p1 := 25000000000000000000*x^24*(234374765625000000*(x^(1/5000000000*a))^609256031*y^9256031 + 7946285261481299*x^12*y^27768093 - 56259267500000000*(x^(1/10000000000*a))^609256031*x^6*z^9256031)^4;

$\mathrm{p1}≔25000000000000000000x^{24}{\left(234374765625000000{\left(x^{\frac{a}{5000000000}}\right)}^{609256031}{y}^{9256031}+7946285261481299x^{12}{y}^{27768093}-56259267500000000{\left(x^{\frac{a}{10000000000}}\right)}^{609256031}{x}^6{z}^{9256031}\right)}^4$

p2 := y^100000000-x;

$\mathrm{p2}≔y^{100000000}-x$

t0 := time():

res := quo(p1, p2, y);

$\mathrm{res}≔99677380232696642539570742968540066928124035645820992834105370025000000000000000000x^{72}{y}^{11072372}$

time_consumed = time()-t0;

$\mathrm{time\_consumed}=0.004$

ee := (x^2+2000*x+1001000)/((x+1001)*(x+1000)*x);

$\mathrm{ee}≔\frac{x^2+2000x+1001000}{\left(x+1001\right)\left(x+1000\right)x}$

t0 := time():

Sum(ee,x) = sum(ee,x);

$\sum _x\frac{x^2+2000x+1001000}{\left(x+1001\right)\left(x+1000\right)x}=\frac{1}{x+1000}+\mathrm{\Psi }\left(x\right)$

time_consumed = time() - t0;

$\mathrm{time\_consumed}=0.045$

M := LinearAlgebra:-RandomMatrix(500);

time_consumed = time( type(M, 'Matrix'(algebraic)) );

$\mathrm{time\_consumed}=0.005$

diff( f(x,y,z,t), x$74, y$143, z$236 );

$\frac{{\partial }^{453}}{\partial x^{74}\partial y^{143}\partial z^{236}}f\left(x\,y\,z\,t\right)$

time_consumed = time( convert( (10), D ) );  # before it was 1/2 minute in typical machines

$\mathrm{time\_consumed}=0.010$

Maple 9.5 for Windows uses version 6.1 of the Intel Math Kernel Library (MKL) resulting in faster computation for large-sized hardware floating-point linear algebra operations.

The following example takes approximately 6.7 seconds in Maple 9 and 4.0 seconds in Maple 9.5 on a 1.7GHz P4 machine running Windows 2000.

M := LinearAlgebra:-RandomMatrix( 1600, outputoptions = [datatype = float[8]] ):

time( M.M ); # see text above for timings

Maple 9.5 for Linux and Macintosh OS X uses tuned versions, from ATLAS, of a select group of CLAPACK functions related to hardware floating-point linear algebra computations, such as LUDecomposition and LinearSolve.

The following example takes approximately 0.66 seconds in Maple 9 and 0.40 seconds in Maple 9.5  on a 2.4GHz P4 machine running Linux. On a G5 machine running OS X 10.3, this example takes approximately 0.43 seconds in Maple 9 and 0.25 seconds in Maple 9.5.

M := LinearAlgebra:-RandomMatrix( 1000, outputoptions = [datatype = float[8]] ):

V := LinearAlgebra:-RandomVector( 1000, outputoptions = [datatype = float[8]] ):

time( LinearAlgebra:-LinearSolve( M, V ) ); # see text above for timings