Compressing Fluid Subspaces

Aaron Demby-Jones, Pradeep Sen, Theodore Kim

Abstract:

Subspace fluid simulations, also known as reduced-order simulations, can be extremely fast, but also require basis matrices that consume an enormous amount of memory. Motivated by the extreme sparsity of Laplacian eigenfunctions in the frequency domain, we design a frequency-space codec that is capable of compressing basis matrices by up to an order of magnitude. However, if computed naively, decompression can be highly inefficient and dominate the running time, effectively negating the advantage of the subspace approach. We show how to significantly accelerate the decompressor by performing the key matrix-vector product in the sparse frequency domain. Subsequently, our codec only adds a factor of three or four to the overall runtime. The compression preserves the overall quality of the simulation, which we show in a variety of examples.