--- myst: html_meta: description: "Profiling guide for Ray contributors, including how to capture stack traces of C++ processes with gdb and analyze Ray's performance. Read this to diagnose high CPU usage, hangs, or bottlenecks in Ray internals." --- (ray-core-internal-profiling)= # 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 (for example, raylet). This can be useful for debugging 100% CPU utilization or infinite loops. Run the command a few times to see what the process is stuck on. ```shell sudo gdb -batch -ex "thread apply all bt" -p ``` 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 macOS have failed. ```bash sudo apt-get install google-perftools libgoogle-perftools-dev ``` You may need to install `graphviz` for `pprof` to generate flame graphs. ```bash sudo apt-get install graphviz ``` ## CPU profiling To launch Ray in profiling mode and profile Raylet, define the following variables: ```bash 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. ```bash # 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= to zoom # into subtrees. google-pprof -focus=epoll_wait -svg $RAYLET /tmp/pprof.out > /tmp/pprof.svg ``` Below is a snapshot of an example SVG output, from the official documentation: ![Example pprof SVG call-graph output annotated with hot paths](http://goog-perftools.sourceforge.net/doc/pprof-test-big.gif) ## Memory profiling To run memory profiling on Ray core components, use [jemalloc](https://github.com/jemalloc/jemalloc). Ray supports environment variables that override `LD_PRELOAD` on core components. You can find the component name from `ray_constants.py`. For example, if you'd like to profile gcs_server, search `PROCESS_TYPE_GCS_SERVER` in `ray_constants.py`. You can see the value is `gcs_server`. You must provide four environment variables for memory profiling. * `RAY_JEMALLOC_LIB_PATH`: The path to the jemalloc shared library `libjemalloc.so`. * `RAY_JEMALLOC_CONF`: The MALLOC_CONF configuration for jemalloc, using comma-separated values. Read [jemalloc docs](http://jemalloc.net/jemalloc.3.html) for more details. * `RAY_JEMALLOC_PROFILE`: Comma-separated Ray components to run Jemalloc `.so`. For example, ("raylet,gcs_server"). Note that the components should match the process type in `ray_constants.py`. (It means "RAYLET,GCS_SERVER" won't work). * `RAY_LD_PRELOAD_ON_WORKERS`: Default value is `0`, which means Ray doesn't preload Jemalloc for workers if a library is incompatible with Jemalloc. Set to `1` to instruct Ray to preload Jemalloc for a worker using values configured by `RAY_JEMALLOC_LIB_PATH` and `RAY_JEMALLOC_PROFILE`. ```bash # Install jemalloc wget https://github.com/jemalloc/jemalloc/releases/download/5.2.1/jemalloc-5.2.1.tar.bz2 tar -xf jemalloc-5.2.1.tar.bz2 cd jemalloc-5.2.1 export JEMALLOC_DIR=$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..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: ```bash ray microbenchmark ``` You can find the microbenchmark results for Ray releases in the [GitHub release logs](https://github.com/ray-project/ray/tree/master/release/release_logs). ## References - The [pprof documentation](http://goog-perftools.sourceforge.net/doc/cpu_profiler.html). - A [Go version of pprof](https://github.com/google/pprof). - The [gperftools](https://github.com/gperftools/gperftools), including libprofiler, tcmalloc, and other useful tools.