Data compression is the process of representing
information in a compact form, in order to reduce the storage
requirements and, hence, communication bandwidth. It has been
one of the critical enabling technologies for the ongoing digital
multimedia revolution for decades. In the variable-length
encoding (VLE) compression method, most frequently occurring
symbols are replaced by codes with shorter lengths. As it is a
common strategy in many compression applications, efficient
parallel implementations of VLE are very desirable. In this paper
we present CUVLE, a GPU implementation of VLE on CUDA.
Our approach is on average more than 20 and 2 times faster than
the corresponding CPU serial implementation and the only
known state-of-the-art GPU implementation, respectively.
