Anyway floating point numerical stability is a different issue: some expressions produce results with less precision than other, different expressions, even though they should be the same thing algebraically (if we were dealing with real numbers or at least rational numbers). The issue here is that FP has limited precision, and it makes it lose some algebraic properties like associativity; and this limited precision sometimes lead to catastrophic cancellation and other problems that sharply reduce the precision (this doesn't happen if you use something with arbitrary precision like rational numbers).
Floating point determinism on the other hand is about always giving you the same result when running the same code (even if you happen to run in a different machine; and that's sometimes challenging because some architectures may not support features like subnormal numbers). It's okay if different code gives different results as far as determinism is concerned.
Anyway floating point numerical stability is a different issue: some expressions produce results with less precision than other, different expressions, even though they should be the same thing algebraically (if we were dealing with real numbers or at least rational numbers). The issue here is that FP has limited precision, and it makes it lose some algebraic properties like associativity; and this limited precision sometimes lead to catastrophic cancellation and other problems that sharply reduce the precision (this doesn't happen if you use something with arbitrary precision like rational numbers).
Floating point determinism on the other hand is about always giving you the same result when running the same code (even if you happen to run in a different machine; and that's sometimes challenging because some architectures may not support features like subnormal numbers). It's okay if different code gives different results as far as determinism is concerned.