## Simulating Low Precision Floating-Point Arithmetics

In earlier blog posts, I wrote about the benefits of using half precision arithmetic (fp16) and about the problems of overflow and underflow in fp16 and how to avoid them. But how can one experiment with fp16, or other low precision formats such as bfloat16, in order to study how algorithms behave in these arithmetics? (For an accessible introduction to fp16 and bfloat16 see the blog post by Nick Higham.)

As of now, fp16 is supported by several GPUs, but these are specialist devices and they can be very expensive. Moreover, architectures that support bfloat16 have not yet not been released. Therefore software that simulates these floating-point formats is needed.

In our latest EPrint, Nick Higham and I investigate algorithms for simulating fp16, bfloat16 and other low precision formats. We have also written a MATLAB function chop that can be incorporated into other MATLAB codes to simulate low precision arithmetic. It can easily be used to study the effect of low precision formats on various algorithms.

Imagine a hypothetical situation where the computer can just represent integers. Then the question is how do we represent numbers like 4/3? An obvious answer would be to represent it via the integer closest to it, 1 in this case. However, one will have to come with a convention to handle the case where the number is in the centre. Now replace the integer in the example with floating-point numbers, and a similar question arises. This process of converting any given number to a floating-point number is called rounding. If we adopt a rule where we choose the closest floating-point number (as above), then we formally call it as ‘round to nearest’. There are other ways to round as well, and different rounding modes can yield different results for the same code. However meddling with the parameters of a floating-point format without a proper understanding of their consequences can be a recipe for disaster. Cleve Moler in his blog on sub-normal numbers makes this point by warning ‘don’t try this at home’. The MATLAB software we have written provides a safe environment to experiment with the effects of changing any parameter of a floating-point format (such as rounding modes and support of subnormal numbers) on the output of a code. All the technical details can be found in the Eprint and our MATLAB codes.