I am trying to re-write in cython a fortran subroutine that uses openmp. I have found no difficulty in re-writing the fortran subroutine itself in cython. The non openmp version works fine. However, I am not sure what to do about the openmp directive....
!$omp parallel do private(x, y, z)
In cython, I understand that you get the openmp parallel do using cython.parallel.prange. However, I don't see how to declare private variables for the loop.
Is this even possible?
thanks for any insight you can provide.
Yes, it is possible. The docs say that:
If you assign to a variable in a prange block, it becomes lastprivate, meaning that the variable will contain the value from the last iteration. ..... Variables assigned to in a parallel with block will be private and unusable after the block, as there is no concept of a sequentially last value.
Related
I am trying to understand the preprocessor directives (like #if , #ifdef, #ifndef) and following is the code I have tried out.
Note: The member functions are further wrapped and used in python and hence the results show the python like calls but this does not affect any of the c++ process.
Question: 1. As per my understanding the global variables have a scope of whole file from the point it is declared. Then in this case, why is the defined value not accepted inside another function?
Requirement: I want to do something like mentioned below:
void Somefunc(int val){
set variable x;
}
Based on the x value, I want to include functions. Now the condition is:
If x=1, only some functions should be compiled since others utilize headers which would throw errors with the compiler I am using.
Thanks in advance!
Preprocessing runs before compilation. It handles the source code as plain text, it doesn't care for C++ language semantics.
The reason why var is not defined is that a preprocessor definition is valid from the point of definition until the end of the file (preprocessed translation unit) or a corresponding #undef.
I'm trying to use openACC to accelerate some of my code. One portion of the code used pow() function from standard library. However there is an error during compilation
PGCC-S-0155-Procedures called in a compute region must have acc routine information: pow
I've roughly know that I need to declare the #pragma acc routine seq in order to remove such type of error. But as I understand this need to be added in the source code of the function(I might be wrong). So how can I work around this?
Include accelmath.h, instead of cmath.h
When using pmap function, an error would occur if the function is not defined on any of the worker processes. However, when the function calls other functions or using other functions inside another .jl file, to use #everywhere macro on every related functions is certainly not a good solution.
Is there a neat way to effectively send a function along with its helpers to all available workers?
I do not think there is a macro that can be used with a function definition to send the definitions of all its helper functions to all the worker processes.
However, there are better ways to send all the functions you need than putting an #everywhere before each of them.
You can put all these functions in a file and include it everywhere with #everywhere include("mynewfile.jl"). If your functions use other functions inside another .jl file, put that include statement for the other file in mynewfile.jl as well. If you are using modules from the other file, put the using or import statements inside mynewfile.jl
In a similar way, instead of a file, you can use #everywhere begin...end block. Put all these functions, using or import statements, includes etc. into a begin...end block and put an #everywhere before the begin. This is especially useful if you are working with IJulia notebooks.
julia> #everywhere begin
g(x) = x^2
f(x) = g(x)*2
end
julia> pmap(f, 1:5)
5-element Array{Int64,1}:
2
8
18
32
50
You can also create modules/packages and just use a single #eveywhere using MyNewModule. If you are using modules outside a package, you should also include the definitions/file of that module everywhere.
You might find it useful to read the relevant manual entry.
I need to refer to the same variable in several functions in a python script. (The write context of a CGPDFDocument).
I can either write global writeContext in every function;
or add the variable as an argument to every function;
....both of which seem to be excessive duplication.
Other answers to questions about global variables in python suggest that they are "bad".
So what's the better way of handling it?
I have heard that MATLAB has an automatic in-need compilation of functions which could create a lot of function-call overhead if you call a function many times like in the following code:
function output = BigFunction( args )
for i = 1:10000000
SmallFunction( args );
end
end
Is it faster to call the function SmallFunction() if I put it in the same file as BigFunction() as a local function? Or is there any good solution other than pasting the code from SmallFunction() into the BigFunction() to optimize the performance?
Edit: It may be false assumption that the function-call overhead is because of the in-need compilation. The question is how to cut down on the overhead without making the code look awful.
Matlab hashes the functions it reads into memory. The functions are only compiled once if they exist as an independent function in its own file. If you put BigFunction in BigFunction.m and SmallFunction in SmallFunction.m then you should recieve the optimization benefit of having the m-script compiled once.
The answer to my first question is that a local function performs the same as a function in another file.
An idea for the second question is to, if possible, make SmallFunction() an inline-function, which has less function-call overhead. I found more about function-call performances in the MathWorks forum, and I paste the question and answer below:
Question:
I have 7 different types of function call:
An Inline function. The body of the function is directory written down (inline).
A function is defined in a separate MATLAB file. The arguments are passed by the calling function (file-pass).
A function is defined in a separate MATLAB file. The arguments are provided by referencing global variables; only indices are provided by the calling function (file-global).
A nested function. The arguments are passed by the enclosing function (nest-pass).
A nested function. The arguments are those shared with the enclosing function; only indices are provided by the enclosing function (nest-share).
A sub function. The arguments are passed by the calling function (sub-pass).
A sub function. The arguments are provided by referencing global variables; only indices are provided by the calling function (sub-global).
I would like to know which function call provides better performance than the others in general.
The answer from MathWorks Support Team pasted here:
The ordering of performance of each function call from the fastest to the slowest tends to be as follows:
inline > file-pass = nest-pass = sub-pass > nest-share > sub-global > file-global
(A>B means A is faster than B and A=B means A is as fast as B)
First, inline is the fastest as it does not incur overhead associated with function call.
Second, when the arguments are passed to the callee function, the calling function sets up the arguments in such a way that the callee function knows where to retrieve them. This setup associated with function call in general incurs performance overhead, and therefore file-pass, nest-pass, and sub-pass are slower than inline.
Third, if the workspace is shared with nested functions and the arguments to a nested function are those shared within the workspace, rather than pass-by-value, then performance of that function call is inhibited. If MATLAB sees a shared variable within the shared workspace, it searches the workspace for the variable. On the other hand, if the arguments are passed by the calling function, then MATLAB does not have to search for them. The time taken for this search explains that type nest-share is slower than file-pass, nest-pass, and sub-pass.
Finally, when a function call involves global variables, performance is even more inhibited. This is because to look for global variables, MATLAB has to expand its search space to the outside of the current workspace. Furthermore, the reason a function call involving global variables appears a lot slower than the others is that MATLAB Accelerator does not optimize such a function call. When MATLAB Accelerator is turned off with the following command,
feature accel off
the difference in performance between inline and file-global becomes less significant.
Please note that the behaviors depend largely on various factors such as operating systems, CPU architectures, MATLAB Interpreter, and what the MATLAB code is doing.