A method performed by a controller of a solid state drive (SSD) comprising receiving from a host a write request to store write data in a nonvolatile semiconductor storage device of the SSD. The method also comprises identifying a first codeword and a second codeword stored in the nonvolatile storage device, the first codeword and the second codeword configured to store write data corresponding to the write request. Responsive to the write request, the method comprises writing a first portion of the write data to the first codeword and writing a second portion of the write data to the second codeword, and sending a message to the host once the write data has been written to the nonvolatile semiconductor storage device. The first and second codewords are adjacently stored, and the write data has a length that is greater than the length of the first and second codewords.

Systems, apparatuses and methods may provide for technology that detects a successful boot of a first firmware component in a computing system, receives a signal from a second firmware component in the computing system, and detects an incompatibility of the first firmware component with respect to the second firmware component based on the signal. In one example, only the first firmware component is repaired in response to the incompatibility.

A method of operating a distributed storage system, the method includes identifying unhealthy chunks of a file. The file is divided into stripes that include data chunks and non-data chunks. The method also includes identifying healthy chunks available for reconstructing the unhealthy chunks and reconstructing unhealthy data chunks before reconstructing unhealthy non-data chunks using the available healthy chunks. When the unhealthy chunk is an unhealthy word-check chunk: reconstructing the unhealthy word-check chunk using healthy word-check chunks and healthy code-check-word-check chunks; determining whether reconstruction of the unhealthy word-check chunk is possible using only healthy word-check chunks and healthy code-check-word-check chunks; and when reconstruction of the unhealthy word-check chunk is not possible using only healthy word-check chunks and healthy code-check-word-check chunks, reconstructing the unhealthy word-check chunk using any healthy chunks.

A computer-implemented method of validating an input object with a validation framework is described. A configuration module may load a validation configuration file from an external storage system. A domain module may receive the input object from an external input source. An executor module may collect property metadata of the input object and construct a searchable key based on the property metadata. The executor may retrieve a validator name associated with the searchable key from the validation configuration file and retrieve a validator associated with the validator name, wherein the validator initiates a validation process on the input object. The executor may receive a validation result from the validator and output the validation result and the error list.

An apparatus is provided, connectable to a memory and one or more peripherals. The apparatus includes translation request circuitry to receive a translation request from one of the peripherals to translate an input address within an input domain to an output address within an output domain. Signing circuitry generates a signature of at least part of the output address using a private key. Translation response circuitry responds to the translation request by transmitting to the one of the peripherals a translation response, including the output address and the signature. Gateway circuitry receives access requests to the memory. Each of the access requests comprises a desired memory address in the output domain and a signature of the desired memory address. The gateway performs validation of the signature of the desired memory address using the private key and in response to the validation of a given access request failing, performs an error action.

Methods, systems, and devices for performing memory command verification are described. A system may include a memory device and a memory controller, which may be external (e.g., a host device). The memory device may receive, from the memory controller, a command indicating a type of operation and an address. The memory device may decode the command and execute an operation (e.g., the operation corresponding to the decoded command) at an execution location on the memory device. The system (e.g., the memory device or the memory controller) may determine whether the executed operation and execution location match the type of operation and address indicated in the command, and the system may thereby determine an error associated with the decoding, the execution, or both of the command.

An electronic device, in response to detecting occurrence of a first condition at the device, generates a first alert that corresponds to a respective application in a first class of applications, the first alert including: a first haptic component and a first audio component composed from an audio waveform that is designated for use by the respective application in the first class of applications. In response to detecting occurrence of a second condition at the device, the device generates a second alert that corresponds to a respective application in a second class of applications different from the first class of applications, the second alert including: a second haptic component and a second audio component composed from an audio waveform that is designated for use by applications in the second class of applications.

Provided are a computer program product, system, and method for using a machine learning module to determine when to perform error checking of a storage unit. Input on attributes of at least one storage device comprising the storage unit are provided to a machine learning module to produce an output value. An error check frequency is determined from the output value. A determination is made as to whether the error check frequency indicates to perform an error checking operation with respect to the storage unit. The error checking operation is performed in response to determining that the error checking frequency indicates to perform the error checking operation.

Provided are a computer program product, system, and method for using a machine learning module to determine when to perform error checking of a storage unit. Input on attributes of at least one storage device comprising the storage unit are provided to a machine learning module to produce an output value. An error check frequency is determined from the output value. A determination is made as to whether the error check frequency indicates to perform an error checking operation with respect to the storage unit. The error checking operation is performed in response to determining that the error checking frequency indicates to perform the error checking operation.

Identifying computer program execution characteristics for determine relevance of pattern instruction executions to determine characteristics of a computer program. Filters are utilized to determine which subsequent occurrences of execution of at least one computer instruction are relevant to a counter based on execution characteristics of the at least one computer instruction where the counter counts the subsequent occurrences of execution of at least one computer instruction following prior executions of the same at least one computer instruction.