Technologies for providing shared memory for accelerator sleds includes an accelerator sled to receive, with a memory controller, a memory access request from an accelerator device to access a region of memory. The request is to identify the region of memory with a logical address. Additionally, the accelerator sled is to determine from a map of logical addresses and associated physical address, the physical address associated with the region of memory. In addition, the accelerator sled is to route the memory access request to a memory device associated with the determined physical address.

Techniques and apparatuses to decompress data that has been stack compressed is described. Stack compression refers to compression of data in one or more dimensions. For uncompressed data blocks that are very sparse, i.e., data blocks that contain many zeros, stack compression can be effective. In stack compression, uncompressed data block is compressed into compressed data block by removing one or more zero words from the uncompressed data block. A map metadata that maps the zero words of the uncompressed data block is generated during compression. With the use of the map metadata, the compressed data block can be decompressed to restore the uncompressed data block.

A first storage system compresses data relating to read and write by a primary site and stores the data in a first physical volume. A second storage system compresses data relating to read and write by a secondary site and stores the data in a second physical volume. When performing replication for transferring the data stored in the first physical volume of the first storage system to the second storage system and storing the data in the second physical volume, the first storage system and the second storage system determine, based on a compression scheme executable by the first storage system and a compression scheme executable by the second storage system, a compression scheme to be applied to transfer target data and transfer the transfer target data compressed by the determined compression scheme.

An apparatus comprises a processing device configured to receive system state information corresponding to one or more devices, to predict a usage frequency of the system state information using one or more machine learning models, and to determine, based at least in part on the usage frequency, a compression level for storage of the system state information. The compression level is applied to the system state information to generate at least one compressed file for transmission to a database.

A method to automatically optimize waveform captures from an electrical system includes capturing at least one energy-related waveform using at least one Intelligent Electronic Device (IED) in the electrical system. The at least one captured energy-related waveform is analyzed to determine if the at least one captured energy-related waveform is capable of being compressed, while maintaining relevant attributes for characterization, analysis and/or other use. In response to determining the at least one captured energy-related waveform is capable of being compressed, while maintaining relevant attributes for characterization, analysis, and/or use, the at least one captured energy-related waveform may be compressed using at least one compression technique to generate at least one compressed energy-related waveform. One or more actions may be taken based on or using the at least one compressed energy-related waveform.

Tiers of compression algorithms may be determined using compression information collected regarding compression ratios achieved for data sets using compression algorithms. Each tier may meet specified criteria regarding expected compression ratios achieved for a specified portion or number of data sets. Compression algorithms of each tier may be implemented by a different hardware device that may include hardware accelerators for the algorithms of the tier. Different tiers, and thus different hardware devices, achieve different levels of compression. A recommendation may be provided using compression information collected, such as from one of the hosts, regarding which hardware device to use for compression. The recommendation may be to purchase a license to use or whether to purchase a particular hardware device for compression. Compression information may be collected by a host that issues tagged I/Os providing a hint regarding what compression algorithm to use for the particular I/O operation data.

Systems and methods are provided for loop escape analysis in executing computer instructions. In one embodiment, a method comprises instructions performed by at least one computer process. The method comprises receiving a set of executable computer instructions stored on a storage medium (e.g., by reading the instructions from a tangible, non-transitory storage medium). The method further comprises analyzing the computer instructions to determine a loop, analyzing the computer instructions to determine at least one new variable in the loop, and storing, in a data structure, at least one of an operation related to the variable or a value related to the variable. The method further comprises determining whether to compress the data structure upon reaching the end of the loop, and, based on the determination, compressing the data structure. Systems and computer-readable media are also provided.

An apparatus comprises a processing device configured to collect system state information from host devices, to split the collected system state information into logical chunks, and to determine, based at least in part on a plurality of factors, a compression level to be applied to each of the logical chunks. The plurality of factors comprise a first factor characterizing a time at which the collected system state information is needed at a destination device and at least a second factor characterizing resources available for at least one of performing compression of the collected system state information and transmitting the collected system state information over at least one network to the destination device. The processing device is further configured to apply the determined compression level to each of the logical chunks to generate compressed logical chunks, and to transmit the compressed logical chunks to the destination device.

Apparatuses, systems, and techniques to indicate storage to be compressed. In at least one embodiment, an application programming interface is performed to indicate storage to store information to be compressed.

A method for blockchain-based transaction consensus processing is provided. Node devices in a blockchain include at least one primary node device and several secondary node devices, the primary node device fragments proposed transaction data into a specified number of data fragments based on an erasure code algorithm, and the method includes: receiving a data fragment of the transaction data that is sent by the primary node device in a unicast mode, where respective data fragments sent by the primary node device to individual node devices in a unicast mode are different from one another; broadcasting the received data fragment to other node devices in the blockchain, and receiving data fragments of the transaction data that are broadcast by the other node devices; determining whether the number of received data fragments of the transaction data reaches an erasure code recovery threshold; and if so, performing data recovery on the received data fragments based on an erasure code reconstruction algorithm to obtain original content of the transaction data, to complete consensus processing with respect to the original content of the transaction data.