Profiling for Ray Developers#

This guide helps contributors to the Ray project analyze Ray performance.

Getting a stack trace of Ray C++ processes#

You can use the following GDB command to view the current stack trace of any running Ray process (e.g., raylet). This can be useful for debugging 100% CPU utilization or infinite loops (simply run the command a few times to see what the process is stuck on).

sudo gdb -batch -ex "thread apply all bt" -p <pid>

Note that you can find the pid of the raylet with pgrep raylet.

Installation#

These instructions are for Ubuntu only. Attempts to get pprof to correctly symbolize on Mac OS have failed.

sudo apt-get install google-perftools libgoogle-perftools-dev

Launching the to-profile binary#

If you want to launch Ray in profiling mode, define the following variables:

export PERFTOOLS_PATH=/usr/lib/x86_64-linux-gnu/libprofiler.so
export PERFTOOLS_LOGFILE=/tmp/pprof.out

The file /tmp/pprof.out will be empty until you let the binary run the target workload for a while and then kill it via ray stop or by letting the driver exit.

Memory Profiling#

If you want to run memory profiling on Ray core components, you can use Jemalloc (https://github.com/jemalloc/jemalloc). Ray supports environment variables to override LD_PRELOAD on core components.

You can find the component name from ray_constants.py. For example, if you’d like to profile gcs_server, search PROCESS_TYPE_GCS_SERVER in ray_constants.py. You can see the value is gcs_server.

Users are supposed to provide 3 env vars for memory profiling.

  • RAY_JEMALLOC_LIB_PATH: The path to the jemalloc shared library so.

  • RAY_JEMALLOC_CONF: The MALLOC_CONF of jemalloc (comma separated).

  • RAY_JEMALLOC_PROFILE: Comma separated Ray components to run Jemalloc so. e.g., (“raylet,gcs_server”). Note that the components should match the process type in ray_constants.py. (It means “RAYLET,GCS_SERVER” won’t work).

# Install jemalloc
wget https://github.com/jemalloc/jemalloc/releases/download/5.2.1/jemalloc-5.2.1.tar.bz2
tar -xf jemalloc-5.2.1.tar.bz2
cd jemalloc-5.2.1
./configure --enable-prof --enable-prof-libunwind
make

# set jemalloc configs through MALLOC_CONF env variable
# read http://jemalloc.net/jemalloc.3.html#opt.lg_prof_interval
# for all jemalloc configs
# Ray start will profile the GCS server component.
RAY_JEMALLOC_CONF=prof:true,lg_prof_interval:33,lg_prof_sample:17,prof_final:true,prof_leak:true \
RAY_JEMALLOC_LIB_PATH=~/jemalloc-5.2.1/lib/libjemalloc.so \
RAY_JEMALLOC_PROFILE=gcs_server \
ray start --head

# You should be able to see the following logs.
2021-10-20 19:45:08,175       INFO services.py:622 -- Jemalloc profiling will be used for gcs_server. env vars: {'LD_PRELOAD': '/Users/sangbincho/jemalloc-5.2.1/lib/libjemalloc.so', 'MALLOC_CONF': 'prof:true,lg_prof_interval:33,lg_prof_sample:17,prof_final:true,prof_leak:true'}

Visualizing the CPU profile#

The output of pprof can be visualized in many ways. Here we output it as a zoomable .svg image displaying the call graph annotated with hot paths.

# Use the appropriate path.
RAYLET=ray/python/ray/core/src/ray/raylet/raylet

google-pprof -svg $RAYLET /tmp/pprof.out > /tmp/pprof.svg
# Then open the .svg file with Chrome.

# If you realize the call graph is too large, use -focus=<some function> to zoom
# into subtrees.
google-pprof -focus=epoll_wait -svg $RAYLET /tmp/pprof.out > /tmp/pprof.svg

Here’s a snapshot of an example svg output, taken from the official documentation:

http://goog-perftools.sourceforge.net/doc/pprof-test-big.gif

Running Microbenchmarks#

To run a set of single-node Ray microbenchmarks, use:

ray microbenchmark

You can find the microbenchmark results for Ray releases in the GitHub release logs.

References#