Unstable Algorithms which become stable when hardware provides Kulisch exact dot product instruction

In John Gustaffson's book The End of Error, he discusses Ulrich Kulisch's exact dot product, which (in double precision) requires a 2100 bit fixed point register which rounds only once after the computation of the dot product is complete.

I have found only one statement about how this instruction can be used in numerical analysis, namely Kulish claims "it is the EDP which makes residual correction effective . . " However, I could not find a reference demonstrating this claim.

What numerical methods would benefit from an exact dot product? And more interestingly, are there algorithms which become stable in the presence of a hardware dot product which are unstable otherwise?

• I think the classical Gram-Schmidt algorithm is one of them. Except for some simple cases, it is not stable under floating point arithmetic, but the issue can be fixed by reordering the operations -called Modified Gram-Schmidt- (math.stackexchange.com/a/1676362). I don't know many algorithms that fail only due to errors coming from dot products, but it is a source of error and getting rid of a source of error is always good. May 29 at 15:37
• The poster has the sentence "This has a direct and positive influence on all iterative solvers of systems of equations." so this would actually be beneficial for all Krylov subspace methods as an example. May 29 at 15:39
• I know I am putting a lot of comments and probably I should combine all to an answer; but here are more links that are relevant: blogs.mathworks.com/cleve/2015/02/16/… and blogs.mathworks.com/cleve/2015/03/02/… May 29 at 15:56
• @AbdullahAliSivas: Nice link! Cleve is doing essentially the same thing in software as Kulisch wanted in hardware. May 29 at 16:42
• The poster has a very simple example that fails under double precision arithmetic, but is stable under exact dot products: $x_{n+1}=3.75 x_n (1-x_n), x_0 = 0.5$ May 30 at 2:54