Finite

0x2.0-1

Announcements

Welcome to Computing Security
- Composite multi-session lecture.
Action Items:
- Broadly supports SHA, RSA, and mixed precision arithmetic, concurrent assignments with this lecture.

Apocryphal Quote

I cannot find it, but I believe a philosopher one jested:

I am a weapons-grade finitist. I don’t believe in numbers larger than two.

Arrays of such numbers are sufficient for computation of arbitrary precision.
We can not capture the infinite, but we may model it.

These Slides

Reintroduce the technologies
- stdint
New
- Finite sets, rings
- Arbitrary/high precision integers
- Arithmetic operations
- Function types

What are integers?

Denote integers as the mathematical symbol $\mathbb{Z}$
- Something to do with the German/Deutsch
Not particular useful in cryptography, actually
- We tend to want the naturals, denoted $\mathbb{N}$
- Counting numbers, $0$ and up.

In Python

We can write them in Python with itertools

fields.py

from itertools import count
N = count()
make_z = lambda n : n // 2 if n % 2 else -n // 2
Z = (make_z(n) for n in count(1))
# We can see elements of these infinite collections with
[next(N) for _ in range(5)], [next(Z) for _ in range(5)]

What do you see?

([0, 1, 2, 3, 4], [0, -1, 1, -2, 2])

Aside

It is moderately controversial to assert: \[ 0 \in \mathbb{N} \]
- Enderton, Herbert B. (1977). Elements of set theory. New York: Academic Press. p. 66. ISBN 0122384407.
Fortunately this is a CS class.

assert(0 in count())

$\mathbb{N}$ is akin to `uint`

The natural numbers $\mathbb{N}$ probably look a lot like the unsigned integers.
There’s only one real problem.
We don’t have the unsigned integers in C
- We don’t have them in Python either, but for a different reason.

The Problem

Python has been lying to us for years that its integer have no upper limit.
- We say $\nexists n : n \notin$ count()
- Let’s just try a reasonably sized number, say googolplex $= 10^{10^{100}}$

Test with `-c`

We use the -c flag to Python to run a script directly at command line.

python3 -c "print('hello world')"

Perform multi-line calculuations using ;
- Oh - like C. 🤔

python3 -c "from itertools import count; print(0 in count())"

Time it

Running on Linux
- podman start -l ; podman exec -it -l
We can use time to… time things.

$ time python3 -c "from itertools import count; print(0 in count())"
True
0.01user 0.00system 0:00.01elapsed 109%CPU (0avgtext+0avgdata 8748maxresident)k
0inputs+0outputs (0major+942minor)pagefaults 0swaps

Pivot to bash

Looks nicer in /bin/bash

$ /bin/bash
user@DESKTOP-THMS2PJ:~/tmp$ time python3 -c "from itertools import count; print(0 in count())"
True

real    0m0.013s
user    0m0.014s
sys     0m0.000s

We could argue it takes .013 seconds to check
Or .013 seconds to find itertools on a SDD

Bigger Numbers

Check e.g. 1000

$ /bin/bash
user@DESKTOP-THMS2PJ:~/tmp$ time python3 -c "from itertools import count; print(1000 in count())"
True

real    0m0.012s
user    0m0.012s
sys     0m0.000s

Trivial.

Test it

Try a few powers yourself. What do you find?

time python3 -c "from itertools import count; n=1; print(10**n in count())"

10^$n$	`real`
1	00.012
2	00.012
3	00.012
4	00.013
5	00.013
6	00.027
7	00.164
8	01.498
9	16.810

Expect: \[ t(10^{10}) \in [140,170] \]
Expect for $n \gt 9$ \[ t(10^n) \in [14,17] \times 10^{n-9} \]
Expect $10^{10^{100}}$ in
- (Googol - 9) seconds
- 316 novemvigintilion years

C for Clear

C is a little more forthright about how big its numbers get.

max.c

#include <stdio.h>
#include <stdint.h>

int main() {
    uint32_t n = 0;
    while ((n + 1) > n) {
        n++;
    }
    printf("n = %u\n", n);
    return 0;
}

C fast

For me, this took 5.19s on uint32_t
- Same value in Python was 1m9.32s
How long for uint64_t?
- In Python?
How would speed up the code?
- What assumptions did you make?
How many base 10 digits does the largest number we can store in 32 bytes have?

limits.h

C discloses the maximum values it can tolerate in <limits.h>

num.c

#include <stdio.h>
#include <limits.h>

int main() {
    printf("n = %u\n", UINT_MAX);
    return 0;
}

The correct answer was binary search.
- The incorrect answer (so far) was subtraction.

