kernelopts(numcpus=1);
# setting numcpus to 1 is not sufficient to turn off multicores for LinearAgebra which is an external library
# In Unix use  setenv OMP_NUM_THREADS 1  in the shell

U := rand(10^10):
F := proc() Float(U(),-10) end: # create 10 digit float on [0,1)
with(linalg):
with(LinearAlgebra):

for n in [50,100,200] do

printf("n=%d\n\n",n);
A := matrix(n,n,F):
B := matrix(n,n,F):
b := vector(n,F):
C := augment(A,b):

# Using the old linalg package with software floats
CodeTools[Usage](rref(C)):
CodeTools[Usage](linsolve(A,b)):
CodeTools[Usage](multiply(A,B)):
CodeTools[Usage](singularvals(A)):
CodeTools[Usage](eigenvalues(A)):

printf("LinearAlgebra\n");

# Using the new Linear Algebra package with hardware floats
A := Matrix(convert(A,listlist),datatype=float[8]):
B := Matrix(convert(B,listlist),datatype=float[8]):
b := Vector(convert(b,list),datatype=float[8]):
C := <A|b>:

CodeTools[Usage](ReducedRowEchelonForm(A)): # slow?
# Run these 100 times to get an accurate timing
CodeTools[Usage](to 100 do LinearSolve(A,b) od):
CodeTools[Usage](to 100 do A.B od):
CodeTools[Usage](to 100 do SingularValues(A) od):
CodeTools[Usage](to 100 do Eigenvalues(A) od);

od: