  A novel inline data compression method is presented for single-precision
vectors in three dimensions. The primary application of the method is for
accelerating computational physics calculations where the throughput is bound
by memory bandwidth. The scheme employs spherical polar coordinates, angle
quantisation, and a bespoke floating-point representation of the magnitude to
achieve a fixed compression ratio of 1.5. The anisotropy of this method is
considered, along with companding and fractional splitting techniques to
improve the efficiency of the representation. We evaluate the scheme
numerically within the context of high-order computational fluid dynamics. For
both the isentropic convecting vortex and the Taylor--Green vortex test cases,
the results are found to be comparable to those without compression.
Performance is evaluated for a vector addition kernel on an NVIDIA Titan V GPU;
it is demonstrated that a speedup of 1.5 can be achieved.
