs 2.54µs 2.67µs 2% ▄▄▄▅▅▃▂▁▂█ gpu gradient_conicalZero_clamp_hicolor
out/Debug/nanobench:
42/42 MB desk_css3gradients.skp_1 8888
BUG=skia:
NOTREECHECKS=true
Review URL: https://codereview.chromium.org/1115203002
diff --git a/bench/nanobench.cpp b/bench/nanobench.cpp
index c33f2c7..85685fb 100644
--- a/bench/nanobench.cpp
+++ b/bench/nanobench.cpp
@@ -137,7 +137,7 @@
void fence() override {
SK_GL(*this->gl, Finish());
}
-
+
bool needsFrameTiming() const override { return true; }
bool init(SkImageInfo info, Benchmark* bench) override {
uint32_t flags = this->config.useDFText ? SkSurfaceProps::kUseDistanceFieldFonts_Flag : 0;
@@ -166,7 +166,7 @@
log->configOption("GL_SHADING_LANGUAGE_VERSION", (const char*) version);
}
};
-
+
#endif
static double time(int loops, Benchmark* bench, Target* target) {
@@ -899,7 +899,7 @@
} else if (FLAGS_quiet) {
SkDebugf("median\tbench\tconfig\n");
} else {
- SkDebugf("maxrss\tloops\tmin\tmedian\tmean\tmax\tstddev\t%-*s\tconfig\tbench\n",
+ SkDebugf("curr/maxrss\tloops\tmin\tmedian\tmean\tmax\tstddev\t%-*s\tconfig\tbench\n",
FLAGS_samples, "samples");
}
@@ -963,8 +963,9 @@
if (targets.count() == 1) {
config = ""; // Only print the config if we run the same bench on more than one.
}
- SkDebugf("%4dM\t%s\t%s\n"
- , sk_tools::getBestResidentSetSizeMB()
+ SkDebugf("%4d/%-4dMB\t%s\t%s\n"
+ , sk_tools::getCurrResidentSetSizeMB()
+ , sk_tools::getMaxResidentSetSizeMB()
, bench->getUniqueName()
, config);
} else if (FLAGS_verbose) {
@@ -979,8 +980,9 @@
SkDebugf("%s\t%s\t%s\n", HUMANIZE(stats.median), bench->getUniqueName(), config);
} else {
const double stddev_percent = 100 * sqrt(stats.var) / stats.mean;
- SkDebugf("%4dM\t%d\t%s\t%s\t%s\t%s\t%.0f%%\t%s\t%s\t%s\n"
- , sk_tools::getBestResidentSetSizeMB()
+ SkDebugf("%4d/%-4dMB\t%d\t%s\t%s\t%s\t%s\t%.0f%%\t%s\t%s\t%s\n"
+ , sk_tools::getCurrResidentSetSizeMB()
+ , sk_tools::getMaxResidentSetSizeMB()
, loops
, HUMANIZE(stats.min)
, HUMANIZE(stats.median)