In various examples, a VPU and associated components may be optimized to improve VPU performance and throughput. For example, the VPU may include a min/max collector, automatic store predication functionality, a SIMD data path organization that allows for inter-lane sharing, a transposed load/store with stride parameter functionality, a load with permute and zero insertion functionality, hardware, logic, and memory layout functionality to allow for two point and two by two point lookups, and per memory bank load caching capabilities. In addition, decoupled accelerators may be used to offload VPU processing tasks to increase throughput and performance, and a hardware sequencer may be included in a DMA system to reduce programming complexity of the VPU and the DMA system. The DMA and VPU may execute a VPU configuration mode that allows the VPU and DMA to operate without a processing controller for performing dynamic region based data movement operations.

A computer-implemented method according to one embodiment includes causing a plurality of I/O queues to be created between an initiator and a storage target device. The created I/O queues are reserved for I/O requests for which adjustments of current priorities of extents of data associated with the I/O requests are to be performed. The method further includes determining identifying information of an I/O request sent from the initiator to the storage target device and determining whether the I/O request was sent from the initiator to the storage target device using one of the created I/O queues. In response to a determination that the I/O request was sent using a first of the created I/O queues having one of the adjustments associated therewith, a tiering manager of the storage target device is instructed to perform the adjustment on the current priority of the extent of data associated with the I/O request.

A method for computational storage may include storing, at a storage device, a first portion of data, wherein the first portion of data may include a first fragment of a record, and a second portion of data may include a second fragment of the record, and appending the second fragment of the record to the first portion of data. The method may further include performing, at the storage device, an operation on the first and second fragments of the record. The method may further include determining that the first portion of data may include a first fragment of a record, and a second portion of data may include a second fragment of the record, wherein appending the second fragment of the record to the first portion of data may include appending, based on the determining, the second fragment of the record to the first portion of data.

A storage system. In some embodiments, the storage system includes a plurality of object stores, and a plurality of data managers, connected to the object stores. The plurality of data managers may include a plurality of processing circuits. A first processing circuit of the plurality of processing circuits may be configured to process primarily input-output operations, and a second processing circuit of the plurality of processing circuits may be configured to process primarily input-output completions.

Method and system for training a machine learning model based on a training dataset formed by data objects distributed across a virtual object storage service. The method comprises fetching from the virtual object storage service, the training dataset; copying the fetched training dataset on a first local storage device and maintaining a list of modifications executed on the training dataset that occurred on the virtual object storage service. The method comprises, upon receiving a request to initiate training of the machine learning model, generating a synchronized training dataset mirroring the training dataset stored in the virtual object storage service; storing the synchronized training dataset in a second local storage device; and fetching training data from the synchronized training dataset stored in the second local storage device as the training of the machine learning model is executed.

A method of storing data, a method of reading data, a device, and a storage medium are provided, which relate to a field of artificial intelligence, in particular to the fields of cloud computing technology and distributed storage technology. A specific implementation scheme includes: storing at least one target data into a target file in a storage class memory device; recording a storage address of the at least one target data in the storage class memory device in a dynamic random access memory as a first index data; and synchronously storing the first index data into the storage class memory device as a second index data.

In a case of performing control of migrating data from a relatively-old-generation magnetic tape as a migration-source magnetic tape included in one storage pool of the plurality of storage pools to relatively-new-generation magnetic tapes as migration-destination magnetic tapes included in each of the plurality of storage pools, an information processing apparatus excludes, from migration targets, data recorded in a relatively-new-generation magnetic tape included in a storage pool to which the migration-destination magnetic tape belongs.

A storage device according to the present technology may include a memory device for storing data, a buffer memory configured to temporarily store data to be stored in the memory device, and a memory controller configured to determine a delay time based on a plurality of parameters upon receipt of a write request from a host, and transmit a data request to the host after the delay time has elapsed.

Operation of a non-volatile memory (NVM) storage module may comprise receiving a plurality of commands as associated with a plurality of priority-based queues from a host-memory. A received command is evaluated in accordance with a priority associated with the queue storing the command and a size of the command. The evaluated command is split into a plurality of sub-commands, each of the sub-commands having a size determined in accordance with the evaluation. A predetermined number of hardware resources are allocated for each of the evaluated command based on at least the size of each of the sub-commands to thereby enable a processing of the evaluated command based on the allocated resources. Quality of service (QoS) for the evaluated-command may thus be augmented.

In one approach, filesets to be backed up are divided into partitions and snapshots are pulled for each partition. In one architecture, a data management and storage (DMS) cluster includes a plurality of peer DMS nodes and a distributed data store implemented across the peer DMS nodes. One of the peer DMS nodes receives fileset metadata for the fileset and defines a plurality of partitions for the fileset based on the fileset metadata. The peer DMS nodes operate autonomously to execute jobs to pull snapshots for each of the partitions and to store the snapshots of the partitions in the distributed data store.