The following example creates fifteen child processes and uses them simultaneously to compute the second integral homology of each of the 2328 groups of order 128. The final command shows that
H_2(G, Z)= Z_2^21
for the 2328-th group G in GAP's library of small groups. The penulimate command shows that the parallel computation achieves a speedup of 10.4 .
gap> Processes:=List([1..15],i->ChildProcess());; gap> fn:=function(i);return GroupHomology(SmallGroup(128,i),2);end;; gap> for p in Processes do > ChildPut(fn,"fn",p); > od; gap> Exec("date +%s");L:=ParallelList([1..2328],"fn",Processes);;Exec("date +%s"); 1716105545 1716105554 gap> Exec("date +%s");L1:=List([1..2328],fn);;Exec("date +%s"); 1716105586 1716105680 gap> speedup:=1.0*(680-586)/(554-545); 10.4444 gap> L[2328]; [ 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2 ]
The function ParallelList()
is built from HAP's six core functions for parallel computation.
The following commands use core functions to compute the product A=M× N of two random matrices by distributing the work over two processors.
gap> M:=RandomMat(10000,10000);; gap> N:=RandomMat(10000,10000);; gap> gap> s:=ChildProcess();; gap> gap> Exec("date +%s"); 1716109418 gap> Mtop:=M{[1..5000]};; gap> Mbottom:=M{[5001..10000]};; gap> ChildPut(Mtop,"Mtop",s); gap> ChildPut(N,"N",s); gap> NextAvailableChild([s]);; gap> ChildCommand("Atop:=Mtop*N;;",s);; gap> Abottom:=Mbottom*N;; gap> A:=ChildGet("Atop",s);; gap> Append(A,Abottom);; gap> Exec("date +%s"); 1716110143 gap> AA:=M*N;;Exec("date +%s"); 1716111389 gap> speedup:=1.0*(111389-110143)/(110143-109418); 1.71862
The next commands compute the product A=M× N of two random matrices by distributing the work over fifteen processors. The parallelization is very naive (the entire matrices M and N are communicated to all processes) and the computation achieves a speedup of 7.6.
gap> M:=RandomMat(15000,15000);; gap> N:=RandomMat(15000,15000);; gap> S:=List([1..15],i->ChildCreate());; gap> Exec("date +%s"); 1716156583 gap> ChildPutObj(M,"M",S); gap> ChildPutObj(N,"N",S); gap> for i in [1..15] do > cmd:=Concatenation("A:=M{[1..1000]+(",String(i),"-1)*1000}*N;"); > ChildCommand(cmd,S[i]); > od; gap> A:=[];; gap> for i in [1..15] do > C:=ChildGet("A",S[i]); > Append(A,C); > od; gap> Exec("date +%s"); 1716157489 gap> AA:=M*N;;Exec("date +%s"); 1716164405 gap> speedup:=1.0*(64405-57489)/(57489-56583); 7.63355
Section 5.8 illustrates an alternative method of computing the persitent Betti numbers of a filtered pure cubical complex. The method lends itself to parallelisation. However, the following parallel computation of persistent Betti numbers achieves only a speedup of 1.5 due to a significant time spent transferring data structures between processes. On the other hand, the persistent Betti function could be used to distribute computations over several computers. This might be useful for larger computations that require significant memory resources.
gap> file:=HapFile("data247.txt");; gap> Read(file);; gap> F:=ThickeningFiltration(T,25);; gap> S:=List([1..15],i->ChildCreate());; gap> N:=[0,1,2];; gap> Exec("date +%s");P:=ParallelPersistentBettiNumbers(F,N,S);;Exec("date +%s"); 1717160785 1717161285 gap> Exec("date +%s");Q:=PersistentBettiNumbersAlt(F,N);;Exec("date +%s"); 1717161528 1717162276 gap> speedup:=1.0*(1717162276-1717161528)/(1717161285-1717160785); 1.496
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