Rings

As far as I know (not a mathematician) the uints and ints in C are rings
- They have addition and multiplication
They aren’t fields - zero is divisible
- Spoiler alert, but $2^{\frac{n}{2}} \times 2^{\frac{n}{2}} \equiv 0 \pmod{2^n}$

Rings vs Integers

Rings have some “goofy” features
- $a, b \in$ uint$n$_t $\nRightarrow a + b > a$
- Same with multiplication.
Let’s look at an example.

Checkers

num.c

#include <stdlib.h>
#include <stdint.h>

int main(int argc, char **argv) {
    uint8_t x, y;
    x = atoi(argv[1]);
    y = atoi(argv[2]);
    return x + y;
}

atoi - alphabetical to int
From <stdlib.h> per man atoi, but might work without that.

Do some additions

$  ./a.out 100 100 ; echo $?
200
$ ./a.out 200 100 ; echo $?
44

44? From whence?

uint8_t is finite

>>> 200 + 100
300
>>> 2 ** 8
256
>>> 300 - 256
44
>>> 300 % 256
44

Operations on uint8_t values are equivalent to operations on the natural numbers modulo $2^8$ \[ \mathbb{N}/(2^8) \]

In practice

$\exists$ INT_MAX, UINT_MAX, CHAR_MAX, etc.
- They use C names, not stdint names
Sums and products less than these values are unaffected
Sums and products greater than these values are taken modulo said max.
$\forall a, b \in$ uint$n$_t $: a + b \equiv a + b \pmod {2^n}$

Problem Statement

Quoth GitHub

ssh-keygen -t rsa -b 4096 -C "your_email@example.com"

Wait a minute.
What does that 4096 stand for?
- For what does that 4096 stand?

4096 bits

Modern security recommendations are for 4096 bit cryptographic keys.
- 2048 is generally considered “okay” or “acceptable”

NAME
     ssh-keygen — OpenSSH authentication key utility

SYNOPSIS
     ssh-keygen [-q] [-a rounds] [-b bits]

These Slides

✓ Reintroduce the technologies
- ✓ stdint
New
- ✓ Finite sets, rings
- Arbitrary/high precision integers
- Arithmetic operations
- Function types

Big Values

Soon, we will implement RSA
- We’ll talk about what it is then.
First, we need a way to deal with integers that big.
We will use modular arithmetic.
- Finite uint64_t models infinite $\mathbb{N}$

Simple Example

Recall this example from a data application.
We had a data set for which we determined a mean height in inches.
We converted it to inches and feet.

>>> 69.3 // 12, 69.3 % 12
(5.0, 9.299999999999997)

Addition is easy

WNBA MVP and Olympic Gold Medalist A’ja Wilson is 6 ft 4 in
How much taller is that than 5 ft 9.3 in
- Can convert to non-integer inches, but…
- We already had the .299… problem

Difference

We perform “long subtraction”
It’s fun!

	ft	in.	.in
A’ja	6	4	0
Mean	5	9	3

Difference

$0 - 3 \equiv 7 \pmod{10}$
Tenths of inches

	ft	in.	.in
A’ja	6	4	0
Mean	5	9	3
Diff			7

Difference

But wait - 3 is more than 0
Track via a “carry”

	ft	in.	.in
A’ja	6	4	0
Mean	5	9	3
Carry	0	1	0
Diff			7

Difference

$4 - 9 - 1 \equiv 6 \pmod{12}$
12 in = 1 ft

	ft	in.	.in
A’ja	6	4	0
Mean	5	9	3
Carry	0	1	0
Diff		6	7

Difference

Another carry.
12 in = 1 ft

	ft	in.	.in
A’ja	6	4	0
Mean	5	9	3
Carry	1	0	0
Diff		6	7

Difference

$6 - 5 - 1 = 0$
Nonmodular - feet has no max.

	ft	in.	.in
A’ja	6	4	0
Mean	5	9	3
Carry	1	0	0
Diff	0	6	7

Conclusion

Iconic living legend A’ja Wilson is tall af.
We can do addition and substraction on larger values than UINT_MAX or ULONG_MAX by:
- Breaking numbers in smaller ranges
  - A tenths digit
  - A ones digit
  - A twelves digit

Exercise

Make a height adder, try man scanf

hadder.c

#include <stdlib.h>
#include <stdint.h>
#include <stdio.h>

int main() {
        int buf[6];
        printf("Insert 2 heights as XftY.Zin, each on their own line\n");
        scanf("%dft%d.%din", &buf[0], &buf[1], &buf[2]);
        scanf("%dft%d.%din", &buf[3], &buf[4], &buf[5]);

        printf("%dft%d.%din\n", buf[0], buf[1], buf[2]);

        return 0;
}

Usefulness

We can now do arithmetic correctly
- @Python
What else can we do?
- Arbitrary (not infinite) precision.

