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5.0 KiB

# Russian Peasant Multiplication

From Assembly to Basic to C to Javascript!

Here are my implementations of Russian Peasant Multiplication implemented in various languages:

- 6502 Assembly Language (Both ca65 and merlin32 sources)
- Applesoft BASIC
- JavaScript (Procedural version)
- JavaScript (OOP version)

A .dsk image has been provided as an convenience.

To see how much faster the Assembly version is then the BASIC version:

```
RUN RPM.BAS
BRUN RPM.BIN
```

And enter in `123456789`

* `987654321`

respectively for A and B ...

Version | Time |
---|---|

Applesoft | 33 s |

Assembly | ~1 s |

# So what the heck is it?

An alternative algorithm to implement multiplication using only:

- bit-shifts (left and right), and
- addition.

# Algorithm

- Initialize Sum <- zero. In C nomenclature:
`Sum = 0;`

- If B is odd then add A to Sum. In C nomenclature:
`Sum += A;`

- Multiply A by 2 -- that is, Shift A
**left**by one. In C nomenclature:`A <<= 1;`

- Divide B by 2 -- that is, Shift B
**right**by one. In C nomenclature:`B >>= 1;`

- If B is zero then STOP.
`while( b ) { ... }`

- Goto step 2

Paste the following program into an online C compiler

```
#include <stdio.h>
int RPM( int a, int b )
{
int sum = 0;
while( b )
{
if( b & 1 )
sum += a;
a <<= 1;
b >>= 1;
}
return sum;
}
int main()
{
return printf( "%d\n", RPM( 86, 57 ) );
}
```

# Examples

Example of "traditional" multiplication:

In base 10:

```
86
x 57
----
602
430
====
4902
```

In base 2:

```
01010110 (86)
00111001 (57)
--------
01010110 (86 * 2^0 = 86)
00000000 (86 * 2^1 = 172) <- wasted work, partial sum = 0
00000000 (86 * 2^2 = 344) <- wasted work, partial sum = 0
01010110 (86 * 2^3 = 688)
01010110 (86 * 2^4 = 1376)
01010110 (86 * 2^5 = 2752)
==============
01001100100110 (4902 = 86*2^0 + 86*2^3 + 86*2^4 + 86*2^5)
```

Example of Russian Peasant multiplication:

In Base 10:

```
A B B Odd? Sum = 0
86 57 Yes + A = 86
x 2 = 172 / 2 = 28 No = 86
x 2 = 344 / 2 = 14 No = 86
x 2 = 688 / 2 = 7 Yes + A = 774
x 2 = 1376 / 2 = 3 Yes + A = 2150
x 2 = 2752 / 2 = 1 Yes + A = 4902
```

In Base 2:

```
A B B Odd? Sum = 0
01010110 00111001 Yes + A = 00000001010110
010101100 00011100 No = 00000001010110
0101011000 00001110 No = 00000001010110
01010110000 00000111 Yes + A = 00001100000110
010101100000 00000011 Yes + A = 00100001100110
0101011000000 00000001 Yes + A = 01001100100110
```

In Base 8:

```
A B B Odd? Sum = 0
126 71 Yes + A = 126
x 2 = 254 / 2 = 34 No = 126
x 2 = 530 / 2 = 16 No = 126
x 2 = 1260 / 2 = 7 Yes + A = 1406
x 2 = 2540 / 2 = 3 Yes + A = 4146
x 2 = 5300 / 2 = 1 Yes + A = 11446
```

In Base 16:

```
A B B Odd? Sum = 0
56 39 Yes + A = 56
x 2 = AC / 2 = 1C No = 56
x 2 = 158 / 2 = E No = 56
x 2 = 2B0 / 2 = 7 Yes + A = 306
x 2 = 560 / 2 = 3 Yes + A = 866
x 2 = AC0 / 2 = 1 Yes + A = 1326
```

# Bases

Does this algorithm work in other bases such as 2, 8, or 16?

Consider the question:

Q. Does multipling by 2 work in other bases? A. Yes.

Q. Why?
A. When we write a number in a different base we have the *same representation* but a *different presentation.*

Adding, Multiplying, Dividing all *function* the same regardless of which base we use.

# Efficiency

For a "BigInt" or "BigNumber" library this *is NOT* the most efficient (*) way to
multiply numbers as it doesn't scale (**). However, it is rather trivial to implement. You only need a few
functions:

`isEven()`

`isZero()`

`Shl()`

`Shr()`

`AddTo()`

Notes:

(*) Almost everyone uses FFT (Fast Fourier Transforms), Toom, and/or Karatsuba for multiplication. For example GMP, GNU Multiple Precision arithmetic library, uses **seven** different multiplication algorithms!

- Basecase
- Karatsuba
- Toom-3
- Toom-4
- Toom-6.5
- Toom-8.5
- FFT

(**) What do we mean by "Doesn't scale"? As the input numbers uses more bits we end up doing more work other other algorithms.