I have just started to read up on VPP/FD.io, and I have a question about graph optimization and was wondering if (as I suspect) this has already been thought about and either planned or decided against.
The documentation I found on VPP essentially says that VPP uses batch processing and processes all packets in a vector on one step before proceeding to the next step. The claim is this provides overall better throughput because of instruction caching. I was wondering if optimization of the graph to understand where concurrency can be leveraged has been considered, as well as where you could process the vector by two steps with an offset. If this is possible, then steps could be pinned to cores and perhaps both concurrency and instruction caching could be leveraged. For example assume the following graph: [image: image.png] In this graph, steps B,C can be done concurrently as they don't "modify" the vector. Steps D, E can't be done concurrently, but as they don't require look back/forward they can be done in offset. What I am suggesting is, if there are enough cores, then steps could be pinned to cores to achieve the benefits of instruction caching, and after step A is complete, steps B,C could be done concurrently. After B,C are complete then D can be started and as D completes processing on a packet if can then be processed by E (i.e., the entire vector does not need to be processed by D before processing by E is started). I make no argument that this doesn't increase complexity and also introduces coordination costs that don't exists today. To be fair, offset processing could be viewed as splitting the original large vector into smaller vectors and processing the smaller vectors from start to finish (almost dynamic optimization based on dynamic vector resizing). Just curious to hear others thoughts and if some of this has been thought through or experimented with. As I said, just thinking off the cuff and wondering; not fully thought through. avèk respè, /david
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