FAQ

Can we use this to add numbers bigger than $2^n$ using adds over at most $n$ bits at a time?
- Sure! Change the modulos and you’re set.
  - Get it? Because the numbers form a set?
Can we do this for more than 3 fields?
- Sure! Just put the middle (both consumes and produces a carry bit) in a loop!

These Slides

✓ Reintroduce the technologies
- ✓ stdint
New
- ✓ Finite sets, rings
- ✓ Arbitrary/high precision integers
- Arithmetic operations
- Function types

Easy Mode

Addition and subtraction are easy.
For some value of easy.
- Cut a too-big number into chunks.
- Add or subtract within chunks of the same index/offset/significance.
- Only wrinkle is a carry bit.
Identical to digit-based addition.
- uint8_ts as digits in base 256 arithmetic

Hard Mode

Some cryptographical algorithms, however, use two extremely advanced arithmetic operations:
- Multiplication, and
- Division, and
- Modulo
Fortunately this only two operations (need a combined divmod)

Example

Imagine an engineering team lead for:
- 14 MS-level Computer Scientists
  - 9-12 hrs/wk
  - 14 week contract
- 34 BS-level Computer Scientists
  - 6-9 hrs/wk
  - 15.5 week contract
How many person hours is this?

Napkin Math

I’d say
- $14 \times 12 \times 14$
- $34 \times 9 \times 15.5$
I… can’t quite do that in one fell swoop.
- $14 \times 12$ is trivially $12^2 + 24 = 168$
- $9 \times 15.5$ is trivially $155 - 15.5 = 139.5$

$34 \times 139.5$

That just isn’t easy
(140 * 34 isn’t bad, but we need a motivating example).
Express digit-wise:

	1	3	9	5
3	-	-	-	-
4	-	-	-	-

$34 \times 139.5$

Compute all products over single-digit factors

	1	3	9	5
3	3	9	27	15
4	4	12	36	20

These:
- Aren’t single digit
- Aren’t of the same signficance

$34 \times 139.5$

Include sigificance

	100	30	9	.5
30	3000	900	270	15
4	400	120	36	2

Dear Watson

	100	30	9	.5
30	3000	900	270	15
4	400	120	36	2

\[ \begin{align*} 5& \times 4 \times 10^{-1} &= 2&\\ +5& \times 3 \times 10^{0} &= 15&\\ +9& \times 4 \times 10^{0} &= 36&\\ +9& \times 3 \times 10^{1} &= 270&\\ +3& \times 4 \times 10^{1} &= 120&\\ +3& \times 3 \times 10^{2} &= 900&\\ +1& \times 4 \times 10^{2} &= 400&\\ +1& \times 3 \times 10^{3} &= 3000&\\ \end{align*} \]

Express as

\[ \begin{align*} 139.5& = &1 * 10^2 + &3 * 10^1 + &9 * 10^0& + 5 * 10^{-1}\\ 34& = &&3 * 10^1 + &4 * 10^0&\\ \end{align*} \]

Take $x = 10$ \[ \begin{align*} 139.5& = &1 * x^2 + &3 * x + &9& + 5 * x^{-1}\\ 34& = &&3 * x + &4&\\ \end{align*} \]
That is polynomial; can work with those.

Polynomial

\[ (x^2 + 3x + 9 + 5x^{-1})(3x + 4) \]

\[ (x^2 + 3x + 9 + 5x^{-1})(3x) + (x^2 + 3x + 9 + 5x^{-1})(4) \]

\[ (3x^3 + 9x^2 + 27x + 15) + (4x^2 + 12x + 36 + 20x^{-1}) \]

\[ 3x^3 + 13x^2 + 39x + 51 + 20x^{-1} \]

Aside

I think this is covered around ~8th grade
I don’t want to assume the integrity to US public school system
- Or anything school system
- Shout out school
The point of this class isn’t middle/high school math
- That’s the point of life itself /s

Considerations

It is natural to express multiplication of e.g. 4096 bit integers as a polynomial over, say, 64 bit integers.
One teeny problem:
- Overflow.

Overflow

The point of calculating this was to get things down to single digit:

\[ 3x^3 + 13x^2 + 39x + 51 + 20x^{-1} \]

13, 39, 51, and 20 are all not compliant (debatably 20 is okay)
Essentially, 1-digit multiply may produce a 2-digit product.

