| commit | 51ddd38deb5866b10bbe9712ba6e8c4fb6da6381 | [log] [tgz] |
|---|---|---|
| author | Steven Luong <sluong@cisco.com> | Wed Nov 03 15:33:21 2021 -0700 |
| committer | Damjan Marion <dmarion@me.com> | Fri Nov 05 19:20:10 2021 +0000 |
| tree | 5a326543ec4cc0e4f8b72c19c0853303ada7bef4 | |
| parent | 6259406668cee37d39ef3eb0b17ee5dc94ff02fe [diff] |
unittest: gcc-11 errors for clib_strcpy, clib_strstr, clib_strcat, and clib_strncat There are 3 versions of the string functions. For example, for strcpy, they are 1. strcpy(dst, src) -- the legacy unsafe version 2. strcpy_s(dst, dmax, src) -- C11 safeC version which has an addition argument named dmax. 3. clib_strcpy(dst,src) -- clib version to enable legacy code that uses strcpy to make use of strcpy_s without adding the additional argument, dmax, which is required by the C11 safeC version. The implementation for the clib version is to artificially provide dmax to strcpy_s. In this case, it uses 4096 which assumes that if the legacy code works without blowing up, it is likely to work with the clib version without problem. gcc-11 is getting smarter by checking if dmax is within the object's boundary. When the object is declared as static array, it will flag a warning/error if dmax is out of bound for the object since the real size of dst can be determined at compile time. There is no way to find the real size of dst if the object is dynamically allocated at compile time. For this reason, we simply can't provide support for the clib version of the function anymore. If any code is using the clib version, the choice is to migrate to the safeC version. Type: fix Fixes: b0598497afde60146fe8480331c9f96e7a79475a Signed-off-by: Steven Luong <sluong@cisco.com> Change-Id: I99fa59c878331f995b734588cca3906a1d4782f5
The VPP platform is an extensible framework that provides out-of-the-box production quality switch/router functionality. It is the open source version of Cisco's Vector Packet Processing (VPP) technology: a high performance, packet-processing stack that can run on commodity CPUs.
The benefits of this implementation of VPP are its high performance, proven technology, its modularity and flexibility, and rich feature set.
For more information on VPP and its features please visit the FD.io website and What is VPP? pages.
Details of the changes leading up to this version of VPP can be found under doc/releasenotes.
| Directory name | Description |
|---|---|
| build-data | Build metadata |
| build-root | Build output directory |
| docs | Sphinx Documentation |
| dpdk | DPDK patches and build infrastructure |
| extras/libmemif | Client library for memif |
| src/examples | VPP example code |
| src/plugins | VPP bundled plugins directory |
| src/svm | Shared virtual memory allocation library |
| src/tests | Standalone tests (not part of test harness) |
| src/vat | VPP API test program |
| src/vlib | VPP application library |
| src/vlibapi | VPP API library |
| src/vlibmemory | VPP Memory management |
| src/vnet | VPP networking |
| src/vpp | VPP application |
| src/vpp-api | VPP application API bindings |
| src/vppinfra | VPP core library |
| src/vpp/api | Not-yet-relocated API bindings |
| test | Unit tests and Python test harness |
In general anyone interested in building, developing or running VPP should consult the VPP wiki for more complete documentation.
In particular, readers are recommended to take a look at [Pulling, Building, Running, Hacking, Pushing](https://wiki.fd.io/view/VPP/Pulling,_Building,_Run ning,_Hacking_and_Pushing_VPP_Code) which provides extensive step-by-step coverage of the topic.
For the impatient, some salient information is distilled below.
To install system dependencies, build VPP and then install it, simply run the build script. This should be performed a non-privileged user with sudo access from the project base directory:
./extras/vagrant/build.sh
If you want a more fine-grained approach because you intend to do some development work, the Makefile in the root directory of the source tree provides several convenience shortcuts as make targets that may be of interest. To see the available targets run:
make
The directory extras/vagrant contains a VagrantFile and supporting scripts to bootstrap a working VPP inside a Vagrant-managed Virtual Machine. This VM can then be used to test concepts with VPP or as a development platform to extend VPP. Some obvious caveats apply when using a VM for VPP since its performance will never match that of bare metal; if your work is timing or performance sensitive, consider using bare metal in addition or instead of the VM.
For this to work you will need a working installation of Vagrant. Instructions for this can be found [on the Setting up Vagrant wiki page] (https://wiki.fd.io/view/DEV/Setting_Up_Vagrant).
Several modules provide documentation, see @subpage user_doc for more end-user-oriented information. Also see @subpage dev_doc for developer notes.
Visit the VPP wiki for details on more advanced building strategies and other development notes.