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

You may need to install graphviz for pprof to generate flame graphs.

sudo apt-get install graphviz

CPU profiling#

To launch Ray in profiling mode and profile Raylet, define the following variables:

export PERFTOOLS_PATH=/usr/lib/x86_64-linux-gnu/libprofiler.so
export PERFTOOLS_LOGFILE=/tmp/pprof.out
export RAY_RAYLET_PERFTOOLS_PROFILER=1

The file /tmp/pprof.out is 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.

Note: Enabling RAY_RAYLET_PERFTOOLS_PROFILER allows profiling of the Raylet component. To profile other modules, use RAY_{MODULE}_PERFTOOLS_PROFILER, where MODULE represents the uppercase form of the process type, such as GCS_SERVER.

Visualizing the CPU profile#

You can visualize the output of pprof in different ways. Below, the output is 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

Below is a snapshot of an example SVG output, from the official documentation:

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

Memory profiling#

To run memory profiling on Ray core components, use jemalloc. Ray supports environment variables that 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 4 env vars for memory profiling.

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

  • RAY_JEMALLOC_CONF: The MALLOC_CONF configuration for jemalloc, using comma-separated values. Read jemalloc docs for more details.

  • 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).

  • RAY_LD_PRELOAD_ON_WORKERS: Default value is 0, which means Ray doesn’t preload Jemalloc for workers if a library is incompatible with Jemalloc. Set to 1 to instruct Ray to preload Jemalloc for a worker using values configured by RAY_JEMALLOC_LIB_PATH and RAY_JEMALLOC_PROFILE.

# 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
export JEMALLOC_DIR=$PWD
./configure --enable-prof --enable-prof-libunwind
make
sudo make install

# Verify jeprof is installed.
which jeprof

# Start a Ray head node with jemalloc enabled.
# (1) `prof_prefix` defines the path to the output profile files and the prefix of their file names.
# (2) This example only profiles the GCS server component.
RAY_JEMALLOC_CONF=prof:true,lg_prof_interval:33,lg_prof_sample:17,prof_final:true,prof_leak:true,prof_prefix:$PATH_TO_OUTPUT_DIR/jeprof.out \
RAY_JEMALLOC_LIB_PATH=$JEMALLOC_DIR/lib/libjemalloc.so \
RAY_JEMALLOC_PROFILE=gcs_server \
ray start --head

# Check the output files. You should see files with the format of "jeprof.<pid>.0.f.heap".
# Example: jeprof.out.1904189.0.f.heap
ls $PATH_TO_OUTPUT_DIR/

# If you don't see any output files, try stopping the Ray cluster to force it to flush the
# profile data since `prof_final:true` is set.
ray stop

# Use jeprof to view the profile data. The first argument is the binary of GCS server.
# Note that you can also use `--pdf` or `--svg` to generate different formats of the profile data.
jeprof --text $YOUR_RAY_SRC_DIR/python/ray/core/src/ray/gcs/gcs_server $PATH_TO_OUTPUT_DIR/jeprof.out.1904189.0.f.heap

# [Example output]
Using local file ../ray/core/src/ray/gcs/gcs_server.
Using local file jeprof.out.1904189.0.f.heap.
addr2line: DWARF error: section .debug_info is larger than its filesize! (0x93f189 vs 0x530e70)
Total: 1.0 MB
    0.3  25.9%  25.9%      0.3  25.9% absl::lts_20230802::container_internal::InitializeSlots
    0.1  12.9%  38.7%      0.1  12.9% google::protobuf::DescriptorPool::Tables::CreateFlatAlloc
    0.1  12.4%  51.1%      0.1  12.4% ::do_tcp_client_global_init
    0.1  12.3%  63.4%      0.1  12.3% grpc_core::Server::Start
    0.1  12.2%  75.6%      0.1  12.2% std::__cxx11::basic_string::_M_assign
    0.1  12.2%  87.8%      0.1  12.2% std::__cxx11::basic_string::_M_mutate
    0.1  12.2% 100.0%      0.1  12.2% std::__cxx11::basic_string::reserve
    0.0   0.0% 100.0%      0.8  75.4% EventTracker::RecordExecution
...

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#