Uh oh

/bin/sh

$ cat num.c
#include <stdlib.h>
#include <stdint.h>

int main(int argc, char **argv) {
    uint8_t x, y, z;
    x = atoi(argv[1]);
    y = atoi(argv[2]);
    z = x * y;
    return z;
}
$ gcc num.c ; ./a.out 30 30 ; echo $?
132
$ gcc num.c ; ./a.out 3 3 ; echo $?
9
$ python3 -c "print(30 * 30 % 2 ** 8)"
132

Size of ints

Say we have two integers of 8 bits of precision.
We multiple them together.
What is the largest number we can get, and
How many bits does it require?

python3 -c "x = 2 ** 8 - 1 ; x = x * x ; print(x.bit_length())"

Carrys for mults

When we are doing big multiplications:
- We must multiply chunks of at most half the size of our biggest integer.
- We must keep track of significance - their position in an imagined larger integer
- We must perform adds over these terms, using big addition

One Technique

I Made a pointer to a uint32_t
- uint32_t *alias;;
Set it equal to the location of some array of uint64_t
- uint32_t *alias = &array;
Copied elements of the “alias” into 64 bit values.
- uint64_t tmp = alias[5];
Multiplied, tracking significance.

Division

Remember long division?
- Align the highest digits.
  - I took the log base 2.
- Divide.
- Keep track of significance.
  - Difference between logs.
- Calculate remainder.
- Loop.
The final remainder is the mod.

These Slides

✓ Reintroduce the technologies
- ✓ stdint
New
- ✓ Finite sets, rings
- ✓ Arbitrary/high precision integers
- ✓ Arithmetic operations
- Function types

A 4096 bit type

It’s not too bad to pass around 4096 bits.
- An array of 64 uint64_ts or 128 uint32_ts
Here’s an example function declaration:

bigadd

uint64_t bigadd(uint64_t *in0, uint64_t *in1, uint64_t *sum);

Usage

Before call this function, declare three arrays:

uint64_t in0[64], in1[64], sum[64];
bigadd(in0,in1,sum);

You can recycle the arrays, as needed.
- An astute student would be able to implement “in-place add”
- Sum overrights (either) operand
```
uint64_t in0[64], in1[64], sum[64];
bigadd(in0,in1,in0);
```

Usage

Remember this is banned:

uint64_t *whatever(uint64_t *in0, uint64_t *in1) {
    uint64_t sum[64];
    /* much code */
    return sum;
}

My uint64_t value returned the carry value (it was nice to keep track of)

FAQ

Isn’t there a way to return the value from the function, rather than write a value to the memory location at a provided argument.
- Yes.
- This form of coding we use here is memory-safe
- It was endorsed by the White House from 26 Feb 24 to 19 Jan 25
- Read more

FAQ

How do we deal with integers that aren’t of known length.
- Ask me after the midterm.
- Read more
- Read more

These Slides

✓ Reintroduce the technologies
- ✓ stdint
New
- ✓ Finite sets, rings
- ✓ Arbitrary/high precision integers
- ✓ Arithmetic operations
- ✓ Function types

Stinger

/* print the big value as a string */
void seebig(uint64_t *a) {
    size_t i;
    for (i = S-1; i < S ; i--) {
        printf("%016lx ", a[i]); 
        if ((i % 8 == 0 && i)) {
            printf("\n");
        }       
    }
    printf("\n\n");
    return;
}

