Hybrid-Smash: A Heterogeneous CPU-GPU Compression Library

Hybrid-Smash: A Heterogeneous CPU-GPU Compression Library

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Compression algorithms are widely used to reduce data size and improve application performance.Nevertheless, data compression has a computational cost which can limit its use.GPUs could be leveraged to reduce gildan cobalt compression time.However, existing GPU-based compression libraries expect data to compress in GPU memory, although it is usually stored in CPU memory.

Additionally, setup time of GPUs could be a problem when compressing small data sizes.In this paper, we implement a new GPU-based compression library.Contrary to existing ones, our library uses data located in CPU memory.Performance results show that, for the same compression algorithms, GPUs a&d ej-123 are beneficial for larger data sizes whereas smaller data sizes are compressed faster using CPUs.

Therefore, we enhance our proposal with Hybrid-Smash: a heterogeneous CPU-GPU compression library, which transparently uses CPU or GPU compression depending on data size, thus improving compression for any data size.