I have to write a paper where I describe and write parallel algorithms in Erlang. Could you point some good reads or examples? THANKS
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I have written a merge sort algorithm that sort array with integer data.
Now I need to write another sorting algorithm again with multithread concepts -phthread, which again sorting array with integer data.
My task background:
I have 2 child processes which sorting(with different algorithms) one integer array and first completed task print result and parent kill another process. I had done everything just need to implement 2nd algorithm logic
Please tell me which algorithm I have to use and give me an example implementation
Thank you in advance
One portable option is to use Intel Parallel STL. It is compatible with C++11 and implements parallel std::sort.
C++17 parallel algorithms in GNU C++ standard library and gcc-9 delegate to Intel Parallel STL.
I am developing an algorithm that has a large numerical computation part. My project supervisor recommended me to use Fortran because of this and so for the last weeks I've been work on it (so far so good). It would be a new version of an algorithm of his, which is basically a lot of numerical computing.
Mine, however, would have more "logic" to it. Without going into much detail, the brute force approach is done using just fortran because it's just 95% of reading from a file and doing the operations. However, the aim of the project is to provide an efficient algorithm to do this, I had been thinking about methods and wanted to start with a Greedy approach (something like Hill Climbing) and that got me thinking that for this part in particular, maybe it would be better to write the algorithm in C++ instead of Fortran.
So basically, how hard do you think it would be to develop the algorithm "logic" in C++ and then call Fortran whenever the bulk of the numerical computation has to be performed. Would it be worth it? Or should I just stick with one of the two languages?
Sorry if it is a very ignorant question but I can't get an idea of whether writing an algorithm such as Hill Climbing would be more difficult if done with Fortran instead of C++ and the benefits of Fortran in this case would be worth it.
Thanks for your time and have a nice day!
I'm trying to implement a parallelized version of Dijkstra's algorithm (my very first parallel algorithm) for a course project. I got the sequential part down using a priority queue with no problem, but I'm having trouble figuring out how to go about designing a parallel version. I've been using this as a reference so far. I'm not asking anyone to design the whole thing for me, just offer me some insights or good advice about how to go about the implementation. I've been considering these things so far:
OpenMP, MPI or both?
PCAM? (e.g. graph partitioning)
Shared memory?
Try this presentation for ideas:
http://www.cse.buffalo.edu/faculty/miller/Courses/CSE633/Ye-Fall-2012-CSE633.pdf
I have done some research on the Rete algorithm and found several papers on making it parallel (both matching and rule firing) the earliest of which are from the mid '80s. However I can't find any information about any parallel implementation in any of the rule engines in the market today. I understand that parallel rule firing is problematic, but there are several proposed solutions. Does any rule engine implement any parallel version of Rete? If so, which? If not, why not? Is it too hard to achieve any significant performance gain or are there other reasons? Any links to more information is appreciated.
I believe ReteNT does.
We are working on something this summer for the Drools project, I hope we have something prototypable before end of this year. We are takling it from two angles.
1) Merge Rete and Leaps algorithm, that provides lazy beta propagations and evaluations are done on a rule cursor basis (allows for easier parallel rule evaluation).
2) Move from tuple based propagations collection propagations. This will allow nodes to be schedueld as tasks. Further nodes with large number of objects can be locally "map reduced" to further parallelise the creation of the collection that will be propagated from the node.
If you want to chat more, join us on irc :)
http://www.jboss.org/drools/irc
As i have learned basics of various parallel paradigm standard such as OpenMP, MPI, OpenCL to write parallel programming. But i don't have much knowledge about Map-Reduce Programming model.
As it is well known that various popular companies are following the Map-Reduce programming model to solve their huge data intensive tasks. As well as MPI was designed for high performance computing on both massively parallel machines and on workstation clusters.
So my first confusion is ..
Can i use the Map-Reduce model instead of MPI standard or vice-versa? or it depends upon the applications!!
What is the exact difference between them?
Which one is better and when?
You could understand Map-Reduce as a subset of MPI-functionality, as it kind of resembles MPIs collective operations with user-defined functions. Thus you can use MPI instead of Map-Reduce but not vice-versa, as in MPI you can describe many more operations. The main advantage of Map-Reduce seems to be this concentration on this single parallel concept, thereby reducing interfaces that you need to learn in order to use it.