--- title: Finite subtitle: "0x2.0-1" execute: echo: false --- # Announcements - **Welcome** to Computing Security - Composite multi-session lecture. - **Action Items**: - Broadly supports SHA, RSA, and mixed precision arithmetic, concurrent assignments with this lecture. # Apocryphal Quote - I cannot find it, but I believe a philosopher one jested: > I am a weapons-grade finitist. I don't believe in numbers larger than two. - Arrays of such numbers are sufficient for computation of arbitrary precision. - We can not capture the infinite, but we may model it. # These Slides - Reintroduce the technologies - `stdint` - New - Finite sets, rings - Arbitrary/high precision integers - Arithmetic operations - Function types # What are integers? - Denote integers as the mathematical symbol $\mathbb{Z}$ - Something to do with the German/Deutsch - Not particular useful in cryptography, actually - We tend to want the naturals, denoted $\mathbb{N}$ - Counting numbers, $0$ and up. # In Python - We can write them in Python with `itertools` ```{.python filename="fields.py"} from itertools import count N = count() make_z = lambda n : n // 2 if n % 2 else -n // 2 Z = (make_z(n) for n in count(1)) # We can see elements of these infinite collections with [next(N) for _ in range(5)], [next(Z) for _ in range(5)] ``` - What do you see? ```python ([0, 1, 2, 3, 4], [0, -1, 1, -2, 2]) ``` # Aside - It is moderately controversial to assert: $$ 0 \in \mathbb{N} $$ - Enderton, Herbert B. (1977). Elements of set theory. New York: Academic Press. p. 66. ISBN 0122384407. - Fortunately this is a CS class. ```python assert(0 in count()) ``` # $\mathbb{N}$ is akin to `uint` - The natural numbers $\mathbb{N}$ probably look a lot like the unsigned integers. - There's only one real problem. - We don't have the unsigned integers in C - We don't have them in Python either, but for a different reason. # The Problem - Python has been lying to us for years that its integer have no upper limit. - We say $\nexists n : n \notin$ `count()` - Let's just try a reasonably sized number, say googolplex $= 10^{10^{100}}$ # Test with `-c` - We use the `-c` flag to Python to run a script directly at command line. ```python python3 -c "print('hello world')" ``` - Perform multi-line calculuations using `;` - Oh - like C. 🤔 ```python python3 -c "from itertools import count; print(0 in count())" ``` # Time it - Running on Linux - `podman start -l ; podman exec -it -l` - We can use `time` to... time things. ```email $ time python3 -c "from itertools import count; print(0 in count())" True 0.01user 0.00system 0:00.01elapsed 109%CPU (0avgtext+0avgdata 8748maxresident)k 0inputs+0outputs (0major+942minor)pagefaults 0swaps ``` # Pivot to bash - Looks nicer in `/bin/bash` ```email $ /bin/bash user@DESKTOP-THMS2PJ:~/tmp$ time python3 -c "from itertools import count; print(0 in count())" True real 0m0.013s user 0m0.014s sys 0m0.000s ``` - We could argue it takes .013 seconds to check - Or .013 seconds to find itertools on a SDD # Bigger Numbers - Check e.g. 1000 ```email $ /bin/bash user@DESKTOP-THMS2PJ:~/tmp$ time python3 -c "from itertools import count; print(1000 in count())" True real 0m0.012s user 0m0.012s sys 0m0.000s ``` - Trivial. # Test it - Try a few powers yourself. What do you find? ```email time python3 -c "from itertools import count; n=1; print(10**n in count())" ``` # ::::{.columns} :::{.column width="%50"} |10^$n$|`real`| |-|-| |1|00.012| |2|00.012| |3|00.012| |4|00.013| |5|00.013| |6|00.027| |7|00.164| |8|01.498| |9|16.810| ::: :::{.column width="%50"} - Expect: $$ t(10^{10}) \in [140,170] $$ - Expect for $n \gt 9$ $$ t(10^n) \in [14,17] \times 10^{n-9} $$ - Expect $10^{10^{100}}$ in - (Googol - 9) seconds - 316 novemvigintilion years ::: :::: # C for Clear - C is a little more forthright about how big its numbers get. ```{.C filename="max.c"} #include <stdio.h> #include <stdint.h> int main() { uint32_t n = 0; while ((n + 1) > n) { n++; } printf("n = %u\n", n); return 0; } ``` # C fast - For me, this took 5.19s on `uint32_t` - Same value in Python was 1m9.32s - How long for `uint64_t`? - In Python? - How would speed up the code? - What assumptions did you make? - How many