The algorithm here, fast doubling, is sequential (there is no parallelism) and is quite fast, with less than a hundred operations to arrive to the final result. This runs in nanosecond on a CPU and the benchmark in the article is mostly measuring the communication time rather than actual computation time.
They also do not make use of the fact that matrix multiplication is associative, so M⁴ = M² × M², M⁸ = M⁴ × M⁴, etc. That means one can compute F32 using 5 matrix multiplications. This uses 31.
It wouldn’t surprise me at all if doing those 5 matrix multiplications on a CPU were faster than doing 31 on the GPU.
Also, FTA: “To calculate extremly large fibonacci numbers we need to avoid integer overflow. A simple way to avoid that is to just take everything modulo a given number in the matrix multiplication“
⇒ This also is NOT calculating the Fibonacci numbers, copping out when things get difficult on a GPU.
If you want to compute Fn mod some constant for 1 ≤ n ≤ some large constant fast on a GPU, I think I would compute quite a few of the intermediate matrices using the fast way, then spin up some ‘threads’ to compute the intermediate versions by copying those with the ‘standard’ matrix.
> Using the power of GPUs we are able to calculate F99999999 in only 17 milliseconds on my Consumer GPU
FWIW, on my 2020 era laptop, the following:
#include <stdio.h>
int main(void) {
int a = 1, b = 1, c, n = 99999999;
while (--n) {
c = (a+b) % 9837;
a = b;
b = c;
}
printf("%d\n", a);
return 0;
}
takes 330 ms measured as "time a.out".
The problem with Fibonacci is there are algorithmic ways to speed things up. The following (using the method at https://en.wikipedia.org/wiki/Fibonacci_sequence to compute Fibonacci numbers recursively in O(log n) arithmetic operations) takes Python 0.1 ms to compute the same value:
import functools
@functools.cache
def F(n):
if n <= 2:
return 1
m = n // 2 # rounds down
if n % 2:
# odd
return (F(m+1)**2 + F(m)**2) % 9837
else:
return ((2*F(m+1) - F(m))*F(m)) % 9837
print(F(99999999))
The algorithm here, fast doubling, is sequential (there is no parallelism) and is quite fast, with less than a hundred operations to arrive to the final result. This runs in nanosecond on a CPU and the benchmark in the article is mostly measuring the communication time rather than actual computation time.
They also do not make use of the fact that matrix multiplication is associative, so M⁴ = M² × M², M⁸ = M⁴ × M⁴, etc. That means one can compute F32 using 5 matrix multiplications. This uses 31.
It wouldn’t surprise me at all if doing those 5 matrix multiplications on a CPU were faster than doing 31 on the GPU.
Also, FTA: “To calculate extremly large fibonacci numbers we need to avoid integer overflow. A simple way to avoid that is to just take everything modulo a given number in the matrix multiplication“
⇒ This also is NOT calculating the Fibonacci numbers, copping out when things get difficult on a GPU.
If you want to compute Fn mod some constant for 1 ≤ n ≤ some large constant fast on a GPU, I think I would compute quite a few of the intermediate matrices using the fast way, then spin up some ‘threads’ to compute the intermediate versions by copying those with the ‘standard’ matrix.
The matrix in question is tiny (2x2). There's no hope a GPU would ever outperform a CPU on that.
It only gets interesting if you need a large matmuls or millions of small matmuls in parallel.
> Using the power of GPUs we are able to calculate F99999999 in only 17 milliseconds on my Consumer GPU
FWIW, on my 2020 era laptop, the following:
takes 330 ms measured as "time a.out".The problem with Fibonacci is there are algorithmic ways to speed things up. The following (using the method at https://en.wikipedia.org/wiki/Fibonacci_sequence to compute Fibonacci numbers recursively in O(log n) arithmetic operations) takes Python 0.1 ms to compute the same value:
Maybe a better test is digits of Pi?
Or recursive hashing?
A test of what?
Test of your GPU approach/library versus CPU.