Accelerating data compression using general purpose GPUs
Date Issued
2022
Author(s)
Ristovski, Kristijan
Abstract
The amount of data has been exponentially rising
over the years and data centers have invested heavily in research
for solutions to efficiently transport and store the data. One of
the fields that is crucial to solving this problem is compression
of the generated data to reduce the amount of hardware
needed for storage of said data. Multiple solutions have already
been explored and proposed, and in this paper we experiment
with GPU parallelization of compression algorithms to reduce
the time required to compress data while maintaining the
maximal possible compression ratio. The experiments in this
paper explore the viability of using general purpose GPUs for
accelerating at home data compression, with the possibility of
scaling up to large data centers i.e. using GPU parallelization
to reduce compression times.
over the years and data centers have invested heavily in research
for solutions to efficiently transport and store the data. One of
the fields that is crucial to solving this problem is compression
of the generated data to reduce the amount of hardware
needed for storage of said data. Multiple solutions have already
been explored and proposed, and in this paper we experiment
with GPU parallelization of compression algorithms to reduce
the time required to compress data while maintaining the
maximal possible compression ratio. The experiments in this
paper explore the viability of using general purpose GPUs for
accelerating at home data compression, with the possibility of
scaling up to large data centers i.e. using GPU parallelization
to reduce compression times.
Subjects
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