base 10 digits does the largest number we can store in 32 bytes have? # limits.h - C discloses the maximum values it can tolerate in `<limits.h>` ```{.C filename="num.c"} #include <stdio.h> #include <limits.h> int main() { printf("n = %u\n", UINT_MAX); return 0; } ``` - The correct answer was binary search. - The incorrect answer (so far) was subtraction. # Rings - As far as I know (not a mathematician) the `uint`s and `int`s in C are *rings* - They have addition and multiplication - They aren't fields - zero is divisible - Spoiler alert, but $2^{\frac{n}{2}} \times 2^{\frac{n}{2}} \equiv 0 \pmod{2^n}$ # Rings vs Integers - Rings have some "goofy" features - $a, b \in$ `uint`$n$`_t` $\nRightarrow a + b > a$ - Same with multiplication. - Let's look at an example. # Checkers ```{.C filename="num.c"} #include <stdlib.h> #include <stdint.h> int main(int argc, char **argv) { uint8_t x, y; x = atoi(argv[1]); y = atoi(argv[2]); return x + y; } ``` - `atoi` - alphabetical to int - From `<stdlib.h>` per `man atoi`, but might work without that. # Do some additions ```email $ ./a.out 100 100 ; echo $? 200 $ ./a.out 200 100 ; echo $? 44 ``` - 44? From whence? # uint8_t is *finite* ```email >>> 200 + 100 300 >>> 2 ** 8 256 >>> 300 - 256 44 >>> 300 % 256 44 ``` - Operations on `uint8_t` values are equivalent to operations on the natural numbers modulo $2^8$ $$ \mathbb{N}/(2^8) $$ # In practice - $\exists$ `INT_MAX`, `UINT_MAX`, `CHAR_MAX`, etc. - They use C names, not `stdint` names - Sums and products less than these values are unaffected - Sums and products greater than these values are taken modulo said max. - $\forall a, b \in$ `uint`$n$`_t` $: a + b \equiv a + b \pmod {2^n}$ # Problem Statement - Quoth GitHub > [`ssh-keygen -t rsa -b 4096 -C "your_email@example.com"`](https://docs.github.com/en/authentication/connecting-to-github-with-ssh/generating-a-new-ssh-key-and-adding-it-to-the-ssh-agent) - Wait a minute. - What does that 4096 stand for? - For what does that 4096 stand? # 4096 bits - Modern security recommendations are for 4096 bit cryptographic keys. - 2048 is generally considered "okay" or "acceptable" ```email NAME ssh-keygen — OpenSSH authentication key utility SYNOPSIS ssh-keygen [-q] [-a rounds] [-b bits] ``` # These Slides - &check; Reintroduce the technologies - &check; `stdint` - New - &check; Finite sets, rings - Arbitrary/high precision integers - Arithmetic operations - Function types # Big Values - Soon, we will implement RSA - We'll talk about what it is then. - First, we need a way to deal with integers that big. - We will use *modular arithmetic*. - Finite `uint64_t` models infinite $\mathbb{N}$ # Simple Example - Recall this example from a data application. - We had a data set for which we determined a mean height in inches. - We converted it to inches and feet. ```python >>> 69.3 // 12, 69.3 % 12 (5.0, 9.299999999999997) ``` # Addition is easy - WNBA MVP and Olympic Gold Medalist A'ja Wilson is [6 ft 4 in](https://en.wikipedia.org/wiki/A%27ja_Wilson) - How much taller is that than 5 ft 9.3 in - Can convert to non-integer inches, but... - We already had the .299... problem # Difference - We perform "long subtraction" - It's fun! | |ft|in.|.in| |-|--|---|---| |A'ja|6|4|0| |Mean|5|9|3| # Difference - $0 - 3 \equiv 7 \pmod{10}$ - Tenths of inches | |ft|in.|.in| |-|--|---|---| |A'ja|6|4|0| |Mean|5|9|3| |Diff| | |7| # Difference - But wait - `3` is more than `0` - Track via a "carry" | |ft|in.|.in| |-|--|---|---| |A'ja|6|4|0| |Mean|5|9|3| |Carry|0|1|0| |Diff| | |7| # Difference - $4 - 9 - 1 \equiv 6 \pmod{12}$ - 12 in = 1 ft | |ft|in.|.in| |-|--|---|---| |A'ja|6|4|0| |Mean|5|9|3| |Carry|0|1|0| |Diff| |6|7| # Difference - Another carry. - 12 in = 1 ft | |ft|in.|.in| |-|--|---|---| |A'ja|6|4|0| |Mean|5|9|3| |Carry|1|0|0| |Diff| |6|7| # Difference - $6 - 5 - 1 = 0$ - Nonmodular - feet has no max. | |ft|in.|.in| |-|--|---|---| |A'ja|6|4|0| |Mean|5|9|3| |Carry|1|0|0| |Diff|0|6|7| # Conclusion - Iconic living legend A'ja Wilson is tall af. - We can do addition and substraction on larger values than `UINT_MAX` or `ULONG_MAX` by: - Breaking numbers in smaller ranges - A tenths digit - A ones digit - A twelves digit # Exercise - Make a height adder, try `man scanf` ```{.C filename="hadder.c"} #include <stdlib.h> #include <stdint.h> #include <stdio.h> int main() { int buf[6]; printf("Insert 2 heights as XftY.Zin, each on their own line\n"); scanf("%dft%d.