Provided are techniques for capturing compression efficiency metrics for processing data. In response to retrieving native data for a first operation, perform the first operation; perform a second operation to generate a compression efficiency metric from the native data based on a ratio of the native data to compressed native data; and store the compression efficiency metric persistently for subsequent use in prioritizing compression of the native data.

Provided are techniques for capturing compression efficiency metrics for processing data. In response to retrieving native data for a first operation, perform the first operation; perform a second operation to generate a compression efficiency metric from the native data based on a ratio of the native data to compressed native data; and store the compression efficiency metric persistently for subsequent use in prioritizing compression of the native data.

The present disclosure provides systems, methods, and computer program products for providing efficient embedding table storage and lookup in machine-learning models. A computer-implemented method may include obtaining an embedding table comprising a plurality of embeddings respectively associated with a corresponding index of the embedding table, compressing each particular embedding of the embedding table individually allowing each respective embedding of the embedding table to be decompressed independent of any other embedding in the embedding table, packing the embedding table comprising individually compressed embeddings with a machine-learning model, receiving an input to use for locating an embedding in the embedding table, determining a lookup value based on the input to search indexes of the embedding table, locating the embedding based on searching the indexes of the embedding table for the determined lookup value, and decompressing the located embedding independent of any other embedding in the embedding table.

In one aspect, a method includes splitting empty RAID stripes into sub-stripes and storing pages into the sub-stripes based on a compressibility score. In another aspect, a method includes reading pages from 1-stripes, storing compressed data in a temporary location, reading multiple stripes, determining compressibility score for each stripe and filling stripes based on the compressibility score. In a further aspect, a method includes scanning a dirty queue in a system cache, compressing pages ready for destaging, combining compressed pages in to one aggregated page, writing one aggregated page to one stripe and storing pages with same compressibility score in a stripe.

The present disclosure provides systems, methods, and computer program products for providing efficient embedding table storage and lookup in machine-learning models. A computer-implemented method may include obtaining an embedding table comprising a plurality of embeddings respectively associated with a corresponding index of the embedding table, compressing each particular embedding of the embedding table individually allowing each respective embedding of the embedding table to be decompressed independent of any other embedding in the embedding table, packing the embedding table comprising individually compressed embeddings with a machine-learning model, receiving an input to use for locating an embedding in the embedding table, determining a lookup value based on the input to search indexes of the embedding table, locating the embedding based on searching the indexes of the embedding table for the determined lookup value, and decompressing the located embedding independent of any other embedding in the embedding table.