A method includes determining a data set for storage that includes a plurality of uncompressed data slabs in accordance with a serialized data slab ordering. A storage data set that includes a plurality of compressed data slabs is created based on the data set in accordance with the serialized data slab ordering. Each compressed data slab of the plurality of compressed data slabs is generated from at least one corresponding uncompressed data slab of the plurality of uncompressed data slabs that includes a plurality of values based on generating compressed data for each compressed data slab based on the at least one corresponding uncompressed data slab, and generating compression information for each compressed data slab. The storage data set is stored via a plurality of computing devices.

A method includes determining a data set for storage that includes a plurality of uncompressed data slabs in accordance with a serialized data slab ordering. A storage data set that includes a plurality of compressed data slabs is created based on the data set in accordance with the serialized data slab ordering. Each compressed data slab of the plurality of compressed data slabs is generated from at least one corresponding uncompressed data slab of the plurality of uncompressed data slabs that includes a plurality of values based on generating compressed data for each compressed data slab based on the at least one corresponding uncompressed data slab, and generating compression information for each compressed data slab. The storage data set is stored via a plurality of computing devices.

An input sequence that has a plurality of bits is received where the input sequence is associated with a first section of data within a compressed block. The plurality of bits in the input sequence are divided into a first sub-sequence comprising a first set of bits and a second sub-sequence comprising a second set of bits. The first sub-sequence is encoded using a first Huffman code tree to obtain a first codeword and the second sub-sequence is encoded using a second Huffman code tree to obtain a second codeword. Encoded data that includes information associated with the first Huffman code tree, information associated with the second Huffman code tree, the first codeword, and the second codeword is output.

An input sequence that has a plurality of bits is received where the input sequence is associated with a first section of data within a compressed block. The plurality of bits in the input sequence are divided into a first sub-sequence comprising a first set of bits and a second sub-sequence comprising a second set of bits. The first sub-sequence is encoded using a first Huffman code tree to obtain a first codeword and the second sub-sequence is encoded using a second Huffman code tree to obtain a second codeword. Encoded data that includes information associated with the first Huffman code tree, information associated with the second Huffman code tree, the first codeword, and the second codeword is output.

Methods and systems for condensing a sequential data set on a computer system. A sequential data set is received by the computer system and analyzed to identify a number of occurrences in the sequential data set of each of a plurality of unique data value pairs. A condensed data set is generated including a data element for each of the unique data value pairs. Each data element in the condensed data set includes an identification of the first data value and the second data value of the unique data value pair as well as a count indicative of the number of occurrences in the sequential data set of the first data value immediately followed by the second data value. A graphical signature is generated including a plurality of vectors each extending between two nodes corresponding to the data values of a different data element.

Techniques and solutions are described for compressing data and facilitating access to compressed data. Compression can be applied to proper data subsets of a data set, such as to columns of a table. Using various methods, the proper data subsets can be evaluated to be included in a group of proper data subsets to be compressed using a first compression technique, where unselected proper data subsets are not compressed using the first compression technique. Data in the data set can be reordered based on a reordering sequence for the proper data subsets. Reordering data in the data set can improve compression when at least a portion of the proper data subsets are compressed. A data structure is provided that facilitates accessing specified data stored in a compressed format.

Techniques and solutions are described for compressing data and facilitating access to compressed data. Compression can be applied to proper data subsets of a data set, such as to columns of a table. Using various methods, the proper data subsets can be evaluated to be included in a group of proper data subsets to be compressed using a first compression technique, where unselected proper data subsets are not compressed using the first compression technique. Data in the data set can be reordered based on a reordering sequence for the proper data subsets. Reordering data in the data set can improve compression when at least a portion of the proper data subsets are compressed. A data structure is provided that facilitates accessing specified data stored in a compressed format.

An exemplary computing environment having a DNN module can maintain one or more bandwidth throttling mechanisms. Illustratively, a first throttling mechanism can specify the number of cycles to wait between transactions on a cooperating fabric component (e.g., data bus). Illustratively, a second throttling mechanism can be a transaction count limiter that operatively sets a threshold of a number of transactions to be processed during a given transaction sequence and limits the number of transactions such as multiple transactions in flight to not exceed the set threshold. In an illustrative operation, in executing these two exemplary calculated throttling parameters, the average bandwidth usage and the peak bandwidth usage can be limited. Operatively, with this fabric bandwidth control, the processing units of the DNN are optimized to process data across each transaction cycle resulting in enhanced processing and lower power consumption.

A device and a method for an improved compacting of compressed and uncompressed data blocks into an output buffer are provided. The device is configured to obtain a set of input data blocks comprising at least one of a compressed data block and an uncompressed data block; compact the compressed data blocks into the output buffer, starting from a first predefined region in the output buffer, such that the compressed data blocks are sequentially compacted; and compact the uncompressed data blocks into the output buffer, starting from a second predefined region in the output buffer, such that the uncompressed data blocks are sequentially compacted.

A device and a method for an improved compacting of compressed and uncompressed data blocks into an output buffer are provided. The device is configured to obtain a set of input data blocks comprising at least one of a compressed data block and an uncompressed data block; compact the compressed data blocks into the output buffer, starting from a first predefined region in the output buffer, such that the compressed data blocks are sequentially compacted; and compact the uncompressed data blocks into the output buffer, starting from a second predefined region in the output buffer, such that the uncompressed data blocks are sequentially compacted.