GNU Radio was originally a streaming system with no other mechanism to pass data between blocks. Streams of data are a model that work well for samples, bits, etc., but can lack for control and meta data.
Part of this is solved using the message passing interface, which allows blocks to subscribe to messages published by any other block in the flowgraph (see Message Passing). The main drawback to the message passing system is that is works asynchronously, meaning that there is no guarantee when a message may arrive relative to the data stream.
Stream tags are an isosynchronous data stream that runs parallel to the main data stream. A stream tag is generated by a block's work function and from there on flows downstream with a particular sample until it reaches a sink or is forced to stop propagating by another block.
Stream tags are defined for a specific item in the data stream and are formed as a key:value pair. The key identifies what the value is while the value holds the data that the tag contains. Both key and value are PMTs (Polymorphic Types) where the key is a PMT symbol while the value any type of PMT and can therefore handle any data we wish to pass. A fourth part of the tag is the srcid, which is a PMT symbol and is used to identify the block that created the tag (which is usually the block's alias()).
To enable the stream tags, we have extended the API of gr::block to understand absolute item numbers. In the data stream model, each block's work function is given a buffer in the data stream that is referenced from 0 to N-1. This is a relative offset into the data stream. The absolute reference starts from the beginning of the flowgraph and continues to count up with ever item. Each input stream is associated with a concept of the 'number of items read' and each output stream has a 'number of items written.' These are programmed using the two API calls:
Each tag is associated with some item in this absolute time scale that is calculated using these functions.
Like the rest of the data stream, the number of items read/written are only updated once during the call to work. So in a work function, nitems_read/written will refer to the state of the data stream at the start of the work function. We must therefore add to this value the current relative offset in the data stream. So if we are iterating i over all output items, we would write the stream tag to output ports at nitems_written(0)+i for the 0th output port.
The stream tags API consists of four functions, two to add and two to get the stream tags. These functions are:
The two function calls to add items tags are defined here. We add a tag to a particular output stream of the block. We can output them to multiple output streams if we want, but to do so means calling one of these functions once for each port.
Again, a tag is defined as:
We can create a gr::tag_t structure to hold all of the above information of a tag, which is probably the easiest/best way to do it. The gr::tag_t struct is defined as having the same members as in the above list. To add a gr::tag_t tag to a stream, use the function:
The secondary API allows us to create a tag by explicitly listing all of the tag information in the function call:
To get tags from a particular input stream, we again have two functions we can use. Both of these pass back vectors of gr::tag_t. The second function allows us to specify a particular key (as a PMT symbol) that filters out all but the key we are interested in, which reduces the effort inside the work function for getting the right tag's data.
The first call just returns any tags between the given range of items:
Adding a fifth argument to this function allows us to filter on the key key.
Tags are propagated downstream from block to block like the normal data streams. How blocks are actually moved depends on a specific propagation policy. We defined three types of policies:
The default behavior of a block is the 'All-to-All' method of propagation.
To set a different propagation policy, use the function:
See the gr::block::tag_propagation_policy_t documentation for details on this enum type.
When a tag is propagated through a block that has a rate change, the item's offset in the data stream will change. The scheduler uses the block's gr::block::relative_rate concept to perform the update on the tag's offset value. The relative rate of a block determines the relationship between the input rate and output rate. Decimators that decimate by a factor of D have a relative rate of 1/D.
Synchronous blocks (gr::sync_block), decimators (gr::sync_decimator), and interpolators (gr::sync_interpolator) all have pre-defined and well-understood relative rates. A standard gr::block has a default relative rate of 1.0, but this must be set if it does not work this way. Often, we use a gr::block because we have no pre-conceived notion of the number of input to output items. If it is important to pass tags through these blocks that respect the change in item value, we would have to use the TPP_DONT tag propagation policy and handle the propagation internally.
In no case is the value of the tag modified when propagating through a block. This becomes relevent when using Tagged Stream Blocks.
Tags can be very useful to an application, and their use is spreading. USRP sources generate tag information on the time, sample rate, and frequency of the board if anything changes. We have a meta data file source/sink that use tags to store information about the data stream. But there are things to think about when using tags in a block.
First, when tags are not being used, there is almost no effect on the scheduler. However, when we use tags, we add overhead by getting and extracting tags from a data stream. We also use overhead in propagating the tags. For each tag, each block must copy a vector of tags from the output port(s) of one block to the input port(s) of the next block(s). These copy operations can add up.
The key is to minimize the use of tags. Use them when and only when necessary and try to provide some control over how tags are generated to control their frequency. A good example is the USRP source, which generates a time tag. If it generated a tag with every sample, we would have thousands of tags per second, which would add a significant amount of overhead. Conversely, if we started at time t0 at sample rate sr, then after N samples, we know that we are now at time t0 + N/sr. So continuously producing new tags adds no information.
The main issue we need to deal with in the above situation is when there is a discontinuity in the packets received from the USRP. Since we have no way of knowing in the flowgraph how many samples were potentially lost, we have lost track of the timing information. The USRP driver recognizes when packets have been dropped and uses this to queue another tag, which allows us to resync. Likewise, any time the sample rate or frequency changes, a new tag is issued.