I would like to see an example of a for loop which can't be auto-vectorized but which could when rewritten using parallel STL.
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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 was told that C++ basically has three kinds of loops:
for loop
while loop
do-while loop
What about the range-based for loop?
Isn't for_each a looping statement as well?
I'm confused what to answer if somebody asks me the number of types of Loops in C++. I understand that for_each is an STL algorithm which could have been implemented using one of those above looping constructs in C++. But by this logic, any of those basic loops could be simulated by one of the other loops.
Is there any quote in the C++ Standard which confirms the number?
What about the range-based for loop?
A range based for loop is a loop.
Isn't for_each a looping statement as well?
No, it's a function template.
I'm confused what to answer if somebody asks me the number of types of Loops in C++
Depends on what they mean. One may include goto and recursion in addition to for, while, do while but not the STL algorithms, others may include everything that is capable of repeating a piece of code, yet others may just count goto as the "real deal" because other loops can be emulated with goto. Then there is also setjmp/longjmp. In any case that is a vague and unuseful question.
In Intel's strip mining example:
https://software.intel.com/en-us/articles/strip-mining-to-optimize-memory-use-on-32-bit-intel-architecture
Why not merge Transform and Lighting into one loop? It would solve the cache eviction problem.
Someone has asked the same question in the comments but there is no answer.
If splitting the loop is somehow faster ... Why? Under what conditions should we split loops?
I looked through several posts about splitting loops but I still don't get it.
If only one of those operations can be vectorized, then combining them could prevent vectorization of the loop. In that case dividing the array into cache sized stripes would be better. I don't know if that applies to Transform and Lighting. If not, then maybe they aren't necessarily a good example for the demonstration.
When the loop is partially vectorizable (is that a word?) and partly not, fission* is usually the way to go.
*splitting is actually the name for the technique used in the article according to the wikipedia
I want to "simulate" mapreduce for a software assignment using TBB, the pipeline paradigm seems like a good way to see it, since serial filters could be I/O, and parallel ones could be Map and Reduce implementations, however this function implementations receive and return a single elements (this is ok for map if just one tuple is generated by input, but how about something like word counting that need multiple output?), and reduce simply aggregates on a global hashmap without actually returning "something"
Is there a way to use pipeline for this purpose, or I should use something like parallel_while/for?
Thanks!
The parallel pipeline generally does not scale as well as parallel_for, so I would be inclined to try to use parallel_for or some parallel recursive scheme. I recommend looking at parallel sort algorithms for guidance, since map-reduce is quite similar to a sort, except that duplicate keys are merged. For small core counts, something similar to parallel sample sort seems like good inspiration. (See http://parallelbook.com/sites/parallelbook.com/files/code20131121.zip for an implementation in in TBB). For large core counts, something similar to parallel merge sort might be better (see https://software.intel.com/en-us/articles/a-parallel-stable-sort-using-c11-for-tbb-cilk-plus-and-openmp for a discussion and code).
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