sssp | S3 Proxy Mesos Framework | Job Orchestrator library

What could be the reason?

In order to be interoperable with g++, pgc++ (aka nvc++) uses the g++ STL and system header files. Since the location of these headers can vary, on installation a configuration file, "localrc", is created to store these locations.

What could be happening here is that on install, a single system install was selected and hence the generated localrc is for the system from which the compilers were installed, not the remote system.

If this is the case, consider re-installing with the "network" option. In this case, the creation of the localrc is delayed until the first compiler invocation with a unique localrc generated for each system.

Another possibility is that creating of the localrc file failed for some unknown reason. Possibly a permission issue. To check, you can run the 'makelocalrc' utility to see if any errors occurred.

Note for newer versions of nvc++, we are moving away from using pre-generated config files and instead determining these config items each time the compiler is invoked. The pervious concern being the overhead involved in generating the config, but this has become less of a problem.

You can use graphFrame.

You can't consume a generator in parallel, every non-trivial generator's next state is determined by its current state. You have to call next() sequentially.

From https://github.com/networkx/networkx/blob/master/networkx/algorithms/bridges.py#L162 this is how the function is implemented

It looks like the answer to your first question -- initialization of the Euclidian distance matrix -- was answered in Using cupy to create a distance matrix from another matrix on GPU, but you can certainly optimize the creation of the graph and computation of Dijkstra's matrix utilizing cuDF and cuGraph.

To create the graph efficiently you can construct a cuDF dataframe listing the edges and their weights. This is straightforward due to structure of the Euclidian distance matrix. cuGraph takes in this dataframe as an edge list and returns the graph. Then you can loop over the nodes to compute shortest to applicable vertices. This could later be parallelized or distributed with Dask if the problem size increases.

The code below is about 40x faster than utilizing nx.all_pairs_dijkstra_path_length for this problem size, it also includes the initial distance computation.

The email message object provides a get_all method which returns all instances of a header, so we can use this to obtain all the values of the received header.

Okay, so what you need to do is find the shortest path from the airport to the destination, then add 60 to all of the edges in that path, then perform dijkstra's algorithm again starting from your house and going to the office. There's probably a more efficient way to do it in one pass, but that'll get you started.