pagerank | C # implementation of the PageRank algorithm | Learning library

Leaves have an out-degree of zero, so:

Here, you have permission denied error message;

SGX CPUs (before Icelake) have a limited EPC, this is 128M for CPUs like Skylake, but you can also get 256M with Xeon E-2200. This does not mean that your application cannot use more memory, it simply means that the hardware-accelerated memory range is limited. Pages that don't fit into the EPC are swapped to non-EPC memory (at a considerable performance cost), however this is only implemented in the linux driver.

So, you can set the enclave heap to something much larger like 2G. What you'll see is slower startup time (that 2G must be completely initialized), and if your compute's memory access pattern is scattered in that 2G range then you'll see extremely degraded performance. So try to keep your access patterns local, use sequential/scanning like operations etc, the usual considerations for cache-friendly compute.

Regarding your actual issue, it could be that you're running out of the allocated heap, and that vector just happens to be the "last straw". Remember that the heap must contain not only these datastructures but also the code itself. If you're parsing the input from some serialized format then it could be that the serialized bytes are still retained in memory, if you have other state then that also uses memory, there can be many sources of extraneous usage. If you're using the Intel SDK then I'd recommend compiling in simulation mode, or just link your application into a non-SGX ELF and use usual memory debugging tools to track memory usage.

Amazon Neptune does not currently support the Apache TinkerPop GraphComputer interface. You have a few options.

1. In some cases it is possible to use the example queries in the Gremlin Recipes document to calculate connected components etc.
2. Export the data using the Neptune Export tool and run the analysis you need to do using Spark (Glue and EMR are good options). This is quite commonly done today.
3. For modest size datasets you can import the data into NetworkX and run the analysis all from a Jupyter Notebook.

You need to indicate to the planner that your m.score field is numeric, so pulls that from the index. I.e. where m.score > 0

You should see it in your query plans.

Your query looks also really convoluted, and generated. But actually not taking into account that always "false" expressions can just be left out from the query parts e.g. WHERE NOT [] = []

Yes, you need to add the jar in the spark-submit command :

For reasons unknown to me, the histplot for column eigen_central has a problem determining a reasonable number of bins. The pairplot works with kde plots in the diagonal sns.pairplot(central, diag_kind="kde"), and the histplot for column eigen_central alone also does not work as expected. You can overcome this problem by defining the bin number:

Here you can find more information regarding sorting arrays in javascript.

You can chain some Array operations like sort and filter, so the solution would be to first filter out the characters without an age, and then sort the result:

1. Allocate new variables

2. Store the result to the new variables during calculation

3. Store results to the original variables from the new variables after calculation