# Solving large linear systems of equations in parallel / distributed systems

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Ben238
on 25 Jan 2023

Commented: Walter Roberson
on 27 Jan 2023

### Accepted Answer

Walter Roberson
on 25 Jan 2023

Gb = 60000^2*8/1024^3

The matrix is close to 27 gigabytes. The inverse is the same size, so you will need at least 54 gigabytes to store the matrix and its inverse.

Suppose you have 60000 cores (actually possible! see https://www.pcgamer.com/this-computer-is-26-inches-tall-and-houses-a-400000-core-processor/ ) and you have each one compute the minors "leave one out" style. Does that reduce the task to 60000 simultaneous computations on a 60000 x 1 matrix? NO, it reduces the task to 60000 simultaneous computations on 59999 x 59999 matrices.

To within round-off error, if you divide the work over N cores, each core still needs to work with an array just fractionally less than the original 27 gigabytes. Even supposing you could array to all use the same output array and even supposing that each core did not need an intermediate array (probably not true), if you had 128 gigabytes of total ram, you would only be able to split into at most 3 cores -- 27 * 3 slightly-different inputs + 27 output. In reality because intermediate arrays would be needed, you would probably not be able to do more than 2 cores in 128 gigabytes.

How large is your computer system? How much RAM? How many cores?

##### 2 Comments

Walter Roberson
on 27 Jan 2023

I do not know whether using a distributed system could potentially be of use in such a situation; it probably could be in some cases, such as if you have a block-diagonal system.

I would point out, though, that the current solvers used by \ call into high-performance libraries that are already multi-core . Some of the steps such as matrix multiplication or dot products can be broken up into pieces and performed over multiple cores, and the solvers invoked by \ already know how to do that.

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