Lines Matching refs:profile

1 # View the profile
7 After using `simpleperf record` or `app_profiler.py`, we get a profile data file. The file contains
9 or cpu-clock) used in this sample, etc. We have many choices for viewing the profile. We can show
13 Below shows some recommended UIs to view the profile. Google developers can find more examples in
14 [go/gmm-profiling](go/gmm-profiling?polyglot=linux-workstation#viewing-the-profile).
20 Google servers, with a powerful flamegraph UI, with strong drilldown, search, pivot, profile diff,
25 We can use `pprof_proto_generator.py` to convert profiles into pprof.profile protobufs for use in
43 the profile before uploading:
46 gzip pprof.profile
49 After compressing, you can upload the `pprof.profile.gz` file to either http://pprof/ or
51 the following `pprof` command to upload the compressed profile:
54 # Upload all threads in profile, grouped by threadpool.
56 pprof --flame --tagroot threadpool pprof.profile.gz
58 # Upload all threads in profile, grouped by individual thread name.
59 pprof --flame --tagroot thread pprof.profile.gz
61 # Upload all threads in profile, without grouping by thread.
62 pprof --flame pprof.profile.gz
105 lets you spot repeating patterns of work by laying out the profile as a subsecond heatmap.
148 # Create `Linux perf script` format profile.
151 # Create `Linux perf script` format profile using Proguard map.
157 Open the profile "as Linux Perf", and click start and end sections to get a flamegraph of that
282 $ pprof pprof.profile