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MaplePrimes Activity

These are questions asked by Gillee


I was applying the Grip Map to the procedure LArip. It apparently finished in less than 5 seconds. But when I wanted to display the results as an Array, this step required minutes. How can this time be reduced? Here is a piece of the code:

n := 8;
r := 2^n;
M := Vector(r, randperm(r) + [-seq(1, i = 1 .. r)], datatype = integer[4]);

LArip := proc(k::integer, r::integer, M::Vector(datatype = integer[4]))
local N, j, t, i, A;
N := Vector(r, 0); A := Vector(r, 0);
i := k - 1;
for j from 0 to r - 1 do
for t from 0 to r - 1 do
N[t + 1] := Occurrences(1, Split(Xor(And(t, i), And(M[t + 1], j)))) mod 2;
end do;
A[j + 1] := Occurrences(0, convert(N[], list));
end do;
return convert(A, list);
end proc;

infolevel[Grid:-Map] := 3;
rt := time[real]();
Bb := Grid:-Map(k -> LArip(k, r, M), [$ (1 .. r)]);
GridMapTime := time[real]() - rt;

The worksheet is here:

Thank you for your help.


I was hoping to run two procs: tgf3 and tgf4 in parallel using Grid, Run and get a faster execution time. As I understood the description of Grid Run, the first call to Grid Run will run in the background and before it is finished the second call Grid Run will start. I do not believe I have that situation in my script. I am not understanding Grid Run. How can this problem be fixed? Here is just a portion of the script using Grid Run:

Use Grid Run 0 for tgf3 and 1 for tgf4. Determine the real time and compare times. The Grid Runs do not appear to run in parallel.

rgt := time[real]();
Grid:-Run(0, `~`[tgf3@op](convert(L, listlist)), 'assignto' = ans3roots);
Grid:-Run(1, `~`[tgf4@op](convert(L, listlist)), 'assignto' = ans4roots);

GridRunTime := time[real]() - rgt;
                     GridRunTime := 44.074

Here is my script:


Thank you for your help.


I was able to get Procedure 2 to work using a for - do loop. I was wondering if it is possible to speed up the calculation by using map to find the number of roots? I do not fully understand map and passing data.

tgf := proc(a, b, c, d, t, m, n)

          local X;

          X := [solve(abs(a*x + b) + abs(c*x + d) - t*x^2 + m*x - n = 0)]; 

          return nops(X);

end proc;

res := CodeTools:-Usage(map(tgf, L));
Error, (in CodeTools:-Usage) invalid input: tgf uses a 2nd argument, b, which is missing

The L Array is tripping me up, here is a partial display of the array:

Array(1..262, 1..7, [[5,2,3,9,1,1,1],[5,2,3,9,2,1,1],[5,4,3,7,1,1,1],[5,4,3,7,2,1,1],[5,5,3,6,1,1,1],[5,5,3,6,2,1,1],[5,5,4,8,1,1,2],[5,5,4,8,2,1,2], ... ,[10,10,5,10,2,2,2]], datatype = integer[4]).

I made L Array into a list of list, R. Somewhat works.

Here is the script: 

Thanks for any help.

I was using the DirectSearch:-GlobalSearch function on a LaguerrelL for values of 4 and 5. At a value of 4 I get a solution, but at 5 I get an error message that the Kernel connection is lost. I ran this script on two different PCs using Maple 2019 and 2020 with WIN 10 and get the same results. Why does this happen?


Here is my worksheet. Thanks for looking.



I have noticed a small decrease in real time running 4 cpus versus 1 cpu. I am not sure if implemented the multi-threads  feature correctly or efficiently. Could you please show me some alternative coding methods with even more improvements? Thanks in advance.  


# Generative Model

n_trials := 16:
n_sample := 1:

subscribers := Vector[row](n_draw):
gen_model := proc(ib,ie)
        global n_trials, n_sample, prior_rate;
        local i, Y;
        for i from ib to ie do
          Y := RandomVariable(Binomial(n_trials, prior_rate(i)));
          subscribers(i) := apply(Sample(Y, n_sample), 1):
        end do:
        return subscribers:
end proc:

# Multi-threads, use 4 cpus
gm1 := Create(gen_model(1, n_draw/4), out1):
gm2 := Create(gen_model(n_draw/4 + 1, n_draw/2), out2):
gm3 := Create(gen_model(n_draw/2 + 1, 3*n_draw/4), out3):
gm4 := Create(gen_model(3*n_draw/4 + 1, n_draw), out4):

Usage(Wait(gm1, gm2, gm3, gm4));
memory used=1.97GiB, alloc change=0.52GiB, cpu time=83.56s, real time=45.52s, gc time=6.20s

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