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authorSean Christopherson <seanjc@google.com>2026-09-23 09:37:21 -0700
committerPaolo Bonzini <pbonzini@redhat.com>2026-09-26 00:39:55 -0400
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KVM: SEV: Do cache maintenance on the source VM during intra-host migrationgrafted
Manually perform cache maintenance on the source VM during intra-host migration to ensure no stale data is left in CPU caches after the VM is destroyed. Because the source VM is "converted" to a non-SEV VM, KVM's memory reclaim flows won't trigger cache maintenance, e.g. when all guest memory is reclaimed in response to detaching from the mmu_notifier. Note, relying on the destination VM to do cache maintenance isn't an option as KVM doesn't require identical guest memory configurations, i.e. the source VM may have access to memory that the destination VM does not. Enforcing equivalent memory configurations is infeasible, as it would require a *deep* comparison of memslots, e.g. to verify that not only are the memslot identical, but what the memslots point at is also identical. Fixes: b56639318bb2 ("KVM: SEV: Add support for SEV intra host migration") Cc: stable@vger.kernel.org Reported-by: Stefan Teodorescu <fane@google.com> Signed-off-by: Sean Christopherson <seanjc@google.com> Message-ID: <20260923163721.1584779-3-seanjc@google.com> Signed-off-by: Paolo Bonzini <pbonzini@redhat.com>
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+.. SPDX-License-Identifier: GPL-2.0
+
+=====================================
+Using Propeller with the Linux kernel
+=====================================
+
+This enables Propeller build support for the kernel when using Clang
+compiler. Propeller is a profile-guided optimization (PGO) method used
+to optimize binary executables. Like AutoFDO, it utilizes hardware
+sampling to gather information about the frequency of execution of
+different code paths within a binary. Unlike AutoFDO, this information
+is then used right before linking phase to optimize (among others)
+block layout within and across functions.
+
+A few important notes about adopting Propeller optimization:
+
+#. Although it can be used as a standalone optimization step, it is
+ strongly recommended to apply Propeller on top of AutoFDO,
+ AutoFDO+ThinLTO or Instrument FDO. The rest of this document
+ assumes this paradigm.
+
+#. Propeller uses another round of profiling on top of
+ AutoFDO/AutoFDO+ThinLTO/iFDO. The whole build process involves
+ "build-afdo - train-afdo - build-propeller - train-propeller -
+ build-optimized".
+
+#. Propeller requires LLVM 19 release or later for Clang/Clang++
+ and the linker(ld.lld).
+
+#. In addition to LLVM toolchain, Propeller requires a profiling
+ conversion tool: https://github.com/google/llvm-propeller.
+
+Current supported architectures include x86/X86_64 (via LBR),
+and arm64 (via SPE).
+
+The Propeller optimization process involves the following steps:
+
+#. Initial building: Build the AutoFDO or AutoFDO+ThinLTO binary as
+ you would normally do, but with a set of compile-time / link-time
+ flags, so that a special metadata section is created within the
+ kernel binary. The special section is only intend to be used by the
+ profiling tool, it is not part of the runtime image, nor does it
+ change kernel run time text sections.
+
+#. 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. Propeller is most effective on
+ platforms supporting advanced PMU features like LBR on Intel
+ machines. This step is the same as profiling the kernel for AutoFDO
+ (the exact perf parameters can be different).
+
+#. Propeller profile generation: Perf output file is converted to a
+ pair of Propeller profiles via an offline tool.
+
+#. Optimized build: Build the AutoFDO or AutoFDO+ThinLTO optimized
+ binary as you would normally do, but with a compile-time /
+ link-time flag to pick up the Propeller compile time and link time
+ profiles. This build step uses 3 profiles - the AutoFDO profile,
+ the Propeller compile-time profile and the Propeller link-time
+ profile.
+
+#. Deployment: The optimized kernel binary is deployed and used
+ in production environments, providing improved performance
+ and reduced latency.
