Embodiments of the present disclosure relate to a method, electronic device and computer program product for processing data. The method comprises determining a first hotness associated with a first compressed data block stored on a first storage device. The method also comprises: determining, based on the hotness, whether the first compressed data is stored to the second storage device, a type of the second storage device being different from a type of the first storage device. The method further comprises: in response to determining that the first compressed data block is stored to the second storage device, generating, based on a second compression level of the compression algorithm, a second compressed data block from the first compressed data block for storing to the second storage device, wherein the second compression level corresponds to the second storage device.

Technologies for dividing work across one or more accelerator devices include a compute device. The compute device is to determine a configuration of each of multiple accelerator devices of the compute device, receive a job to be accelerated from a requester device remote from the compute device, and divide the job into multiple tasks for a parallelization of the multiple tasks among the one or more accelerator devices, as a function of a job analysis of the job and the configuration of each accelerator device. The compute engine is further to schedule the tasks to the one or more accelerator devices based on the job analysis and execute the tasks on the one or more accelerator devices for the parallelization of the multiple tasks to obtain an output of the job.

A compression scheme can be selected for an input data stream based on characteristics of the input data stream. For example, when the input data stream is searched for pattern matches, input stream characteristics used to select a compression scheme can include one or more of: type and size of an input stream, a length of a pattern, a distance from a start of where the pattern is to be inserted to the beginning of where the pattern occurred previously, a gap between two pattern matches (including different or same patterns), standard deviation of a length of a pattern, standard deviation of a distance from a start of where the pattern is to be inserted to the beginning of where the pattern occurred previously, or standard deviation of a gap between two pattern matches. Criteria can be established whereby one or more characteristics are used to select a particular encoding scheme.

A method for a hardware decompression read pipeline, the method including determining a length and a distance of a first entity from a buffer; launching a first read request for reading a first data from the buffer; obtaining a second entity from the buffer; determining a distance of the second entity; calculating a new distance for the second entity; and decreasing a first counter by one for each cycle that data is read and returned from the buffer, wherein, when a first number of pending read data clock cycles in the first counter is less than a predetermined number of clock cycles and there is no read-after-write conflict: launching a second read request prior to completion of the first read request. In other aspects, a method for a hardware decompression write pipeline and systems for a hardware decompression read pipeline and a hardware decompression write pipeline are provided.

A computer implemented method of data decompression and verification includes decompressing a compressed data segment to generate a decompressed data region. The method also includes generating a segment vector array (SVA) including a number of segment vectors corresponding to data segments within the decompressed data region, each segment vector indicating a location and a size of a corresponding data segment. The method also includes transmitting the SVA to a chain plugin module and transmitting segment vector array data to a SVA-based message constructor. The method also includes constructing a SVA-based message including the location and size of data segments within the decompressed data region, and transmitting the SVA-based message to a hardware accelerator. The method also includes performing verification sessions at the hardware accelerator, each verification session corresponding to a specific data segment indicated by the SVA-based message.

The present invention realizes a storage device that has a high data reduction effect without decreasing I/O performances. The storage device includes a processor, an accelerator, a memory, and a storage medium, the processor specifies data to be compressed that is data stored in the storage medium from data stored in the memory and transmits a compression instruction including information relating to the data to be compressed to the accelerator, and the accelerator reads the plurality of continuous items of data from the memory and compresses the plurality of items of data to be compressed obtained by excluding data that is not to be compressed from the plurality of items of data, based on the information relating to the data to be compressed received from the processor, to generate compressed data stored in the storage device.

Racks and rack pods to support a plurality of sleds are disclosed herein. Switches for use in the rack pods are also disclosed herein. A rack comprises a plurality of sleds and a plurality of electromagnetic waveguides. The plurality of sleds are vertically spaced from one another. The plurality of electromagnetic waveguides communicate data signals between the plurality of sleds.

A semiconductor device for achieving consistency of data is provided. The process performed by the semiconductor device includes a step of compressing data to generate compression information representing compressed data and the amount of information, a step of accessing management data for controlling access to a memory area, a step of permitting writing to a memory area in units of a predetermined data size based on the fact that the management data indicates that the accessed area is not exclusively allocated to another compression/expansion module, a step of writing data to update management data, a step of permitting reading from the area in units of the data size based on the fact that the management data indicates that the accessed area is not exclusively owned to another compression/expansion module, and a step of reading the compressed data and the compressed information from the area in units of the data size.

Techniques for managing and compressing quantum output data (QOD) associated with quantum computing are presented. In response to receiving QOD from a quantum computer, a compressor component can compress QOD at first compression level to generate first compressed QOD, and can compress QOD at second compression level to generate second compressed QOD, the second compressed QOD can be less compressed than the first compressed QOD. Compressor management component (CMC) can determine whether first QOD includes sufficient data to enable it to be suitably processed by quantum logic. If so, CMC can allow first compressed QOD to continue to be sent to quantum logic and can discard second compressed QOD. If not sufficient, CMC can determine that second compressed QOD is to be processed by quantum logic. If CMC determines second compressed QOD does not include sufficient data, CMC can determine that the QOD is to be processed by quantum logic.

According to one embodiment, a compression device includes a first storage unit, a second storage unit, a calculation unit, and a comparison unit. The first storage unit stores addresses associated with hash values, respectively. The second storage unit includes storage areas specified by the addresses, respectively. The calculation unit determines a hash function to be used for first data in accordance with at least a part of the first data, and calculates a hash value using the hash function and at least a part of second data included in the first data. The comparison unit acquires third data from a storage area in the second storage unit specified by a first address, and compares the second data with the third data. The first address is stored in the first storage unit and is associated with the hash value.