First, thanks for the responses and the interest in this topic. I have been traveling for the last few days and have not had a chance to follow up.
- The network would have multiple switches, but not multiple routers. The typical target is an infiniband network. Of course, Java let's us bind to infiniband now. - As far as I understand it, MPI relies on each node executing the same logic in a distributed communication pattern. Thus the concept of a leader election to determine a balanced tree probably does not show up. Instead, the tree is expressed in terms of the MPI rank assigned to each node. I am not suggesting that the same design pattern is used for river. - We do need a means to define the relationship between a distributed communication pattern and the manner in which data are decomposed onto a cluster. I am not sure that the proposal above gives us this directly, but some extension of it probably would. Let me give an example. In our application, we are distributing the edges of a graph among a 2-D cluster of compute nodes (p x p machines). The distribution is done by assigning the edges to compute nodes based on some function (key-range, hash-function) of the source and target vertex identifiers. When we want to read all edges in the graph, we need to do an operation that is data parallel across either the rows (in-edges) or the columns (out-edges) of the cluster. See http://mapgraph.io/papers/UUSCI-2014-002.pdf for a TR that describes this communication pattern for a p x p cluster of GPUs. In order to make this work with river, we would somehow have to associate the nodes with their positions in this 2-D topology. For example, we could annotate each node with a "row" and "column" attribute that specifies its location in the compute grid. We could then have a communicator for each row and each column based on the approach you suggest above. The advantage of such tree based communication patterns is quite large. They require log(p) communication operations where you would otherwise do p communication operations. So, for example, only 4 communication operations vs 16 for a 16 node cluster. Thanks, Bryan On Wed, Jul 29, 2015 at 1:17 PM, Greg Trasuk <tras...@stratuscom.com> wrote: > > I’ve wondered about doing this in the past, but for the workloads I’ve > worked with, I/O time has been relatively low compared to processing time. > I’d guess there’s some combination of message frequency, cluster size and > message size that makes it compelling. > > The idea is interesting, though, because it could enable things like > distributed JavaSpaces, where we’d be distributing the search queries, etc. > > I would guess the mechanism would look like: > > -Member nodes want to form a multicast group. > -They elect a leader > -Leader figures out a balanced notification tree, and passes it on to each > member > -Leader receives multicast message and starts the message passing into the > tree > -Recipients pass the message to local recipients, and also to their > designated repeater recipients (how many?) > -Somehow we monitor for disappearing members and then recast the leader > election if necessary. > > Paxon protocol would be involved, I’d guess. Does anyone have references > to any academic work on presence monitoring and leader election, beyond > Lamport’s original paper? > > I also wonder, is there a reason not to just use Multicast if it’s > available (I realize that it isn’t always supported - Amazon EC2, for > instance). > > Interesting question! > > Cheers, > > Greg Trasuk > > > On Jul 29, 2015, at 12:40 PM, Bryan Thompson <br...@systap.com> wrote: > > > > Hello, > > > > I am wondering if anyone has looked into creating tree based algorithms > for > > multi-cast of RMI messages for river. Assuming a local cluster, such > > patterns generally have log(p) cost for a cluster with p nodes. > > > > For the curious, this is how many MPI messages are communicated under the > > hood. > > > > Thanks, > > Bryan > >