%din", &buf[0], &buf[1], &buf[2]); scanf("%dft%d.%din", &buf[3], &buf[4], &buf[5]); printf("%dft%d.%din\n", buf[0], buf[1], buf[2]); return 0; } ``` # Usefulness - We can now do arithmetic correctly - @Python - What else can we do? - Arbitrary (not infinite) precision. # FAQ - Can we use this to add numbers bigger than $2^n$ using adds over at most $n$ bits at a time? - Sure! Change the modulos and you're set. - Get it? Because the numbers form a set? - Can we do this for more than 3 fields? - Sure! Just put the middle (both consumes and produces a carry bit) in a loop! # These Slides - &check; Reintroduce the technologies - &check; `stdint` - New - &check; Finite sets, rings - &check; Arbitrary/high precision integers - Arithmetic operations - Function types # Easy Mode - Addition and subtraction are easy. - For some value of easy. - Cut a too-big number into chunks. - Add or subtract within chunks of the same index/offset/significance. - Only wrinkle is a carry bit. - Identical to digit-based addition. - `uint8_t`s as digits in base 256 arithmetic # Hard Mode - Some cryptographical algorithms, however, use two extremely advanced arithmetic operations: - Multiplication, and - Division, and - Modulo - Fortunately this only two operations (need a combined `divmod`) # Example - Imagine an engineering team lead for: - 14 MS-level Computer Scientists - 9-12 hrs/wk - 14 week contract - 34 BS-level Computer Scientists - 6-9 hrs/wk - 15.5 week contract - How many person hours is this? # Napkin Math - I'd say - $14 \times 12 \times 14$ - $34 \times 9 \times 15.5$ - I... can't quite do that in one fell swoop. - $14 \times 12$ is trivially $12^2 + 24 = 168$ - $9 \times 15.5$ is trivially $155 - 15.5 = 139.5$ # $34 \times 139.5$ - That just isn't easy - (140 * 34 isn't bad, but we need a motivating example). - Express *digit-wise*: | |1|3|9|5| |-|-|-|-|-| |3|-|-|-|-| |4|-|-|-|-| # $34 \times 139.5$ - Compute all products over single-digit factors | |1|3|9|5| |-|-|-|-|-| |3|3|9|27|15| |4|4|12|36|20| - These: - Aren't single digit - Aren't of the same signficance # $34 \times 139.5$ - Include sigificance | |100|30|9|.5| |-|-|-|-|-| |30|3000|900|270|15| |4|400|120|36|2| # Dear Watson ::::{.columns} :::{.column width="%50"} | |100|30|9|.5| |-|-|-|-|-| |30|3000|900|270|15| |4|400|120|36|2| ::: :::{.column width="%50"} $$ \begin{align*} 5& \times 4 \times 10^{-1} &= 2&\\ +5& \times 3 \times 10^{0} &= 15&\\ +9& \times 4 \times 10^{0} &= 36&\\ +9& \times 3 \times 10^{1} &= 270&\\ +3& \times 4 \times 10^{1} &= 120&\\ +3& \times 3 \times 10^{2} &= 900&\\ +1& \times 4 \times 10^{2} &= 400&\\ +1& \times 3 \times 10^{3} &= 3000&\\ \end{align*} $$ ::: :::: # Express as $$ \begin{align*} 139.5& = &1 * 10^2 + &3 * 10^1 + &9 * 10^0& + 5 * 10^{-1}\\ 34& = &&3 * 10^1 + &4 * 10^0&\\ \end{align*} $$ - Take $x = 10$ $$ \begin{align*} 139.5& = &1 * x^2 + &3 * x + &9& + 5 * x^{-1}\\ 34& = &&3 * x + &4&\\ \end{align*} $$ - That is polynomial; can work with those. # Polynomial $$ (x^2 + 3x + 9 + 5x^{-1})(3x + 4) $$ $$ (x^2 + 3x + 9 + 5x^{-1})(3x) + (x^2 + 3x + 9 + 5x^{-1})(4) $$ $$ (3x^3 + 9x^2 + 27x + 15) + (4x^2 + 12x + 36 + 20x^{-1}) $$ $$ 3x^3 + 13x^2 + 39x + 51 + 20x^{-1} $$ # Aside - I think this is covered around ~8th grade - I don't want to assume the integrity to US public school system - Or anything school system - Shout out school - The point of this class isn't middle/high school math - That's the point of life itself /s # Considerations - It is natural to express multiplication of e.g. 4096 bit integers as a polynomial over, say, 64 bit integers. - One teeny problem: - Overflow. # Overflow - The point of calculating this was to get things down to single digit: $$ 3x^3 + 13x^2 + 39x + 51 + 20x^{-1} $$ - 13, 39, 51, and 20 are all not compliant (debatably 20 is okay) - Essentially, 1-digit multiply may produce a 2-digit product. # Uh oh ```{.C filename="/bin/sh"} $ cat num.c #include <stdlib.h> #include <stdint.h> int main(int argc, char **argv) { uint8_t x, y, z; x = atoi(argv[1]); y = atoi(argv[2]); z = x * y; return z; } $ gcc num.c ; ./a.out 30 30 ; echo $? 