The First Test is done with SINGLE Precision, it compares the

MAT - Statement with conventional Loops. The result is surprising.

`%Input=3000`

%hidA=30000

REGISTER R01 as Long, R02 as Long

tix Q1

#if 0

MAT Hidi()=wi()*InpN()

#else

For R01=1 to %hidA

Hidi(R01)=0

For R02=1 to %Input

Hidi(R01)+=wi(R01,R02)*InpN(R02)

next

next r02

#endif

tix END Q1

? STR$(int(Q1/100000))

I get 68057 for the FOR-Loop.

And 79259 for the MAT Statement.

MAT is SLOWER.

With smaller Numbers the effect is even stronger.

Using

`%Input=300`

%hidA=600

I need 66 Tix for MAT and 24 Tix with the FOR-Loop. MAT is slower anyway.

Using `%Input=30`

%hidA=60

...

? STR$(int(Q1/1000))

I need again 66 Tix for MAT and 24 Tix with the FOR-Loop. MAT is generally slower here.

Now switching to Extended-Datatype. Does it change?

MAT uses 73 Tix, the For-Loop just 36 Tix. So what we see is that Extended is 50 slower then SINGLE using a FOR-Loop, but still faster then MAT using SINGLE.

Let's try Double. It comes with 67 Tix for MAT and 22 with the FOR-Loop. So no big difference to SINGLE anymore.

And with higher numbers?

`%Input=300`

%hidA=600

...

? STR$(int(Q1/100000))

We get 26 with the FOR-Loop and we get 72 with MAT.

With SINGLE it was 24/66.

With EXTENDED it was 46/83

It shows that modern CPU's do not have substancial advantages using SINGLE anymore.

Just use DOUBLE. While EXTENDED ist still a bit more costly.

Unless you use MAT then its not such a large difference.

**Conclusion:** For simple MATRIX-Multiplication with 1-dimensional Vectors, MAT is slower then a conventional FOR-Loop by amazing 50%.