# Posts by Richard Smith

2 posts • joined 20 Sep 2006

### Appro: HPC's all about the GPUs

#### "Error correction inadequate"

Fixing up bit flips may seem important, but is hardly sufficient for a "serious HPC center" to trust the alleged answers generated. As is well-known, there are many other sources of error: floating-point rounding, measurement error, approximate physical constants, use of discrete models in place of continuous models. I wouldn't trust any answer, regardless of the presence of logic detecting bit-flips, unless the solutions are accompanied by guaranteed bounds on the errors. Now if these new GPUs had hardware implementations of Interval Arithmetic instructions, that might be something to get excited about.

### Floating point numbers - what else can be done?

#### Interval Arithmetic

One of the common uses of floating point arithmetic is in

modelling real-world phenomena. The techniques described in

the article I believe are inadequate to handle some of the

issues that crop up in trying to solve these models. For

example: we often have measurement error; know physical

constants to varying degrees of accuracy; discretize data

that is supposed to represent a continuum; use algorithms

that may not be numerically stable over the entire domain;

or deal with problems that are inherently stiff. The real

world is nonlinear.

There is an alternative to using fixed point schemes

(whether integers, scaled integers, rationals, or floating

point numbers as approximations to a continuum), which is

to compute with sets of numbers. For reasons of efficiency

and to take advantage of hardware acceleration, we

generally use intervals, defined as the set of all numbers

between a lower bound and upper bound [a,b].

By using intervals we can represent measurement error,

floating point rounding error, and imprecise constants in a

unified and consistent way. For some of us, one of the

greatest strengths of this approach is that when you

compute something, you also obtain an indication of the

quality of the answer i.e. the width of the interval.

Some problems have traditionally been considered

intractable, which may no longer be the case when using IA.

Consider a (large) solution space. By eliminating boxes

(multidimensional intervals) where it can be proved the

solution cannot be, you can iterate towards more and more

accurate approximations of the solution, subject to the

precision of the arithmetic being used. As the size of

boxes shrink, switch to higher precision if required. A

classic example of this technique is an Interval Newton

method for finding all roots of a function.

For all of this to work, you do need an implementation of

interval arithmetic, one that guarantees containment of the

true solution for all operator-operand combinations. In my

own work, I use the implementation that is part of Sun's

Fortran compilers.