132 $ gcc num.c ; ./a.out 3 3 ; echo $? 9 $ python3 -c "print(30 * 30 % 2 ** 8)" 132 ``` # Size of ints - Say we have two integers of 8 bits of precision. - We multiple them together. - What is the largest number we can get, and - How many bits does it require? ```{.email} python3 -c "x = 2 ** 8 - 1 ; x = x * x ; print(x.bit_length())" ``` - 16 # Carrys for mults - When we are doing big multiplications: - We must multiply chunks of at most half the size of our biggest integer. - We must keep track of significance - their position in an imagined larger integer - We must perform adds over these terms, using big addition # One Technique - I Made a pointer to a `uint32_t` - `uint32_t *alias;`; - Set it equal to the location of some array of `uint64_t` - `uint32_t *alias = &array;` - Copied elements of the "alias" into 64 bit values. - `uint64_t tmp = alias[5];` - Multiplied, tracking significance. # Division - Remember long division? - Align the highest digits. - I took the log base 2. - Divide. - Keep track of significance. - Difference between logs. - Calculate remainder. - Loop. - The final remainder is the mod. # These Slides - &check; Reintroduce the technologies - &check; `stdint` - New - &check; Finite sets, rings - &check; Arbitrary/high precision integers - &check; Arithmetic operations - Function types # A 4096 bit type - It's not too bad to pass around 4096 bits. - An array of 64 `uint64_t`s or 128 `uint32_t`s - Here's an example function declaration: ```{.C filename="bigadd"} uint64_t bigadd(uint64_t *in0, uint64_t *in1, uint64_t *sum); ``` # Usage - Before call this function, declare three arrays: ```{.C } uint64_t in0[64], in1[64], sum[64]; bigadd(in0,in1,sum); ``` - You can recycle the arrays, as needed. - An astute student would be able to implement "in-place add" - Sum overrights (either) operand ```{.C } uint64_t in0[64], in1[64], sum[64]; bigadd(in0,in1,in0); ``` # Usage - Remember this is banned: ```{.C} uint64_t *whatever(uint64_t *in0, uint64_t *in1) { uint64_t sum[64]; /* much code */ return sum; } ``` - My `uint64_t` value returned the carry value (it was nice to keep track of) # FAQ - Isn't there a way to return the value from the function, rather than write a value to the memory location at a provided argument. - Yes. - This form of coding we use here is *memory-safe* - It was endorsed by the White House from 26 Feb 24 to 19 Jan 25 - [Read more](https://web.archive.org/web/20250118013136/https://www.whitehouse.gov/oncd/briefing-room/2024/02/26/press-release-technical-report/) # FAQ - How do we deal with integers that aren't of known length. - Ask me after the midterm. - [Read more](https://gmplib.org/) - [Read more](https://man7.org/linux/man-pages/man3/free.3.html) # These Slides - &check; Reintroduce the technologies - &check; `stdint` - New - &check; Finite sets, rings - &check; Arbitrary/high precision integers - &check; Arithmetic operations - &check; Function types # Stinger ```{.C} /* print the big value as a string */ void seebig(uint64_t *a) { size_t i; for (i = S-1; i < S ; i--) { printf("%016lx ", a[i]); if ((i % 8 == 0 && i)) { printf("\n"); } } printf("\n\n"); return; } ```

Announcements

Apocryphal Quote

These Slides

What are integers?

In Python

Aside

\(\mathbb{N}\) is akin to uint

The Problem

Test with -c

Time it

Pivot to bash

Bigger Numbers

Test it

C for Clear

C fast

limits.h

Rings

Rings vs Integers

Checkers

Do some additions

uint8_t is finite

In practice

Problem Statement

4096 bits

These Slides

Big Values

Simple Example

Addition is easy

Difference

Difference

Difference

Difference

Difference

Difference

Conclusion

Exercise

Usefulness

FAQ

These Slides

Easy Mode

Hard Mode

Example

Napkin Math

\(34 \times 139.5\)

\(34 \times 139.5\)

\(34 \times 139.5\)

Dear Watson

Express as

Polynomial

Aside

Considerations

Overflow

Uh oh

Size of ints

Carrys for mults

One Technique

Division

These Slides

A 4096 bit type

Usage

Usage

FAQ

FAQ

These Slides

Stinger

\(\mathbb{N}\) is akin to `uint`

Test with `-c`