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.

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.

Page filtering in a database using a compression dictionary. A page of a database table is compressed, creating a compression dictionary. The compression dictionary includes entries with a byte sequence from the page and a compression symbol associated with the byte sequence. A part of the compressed page, the compression dictionary, and a page symbol list with compression symbols from the dictionary present in the part of the page, are received. A query having a predicate with a predicate value is received. A predicate symbol list, including symbols in the dictionary whose byte sequences at least partially match the predicate value, is generated. Based on the predicate symbol list and the page symbol list, it is determined that at least one symbol from the predicate symbol list is also present in the part of the page. The query is performed by evaluating the predicate on the part of the page.

Representative embodiments are disclosed for a rapid and highly parallel decompression of compressed executable and other files, such as executable files for operating systems and applications, having compressed blocks including run length encoded (RLE) data having data-dependent references. An exemplary embodiment includes a plurality of processors or processor cores to identify a start or end of each compressed block; to partially decompress, in parallel, a selected compressed block into independent data, dependent (RLE) data, and linked dependent (RLE) data; to sequence the independent data, dependent (RLE) data, and linked dependent (RLE) data from a plurality of partial decompressions of a plurality of compressed blocks, to obtain data specified by the dependent (RLE) data and linked dependent (RLE) data, and to insert the obtained data into a corresponding location in an uncompressed file. The representative embodiments are also applicable to other types of data processing for applications having data dependencies.

A process for compressing an audio speech signal utilizes ASR processing to generate a corresponding text representation and, depending on confidence in the corresponding text representation, selectively applies more, less, or no compression to the audio signal. The result is a compressed audio signal, with corresponding text, that is compact and well suited for searching, analytics, or additional ASR processing.

Technologies for lifecycle management include multiple computing devices in communication with a lifecycle management server. On boot, a computing device loads a lightweight firmware boot environment. The lightweight firmware boot environment connects to the lifecycle management server and downloads one or more firmware images for controllers of the computing device. The controllers may include baseboard management controllers, network interface controllers, solid-state drive controllers, or other controllers. The lifecycle management server may select firmware images and/or versions of firmware images based on the controllers or the computing device. The computing device installs each firmware image to a controller memory device coupled to a controller, and in use, each controller accesses the firmware image in the controller memory device. The controller memory device may be a DRAM device or a high-performance byte-addressable non-volatile memory. Other embodiments are described and claimed.

Techniques are disclosed for identifying fixed bits of a bitstring format. One or more processors are configured to generate a first bitstring having respective first bit values that have a first satisfiability state and generate a second bitstring having respective second bit values that have a second satisfiability state. The one or more processors are configured to identify first potential free bits having respective first common values and generate a third bitstring having first potential free bits with the respective first common values and third remaining bits. The one or more processors are configured to identify second potential free bits having respective second common values and identify a fixed bit that is not included in the first potential free bits and is not included in the second potential free bits.

Systems, methods, and software are disclosed herein for compressing audio data. In an implementation, sampled values of an audio signal have a dynamic range. A division of the dynamic range, into at least a lower range and an upper range, is identified based on a fixed mapping of a lower portion of the sampled values to a subset of quanta in a set of quanta having a depth less than a depth of the sampled values. Then an adaptive mapping of an upper portion of the sampled values to a remaining subset of quanta in the set of quanta is also identified, based at least on a dimension of the upper range. The fixed mapping is used to encode the lower portion of the sampled values based, while the adaptive mapping is used to encode the upper portion of the sampled values based on the adaptive mapping.

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.

Machine logic (for example, software) for compressing the image of an instance of a VM/container during time period(s) when the VM/container instance is inactive. A proxy is used to handle requests made to the VM/container instance during periods when it is inactive. A dependency graph is used to determine that a related set of instances of VM/containers so that: (i) when one of the VM/container instances of the related set is deactivated, then the whole set of VM/container instances are deactivated together; and/or (ii) when one of the VM/container instances of the related set is reactivated, then the whole set of VM/container instances are reactivated together.