Efficient Multibyte Floating Point Data Formats using Vectorization

Andrew Anderson, Servesh Muralidharan, David Gregg

ACM Transactions on Computers, 2017

We propose a scheme for reduced-precision representation of floating point data on a continuum between IEEE-754 floating point types. Our scheme enables the use of lower precision formats for a reduction in storage space requirements and data transfer volume. We describe how our scheme can be accelerated using existing hardware vector units on two general-purpose processor (GPP) microarchitectures (Intel Ivy Bridge and Haswell), as well as on a numerical accelerator (Intel Xeon Phi).

Our evaluation demonstrates that supporting reduced precision by exploiting native vector instructions can yield a low overhead custom-precision floating point solution that does not require specialized hardware support. In our experiments we find cases where our scheme is actually faster than native floating point types where the underlying vector instruction set supports efficient byte-level permutations.