Vector Packet Processing

Introduction

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.

Changes

Details of the changes leading up to this version of VPP can be found under @ref release_notes.

Directory layout

Directory name	Description
build-data	Build metadata
build-root	Build output directory
doxygen	Documentation generator configuration
dpdk	DPDK patches and build infrastructure
@ref extras/libmemif	Client library for memif
@ref src/examples	VPP example code
@ref src/plugins	VPP bundled plugins directory
@ref src/svm	Shared virtual memory allocation library
src/tests	Standalone tests (not part of test harness)
src/vat	VPP API test program
@ref src/vlib	VPP application library
@ref src/vlibapi	VPP API library
@ref src/vlibmemory	VPP Memory management
@ref src/vlibsocket	VPP Socket I/O
@ref src/vnet	VPP networking
@ref src/vpp	VPP application
@ref src/vpp-api	VPP application API bindings
@ref src/vppinfra	VPP core library
@ref src/vpp/api	Not-yet-relocated API bindings
test	Unit tests and Python test harness

Getting started

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.

Quick-start: On an existing Linux host

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

Quick-start: Vagrant

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).

More information

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.

Test Framework

There is PyDoc generated documentation available for the VPP test framework. See @ref test_framework_doc for details.