+
+Preparation
+===========
+
+Configure the kernel with::
+
+ CONFIG_AUTOFDO_CLANG=y
+ CONFIG_PROPELLER_CLANG=y
+
+Customization
+=============
+
+The default CONFIG_PROPELLER_CLANG setting covers kernel space objects
+for Propeller builds. One can, however, enable or disable Propeller 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)::
+
+ PROPELLER_PROFILE_foo.o := y
+
+- For enabling all files in one directory::
+
+ PROPELLER_PROFILE := y
+
+- For disabling one file::
+
+ PROPELLER_PROFILE_foo.o := n
+
+- For disabling all files in one directory::
+
+ PROPELLER__PROFILE := n
+
+
+Workflow
+========
+
+Here is an example workflow for building an AutoFDO+Propeller kernel:
+
+1) Assuming an AutoFDO profile is already collected following
+ instructions in the AutoFDO document, build the kernel on the host
+ machine, with AutoFDO and Propeller build configs ::
+
+ CONFIG_AUTOFDO_CLANG=y
+ CONFIG_PROPELLER_CLANG=y
+
+ and ::
+
+ $ make LLVM=1 CLANG_AUTOFDO_PROFILE=<autofdo-profile-name>
+
+2) Install the kernel on the test machine.
+
+3) 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::
+
+ $ perf record --pfm-event RETIRED_TAKEN_BRANCH_INSTRUCTIONS:k -a -N -b -c <count> -o <perf_file> -- <loadtest>
+
+ - For arm64 with SPE::
+ There are a few kernel features that must be enabled to collect SPE profiles on Arm.
+ Below is a list of the required features:
+
+ - CONFIG_ARM_SPE_PMU=y
+ - CONFIG_PID_IN_CONTEXTIDR=y
+ - kpti=off
+
+ Use the following command to generate SPE perf data file::
+
+ $ perf record -e 'arm_spe_0/branch_filter=1,load_filter=0,store_filter=0/' -a -N -c <count> --no-switch-events -o <perf_file> -- <loadtest>
+
+ Note you can repeat the above steps to collect multiple <perf_file>s.
+
+4) (Optional) Download the raw perf file(s) to the host machine.
+
+5) Use the generate_propeller_profiles tool (https://github.com/google/llvm-propeller) to
+ generate Propeller profile. ::
+
+ $ generate_propeller_profiles \
+ --binary=<vmlinux> --profile=<perf_file> \
+ --format=propeller --propeller_output_module_name \
+ --out=<propeller_profile_prefix>_cc_profile.txt \
+ --propeller_symorder=<propeller_profile_prefix>_ld_profile.txt
+
+ "<propeller_profile_prefix>" can be something like "/home/user/dir/any_string".
+
+ This command generates a pair of Propeller profiles:
+ "<propeller_profile_prefix>_cc_profile.txt" and
+ "<propeller_profile_prefix>_ld_profile.txt".
+
+ If there are more than 1 perf_file collected in the previous step,
+ you can create a temp list file "<perf_file_list>" with each line
+ containing one perf file name and run::
+
+ $ generate_propeller_profiles \
+ --binary=<vmlinux> --profile=@<perf_file_list> \
+ --format=propeller --propeller_output_module_name \
+ --out=<propeller_profile_prefix>_cc_profile.txt \
+ --propeller_symorder=<propeller_profile_prefix>_ld_profile.txt
+
+ For arm64 SPE, add the option '--profiler=perf_spe', like::
+
+ $ generate_propeller_profiles \
+ --binary=<vmlinux> --profile=<perf_file> \
+ --profiler=perf_spe \
+ --format=propeller --propeller_output_module_name \
+ --out=<propeller_profile_prefix>_cc_profile.txt \
+ --propeller_symorder=<propeller_profile_prefix>_ld_profile.txt
+
+6) Rebuild the kernel using the AutoFDO and Propeller
+ profiles. ::
+
+ CONFIG_AUTOFDO_CLANG=y
+ CONFIG_PROPELLER_CLANG=y
+
+ and ::
+
+ $ make LLVM=1 CLANG_AUTOFDO_PROFILE=<profile_file> CLANG_PROPELLER_PROFILE_PREFIX=<propeller_profile_prefix>