Dynamic binary translation framework for instrumenting the Linux kernel and its modules
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Granary is a kernel space dynamic binary translation framework. The main goal of Granary is to enable flexible and efficient instrumentation of Linux kernel modules, while imposing no overhead to non-module kernel code.

Granary's three key novelties are:

  1. Mixed-mode execution. Granary is able to quickly "attach" and "detach" instrumentation. Under normal operation, Granary controls and instruments the execution of module code, while leaving the kernel to execute natively.
  2. Policy-driven instrumentation. Granary is able to do runtime code specialisation by allowing tool authors to explicitly recognize different execution contexts, and associate different instrumentation to code running in those contexts using "instrumentation policies". Policy- driven instrumentation lets Granary do things like instrument the code running inside an RCU read-side critical section differently than the code running outside of a read-side critical section.
  3. Reifying instrumentation. Granary bridges the gap between static and dynamic analysis by integrating static analysis results into its dynamic analyses. Reifying instrumentation allows Granary to implement low-overhead attach/detach, inspect the object graph during kernel/module transitions, and assign runtime type information to dynamically allocated objects.

Copyright Notice

Copyright © 2012-2013 Peter Goodman. All rights reserved.

Granary is licensed under a 3-clause BSD license. See LICENSE.md for the license. See ACKNOWLEDGEMENTS.md for the list of contributors.

Getting Started

  • You will need a recent version of the GCC (at leastgcc/g++ 4.8), or a recent version of Clang (at least clang/clang++ 3.2).
  • You will need Python 2.7 or above, but not Python 3.
  • You will need make/gmake.
  • Consult the documentation files on how to build a kernel that will work with Granary, how to set up a virtual machine, and how to set up SSH to enable (fast) loading.
  • Consult the online discussion group for any other questions / details.

Common Makefile Options

  • KERNEL specifies if Granary is being built for kernel instrumentation (1) or user space instrumentation (0). The default value is 0.
  • KERNEL_DIR specifies the location of one's kernel build directory. This is only relevant if KERNEL=1. The default value is /lib/modules/$(shell uname -r)/build.
  • GR_WHOLE_KERNEL=1. Specifies whether or not Granary should be compiled to instrument all kernel and module code, instead of just module code.
  • GR_CC specifies the C compiler to be used. The default is gcc-4.8.
  • GR_CXX specifies the C++ compiler to be used. The default is g++-4.8.
  • GR_PYTHON specifies the version of Python to use. Granary depends on Python 2.7; however, some setups default to other versions. If Python 2.7 is not your default version, and if it is installed, then you can select the specific binary with this flag.
  • GR_CLIENT specifies the client/tool to be used. The default is null. Other clients include cfg, null_plus, track_entry_exit, bounds_checker, everything_watched, leak_detector, watchpoint_null, rcudbg, shadow_memory, and watchpoint_stats. This might not be a complete list.
  • GR_EXTRA_CC_FLAGS and GR_EXTRA_CXX_FLAGS allow one to specify extra flags to the C and C++ compilers, respectively.
  • GR_DLL allows one to tell the Makefile to generate a dynamically linked library (for use when KERNEL=0). The default value is 0. If GR_DLL=1 and KERNEL=0 then the Makefile will generate libgranary.so on Linux and libgranary.dyld on Mac.


This is a necessary first step. This step creates a number of folders, generates files, etc.

make env

Compiling for kernel space

First, check the related documents to make sure that your kernel is built in a Granary-compatible way. It is suggested that you follow all SSH-related documents so that you can use fastload.py in place of slowload.py for the commands listed below.

This step will prepare a number of auto-generated source and non-source files for Granary. This step does not need to be repeated, unless you plan on changing any of the scripts in the scripts folder.

Note: If you are using a remote machine, e.g. a VM, then specify --remote, otherwise leave it absent from the *load.py script invocation. It helps if your local and remote machines are running the same build of the kernel. This is because {fast,slow}load.py --symbols makes a copy of /proc/kallsyms.

python scripts/slowload.py --symbols
make types KERNEL=1 KERNEL_DIR=/path/to/kernel/source/dir
make detach KERNEL=1 KERNEL_DIR=/path/to/kernel/source/dir
make wrappers KERNEL=1 KERNEL_DIR=/path/to/kernel/source/dir

The next command cleans and builds Granary. Because of the amount of template code, it is often suggested that you clean before each build. If, however, you are not modifying the template-heavy code (wrappers), then cleaning is not necessary.

Note: not cleaning will not guarantee a faster build.

Note: Do not use the -j option to invoke a parallel make for kernel builds. A python script runs as one of the last compilation dependencies, and its job is to extract specific functions from the assembly of the now-compiled files of Granary. A parallel make will mean this script sees inconsistent / incomplete files. Todo: make it so that this last dependency depends on all others, so that parallel make is supported.

make clean KERNEL=1 KERNEL_DIR=/path/to/kernel/source/dir
make all KERNEL=1 KERNEL_DIR=/path/to/kernel/source/dir

Instrumenting kernel modules

To instrument kernel modules, Granary must be loaded and started. These are two distinct steps so that if remote loading/debugging is used, then there is a chance to get Granary's symbol locations within the virtualised environment.

Once Granary is compiled, then execute the following commands. If you are loading Granary locally, then --remote need not be specified. This step might require typing the administrator password, either for the local or remote machine, depending on the --remote flag.

python scripts/slowload.py --remote slothvm

If SSH is properly configured, then you can use fastload.py in place of slowload.py. For example, for an SSH host named slothvm, one can do:

python scripts/fastload.py --remote slothvm

Note: If you are using slowload.py then we suggest using clang and clang++ as the compiler toolchain. However, this can sometimes introduce spurious bugs when debugging remote virtual machines. If fastload.py is used then either GCC or Clang can be used, with no expected drop in load times.

Note: this is a good time to attach gdb if you are running Granary remotely and are concerned that Granary might crash during its initialisation. Instruct gdb to source the auto-generated granary.syms file in the Granary source directory for Granary's symbols and their locations in memory.

On the machine on which Granary will instrument modules, invoke the following command after Granary is loaded.

sudo touch /dev/granary

This will initialise Granary. You can inspect that Granary is correctly initialised by invoking dmesg. Reinvoking sudo touch /dev/granary either has no effect, or-- if Granary is configured with performance counters enabled--outputs performance statistics to the kernel log.

After Granary is loaded and initialised, modules can be instrumented. To instrument a module, simply load it into the kernel. Loading a module is done using either modprobe or insmod.

Compiling for user space

Note: Granary is a kernel-space DBT system first. The user space implementation is highly experimental and mostly suited toward prototyping and debugging small Granary features.

make types KERNEL=0
make detach KERNEL=0
make wrappers KERNEL=0
make clean KERNEL=0
make all KERNEL=0

Compiling with clang

Other options for clang are GR_ASAN and GR_LIBCXX.

Instrumenting user space programs


make all KERNEL=0 GR_DLL=1
LD_PRELOAD=./libgranary.so my_program

To use a client, for example the CFG client, do:

make all KERNEL=0 GR_DLL=1 GR_CLIENT=cfg
LD_PRELOAD=./libgranary.so my_program

Mac OS X

make all KERNEL=0 GR_DLL=1
DYLD_INSERT_LIBRARIES=./libgranary.dylib my_program