A method of storing data is provided. The method includes receiving a plurality of data blocks provided to a hyperscaler system. The method also includes determining a corresponding property for each data block of the plurality of data blocks. The method further includes identifying a set of data blocks from the plurality of data blocks. Each data block of the set of data blocks is associated with a first property. The method further includes storing the set of data blocks in a first zone of a zoned storage system, based on the first property.

Methods of image compression are described. A stream of color image data is filtered with a prediction routine using a pixel neighborhood. The filtered stream of color image data is sorted with a block sorting routing. A version of the color image data is compressed based on the sorted and filtered stream of color image data.

A database management system is described that can encode data to generate a plurality of data vectors. The database management system can perform the encoding by using a dictionary. The database management system can adaptively reorder the plurality of data vectors to prepare for compression of the plurality of data vectors. During a forward pass of the adaptive reordering, most frequent values of a data vector of the plurality of data vectors can be moved-up in the data vector. During a backward pass of the adaptive reordering, content within a rest range of a plurality of rest ranges can be rearranged within the plurality of data vectors according to frequencies of the content. The reordering according to frequency can further sort the rest range by value. Related apparatuses, systems, methods, techniques, computer programmable products, computer readable media, and articles are also described.

A method for compressing data in a local cache of a web server is described. A local cache compression engine accesses values in the local cache and determines a cardinality of the values of the local cache. The local cache compression engine determines a compression rate of a compression algorithm based on the cardinality of the values of the local cache. The compression algorithm is applied to the cache based on the compression rate to generate a compressed local cache.

Systems, methods, and apparatuses are provided for compressing values. A plurality of parameters may be obtained from a memory, each parameter comprising a floating-point number that is used in a relationship between artificial neurons or nodes in a model. A mantissa value and an exponent value may be extracted from each floating-point number to generate a set of mantissa values and a set of exponent values. The set of mantissa values may be compressed to generate a mantissa lookup table (LUT) and a plurality of mantissa LUT index values. The set of exponent values may be encoded to generate an exponent LUT and a plurality of exponent LUT index values. The mantissa LUT, mantissa LUT index values, exponent LUT, and exponent LUT index values may be provided to one or more processing entities to train the model.

A system for compressing Controller Area Network (CAN) messages, the system comprising a processing resource configured to: obtain a CAN messages sequence including a plurality of CAN messages intercepted at a given order by at least one device adapted to monitor messages transmitted via communication channel(s) of a vehicle; group the CAN messages of the CAN messages sequence into MID groups, by a CAN MID field of the CAN messages; for each given MID group of the MID groups split the CAN messages of the MID group into field groups, wherein each field group comprises a respective field of a plurality of fields of the CAN messages of the MID group; employ at least one compression scheme on at least one of the field groups; generate a data structure comprising the field groups; and compress the data structure using a lossless compression algorithm, giving rise to a compressed data structure.

A computing system and a compressing method for neural network parameters are provided. In the method, multiple neural network parameters are obtained. The neural network parameters are used for a neural network algorithm. Every at least two neural network parameters are grouped into an encoding combination. The number of neural network parameters in each encoding combination is the same. The encoding combinations are compressed with the same compression target bit number. Each encoding combination is compressed independently. The compression target bit number is not larger than a bit number of each encoding combination. Thereby, the storage space can be saved and excessive power consumption for accessing the parameters can be prevented.

In artificial neural networks, and other similar applications, there is typically a large amount of data involved that is considered sparse data. Due to the large size of the data involved in such applications, it is helpful to compress the data to save bandwidth resources when transmitting the data and save memory resources when storing the data. Introduced herein is a compression technique that selects elements with significant values from data and restructures them into a structured sparse format. By generating metadata that enforces the structured sparse format and organizing the data according to the metadata, the introduced technique not only reduces the size of the data but also consistently places the data in a particular format. As such, hardware can be simplified and optimized to process the data much faster and much more efficiently than the conventional compression techniques that rely on a non-structured sparsity format.

A method for compressing columnar data may include generating, for a data column included in a data chunk, a dictionary enumerating, in a sorted order, a first set of unique values included in the first data column. A compression technique for generated a compressed representation of the data column having a fewest quantity of bytes may be identified based at least on the dictionary. The compression technique including a dictionary compression applying the dictionary and/or another compression technique. A compressed data chunk may be generated by applying the compression technique to compress the data column included in the data chunk. The compressed data chunk may be stored at a database in a variable-size persistent page whose size is allocated based on the size of the compressed representation of the data column. Related systems and articles of manufacture are also provided.

Selecting tables for compression by threshold statistical values. Identified tables are reordered according to fields having the lowest cardinality to increase the size of character strings replaced by keys during compression. Field locations are mapped between the original table and the reordered table. Dictionary-based compression is performed on reordered tables.