sparse matmul revisited

In http://yetanothermathprogrammingconsultant.blogspot.com/2009/02/timing-of-sparse-matrix-multiplication.html we time a (sparse and dense) matrix multiplication of two matrices (parameters). In OPL this can look like:

int N = 250; {int} I = asSet(1..N); float A[i in I][j in I] = (i==j)?1:0; float B[i in I][j in I] = (i==j)?1:0; //float A[i in I][j in I] = 1; //float B[i in I][j in I] = 1; float C[i in I][j in I] = sum(k in I) A[i,k]*B[k,j]; float s = sum(i in I, j in I) C[i,j]; execute{   writeln(s); }

The timings are included in the following table:

Model	GNU Mathprog	AMPL	GAMS	OPL
sparse (identity)	50 sec	3 sec	0.1 sec	17 sec
dense (all ones)	50 sec	3 sec	3 sec	17 sec

So in IBM OPL: no sparse processing, and a little bit slower than the other commercial modeling systems (but substantial faster than GNU's Mathprog).