Using AutoFDO with the Linux kernel¶

This enables AutoFDO build support for the kernel when using the Clang compiler. AutoFDO (Auto-Feedback-Directed Optimization) is a type of profile-guided optimization (PGO) used to enhance the performance of binary executables. It gathers information about the frequency of execution of various code paths within a binary using hardware sampling. This data is then used to guide the compiler’s optimization decisions, resulting in a more efficient binary. AutoFDO is a powerful optimization technique, and data indicates that it can significantly improve kernel performance. It’s especially beneficial for workloads affected by front-end stalls.

For AutoFDO builds, unlike non-FDO builds, the user must supply a profile. Acquiring an AutoFDO profile can be done in several ways. AutoFDO profiles are created by converting hardware sampling using the “perf” tool. It is crucial that the workload used to create these perf files is representative; they must exhibit runtime characteristics similar to the workloads that are intended to be optimized. Failure to do so will result in the compiler optimizing for the wrong objective.

The AutoFDO profile often encapsulates the program’s behavior. If the performance-critical codes are architecture-independent, the profile can be applied across platforms to achieve performance gains. For instance, using the profile generated on Intel architecture to build a kernel for AMD architecture can also yield performance improvements.

There are two methods for acquiring a representative profile: (1) Sample real workloads using a production environment. (2) Generate the profile using a representative load test. When enabling the AutoFDO build configuration without providing an AutoFDO profile, the compiler only modifies the dwarf information in the kernel without impacting runtime performance. It’s advisable to use a kernel binary built with the same AutoFDO configuration to collect the perf profile. While it’s possible to use a kernel built with different options, it may result in inferior performance.

One can collect profiles using AutoFDO build for the previous kernel. AutoFDO employs relative line numbers to match the profiles, offering some tolerance for source changes. This mode is commonly used in a production environment for profile collection.

In a profile collection based on a load test, the AutoFDO collection process consists of the following steps:

Initial build: The kernel is built with AutoFDO options without a profile.
Profiling: The above kernel is then run with a representative workload to gather execution frequency data. This data is collected using hardware sampling, via perf. AutoFDO is most effective on platforms supporting advanced PMU features like LBR on Intel machines.
AutoFDO profile generation: Perf output file is converted to the AutoFDO profile via offline tools.

The support requires a Clang compiler LLVM 17 or later.

Preparation¶

Configure the kernel with:

CONFIG_AUTOFDO_CLANG=y

Customization¶

The default CONFIG_AUTOFDO_CLANG setting covers kernel space objects for AutoFDO builds. One can, however, enable or disable AutoFDO build for individual files and directories by adding a line similar to the following to the respective kernel Makefile:

For enabling a single file (e.g. foo.o)
```
AUTOFDO_PROFILE_foo.o := y
```
For enabling all files in one directory
```
AUTOFDO_PROFILE := y
```
For disabling one file
```
AUTOFDO_PROFILE_foo.o := n
```
For disabling all files in one directory
```
AUTOFDO_PROFILE := n
```

Workflow¶

Here is an example workflow for AutoFDO kernel:

Build the kernel on the host machine with LLVM enabled, for example,
```
$ make menuconfig LLVM=1
```
Turn on AutoFDO build config:
```
CONFIG_AUTOFDO_CLANG=y
```
With a configuration that with LLVM enabled, use the following command:
```
$ scripts/config -e AUTOFDO_CLANG
```
After getting the config, build with
```
$ make LLVM=1
```
Install the kernel on the test machine.
Run the load tests. The ‘-c’ option in perf specifies the sample event period. We suggest using a suitable prime number, like 500009, for this purpose.
- For Intel platforms:
```
$ perf record -e BR_INST_RETIRED.NEAR_TAKEN:k -a -N -b -c <count> -o <perf_file> -- <loadtest>
```
- For AMD platforms:
  
  The supported systems are: Zen3 with BRS, or Zen4 with amd_lbr_v2. To check,
  
  For Zen3:
```
$ cat proc/cpuinfo | grep " brs"
```
  For Zen4:
```
$ cat proc/cpuinfo | grep amd_lbr_v2
```
  The following command generated the perf data file:
```
$ perf record --pfm-events RETIRED_TAKEN_BRANCH_INSTRUCTIONS:k -a -N -b -c <count> -o <perf_file> -- <loadtest>
```
(Optional) Download the raw perf file to the host machine.
To generate an AutoFDO profile, two offline tools are available: create_llvm_prof and llvm_profgen. The create_llvm_prof tool is part of the AutoFDO project and can be found on GitHub (https://github.com/google/autofdo), version v0.30.1 or later. The llvm_profgen tool is included in the LLVM compiler itself. It’s important to note that the version of llvm_profgen doesn’t need to match the version of Clang. It needs to be the LLVM 19 release of Clang or later, or just from the LLVM trunk.
```
$ llvm-profgen --kernel --binary=<vmlinux> --perfdata=<perf_file> -o <profile_file>
```
or
```
$ create_llvm_prof --binary=<vmlinux> --profile=<perf_file> --format=extbinary --out=<profile_file>
```
Note that multiple AutoFDO profile files can be merged into one via:
```
$ llvm-profdata merge -o <profile_file> <profile_1> <profile_2> ... <profile_n>
```
Rebuild the kernel using the AutoFDO profile file with the same config as step 1, (Note CONFIG_AUTOFDO_CLANG needs to be enabled):
```
$ make LLVM=1 CLANG_AUTOFDO_PROFILE=<profile_file>
```