A method, system, and computer program product for data compression in storage clients. In some embodiments, a storage client for accessing a storage service from a computer program is provided. A compression method is provided in the storage client to reduce a size of data objects. A frequency of compressing data from the computer program or modifying a compression algorithm based on assessing costs and benefits of compressing the data is varied.

A system may include a memory device that stores parameters of a layer of a neural network that have been compressed. The system may also include a special-purpose hardware processing unit programmed to, for the layer of the neural network: (1) receive the compressed parameters from the memory device, (2) decompress the compressed parameters, and (3) apply the decompressed parameters in an arithmetic operation of the layer of the neural network. Various other methods, systems, and accelerators are also disclosed.

Aspects of dynamic data compression selection are presented. In an example method, as uncompressed data chunks of a data stream are compressed, at least one performance factor affecting selection of one of multiple compression algorithms for the uncompressed data chunks of the data stream may be determined. Each of the multiple compression algorithms may facilitate a different expected compression ratio. One of the multiple compression algorithms may be selected separately for each uncompressed data chunk of the data stream based on the at least one performance factor. Each uncompressed data chunk may be compressed using the selected one of the multiple compression algorithms for the uncompressed data chunk.

A method includes identifying, by a server module running at a stream data platform, a set of data to be moved to a target data storage tier of the stream data platform, querying, by the server module, service level agreement (SLA) objectives of the target data storage tier, fetching, by the server module, the set of data, selecting, by the server module, a compressor based on the SLA objectives, compressing the set of data using the selected compressor, and storing the set of data in the target data storage tier.

Embodiments include method, systems and computer program products for data compression in storage clients. In some embodiments, a storage client for accessing a storage service from a computer program is provided. A compression method is provided in the storage client to reduce a size of data objects. A frequency of compressing data from the computer program or modifying a compression algorithm based on assessing costs and benefits of compressing the data is varied.

A system having one or more different asymmetric data decompression algorithms and a processor. A first asymmetric data decompression routine is configured to produce decompressed data with a higher data rate for a given data throughput than a second asymmetric data decompression routine of the plurality of different asymmetric data decompression routines. The processor is configured to analyze one or more data parameters from one or more data blocks containing video data and to select two or more different data decompression routines.

Aspects of dynamic data compression selection are presented. In an example method, as uncompressed data chunks of a data stream are compressed, at least one performance factor affecting selection of one of multiple compression algorithms for the uncompressed data chunks of the data stream may be determined. Each of the multiple compression algorithms may facilitate a different expected compression ratio. One of the multiple compression algorithms may be selected separately for each uncompressed data chunk of the data stream based on the at least one performance factor. Each uncompressed data chunk may be compressed using the selected one of the multiple compression algorithms for the uncompressed data chunk.

Data compression and decompression methods for compressing and decompressing data based on an actual or expected throughput (bandwidth) of a system. In one embodiment, a controller tracks and monitors the throughput (data storage and retrieval) of a data compression system and generates control signals to enable/disable different compression algorithms when, e.g., a bottleneck occurs so as to increase the throughput and eliminate the bottleneck.

Aspects of dynamic data compression selection are presented. In an example method, as uncompressed data chunks of a data stream are compressed, at least one performance factor affecting selection of one of multiple compression algorithms for the uncompressed data chunks of the data stream may be determined. Each of the multiple compression algorithms may facilitate a different expected compression ratio. One of the multiple compression algorithms may be selected separately for each uncompressed data chunk of the data stream based on the at least one performance factor. Each uncompressed data chunk may be compressed using the selected one of the multiple compression algorithms for the uncompressed data chunk.

Example embodiments of the present invention relate to methods, systems, and a computer program product for storing data compressed according to a level of activity of a data set. The method includes evaluating a level of activity for a data set and selecting a compression algorithm according to the level of activity of the data set. The data set then may be compressed according to the selected compression algorithm and the compressed data stored in a data storage system.