A data processing device communicating with a memory device via a memory interface includes: at least one data processor configured to generate first data; a data converter configured to generate second data written to the memory device from the first data; and a controller configured to enable the data converter to generate the second data having a size less than that of the first data to reduce power consumption in at least one of the memory device or the memory interface.

A device includes a processor and associated memory; and a compressor for compressing data representing an electronic document, the electronic document comprising a number of objects. The compressor is to determine for each object of the document whether data of that object is to be compressed with lossy or lossless compression and to compress the data accordingly to generate a compressed electronic document.

A computer system includes a plurality of hardware processors, and a hardware accelerator. A first processor among the plurality of processor runs an application that issues a data compression request to compress or decompress a data stream. The hardware accelerator selectively operates in different modes to compresses or decompresses the data stream. Based on a selected mode, the hardware accelerator can utilize a different number of processors among the plurality of hardware to compress or decompress the data stream.

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 computer system includes a plurality of hardware processors, and a hardware accelerator. A first processor among the plurality of processor runs an application that issues a data compression request to compress or decompress a data stream. The hardware accelerator selectively operates in different modes to compresses or decompresses the data stream. Based on a selected mode, the hardware accelerator can utilize a different number of processors among the plurality of hardware to compress or decompress the data stream.

A system orders a set of objects to be optimally compressed using techniques such as delta encoding which compress data by saving differences between sequential objects. In one embodiment, the system determines transition costs associated with transitioning from each object in the set to each other object in the set. The system determines an ordering of the objects that minimizes a total of all the transition costs and compresses the set in the determined order. In another embodiment, the system determines an estimated optimal ordering for data compression. The system extracts feature values from the objects and applies a projection function to the feature values to generate scalar representations of the objects, for example, by projecting the feature values onto a space filling curve. An ordering of the scalar values for the objects determines an ordering for compressing the objects.

An apparatus comprises a first storage system comprising a plurality of storage devices. The first storage system is configured to participate in a replication process with a second storage system. The first storage system is further configured to identify data to be replicated to the second storage system as part of the replication process, to obtain information characterizing network behavior of at least one network connecting the first storage system to the second storage system, to select a compression method from a set of available compression methods based on the obtained information characterizing the network behavior of said at least one network, to compress the data to be replicated to the second storage system utilizing the selected compression method, and to provide the compressed data to the second storage system.

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.

A method for storage system data aware compression, the method may include pre-compressing data units received by the storage system, by different pre-compression units to provide different pre-compressed versions of the data units; wherein the different pre-compression schemes are associated with different compression schemes, wherein at least some of the different compression schemes are data type specific compression schemes; calculating entropies of the different pre-compressed versions; and selecting a compression scheme out of the different compression schemes based on the entropies of the different pre-compressed versions.