Abstract: A method for a transactional commit in a storage unit is provided. The method includes receiving a logical record from a storage node into a transaction engine of a storage unit of the storage node and writing the logical record into a data structure of the transaction engine. The method includes writing, to a command queue of the transaction engine, an indication to perform an atomic update using the logical record and transferring each portion of the logical record from the data structure of the transaction engine to non-persistent memory of the storage unit as a committed transaction. A storage unit for a storage system is also provided.

Abstract: Executing a machine learning model in an artificial intelligence infrastructure that includes one or more storage systems and one or more graphical processing unit (‘GPU’) servers, including: receiving, by a graphical processing unit (‘GPU’) server, a dataset transformed by a storage system that is external to the GPU server; and executing, by the GPU server, one or more machine learning algorithms using the transformed dataset as input.

Abstract: A method for a transactional commit in a storage unit is provided. The method includes receiving a logical record from a storage node into a transaction engine of a storage unit of the storage node and writing the logical record into a data structure of the transaction engine. The method includes writing, to a command queue of the transaction engine, an indication to perform an atomic update using the logical record and transferring each portion of the logical record from the data structure of the transaction engine to non-persistent memory of the storage unit as a committed transaction. A storage unit for a storage system is also provided.

Abstract: Data transformation offloading in an artificial intelligence infrastructure that includes one or more storage systems and one or more graphical processing unit (‘GPU’) servers, including: storing, within the storage system, a dataset; identifying, in dependence upon one or more machine learning models to be executed on the GPU servers, one or more transformations to apply to the dataset; and generating, by the storage system in dependence upon the one or more transformations, a transformed dataset.

Abstract: An apparatus for suspending a prosthesis from a residual limb comprises a tubular liner adapted to receive the residual limb. A lanyard is secured at one end to the distal end of the liner. A hollow prosthetic socket defines a well configured to receive the residual limb and includes a socket seal disposed at the distal end of the socket. An upper surface of the socket seal defines a recess opening to the exterior of the socket through a side wall. A sealing element disposed along the length of the lanyard is configured to engage within the passage. The free end of the lanyard is pulled through passage such that the sealing element sealingly engages within the passage for preventing fluid communication through the passage. The liner at least partially contacts the interior surface of the socket for providing a fluid seal and creating negative pressure within the socket.

Abstract: A plurality of storage nodes is provided. Each of the plurality of storage nodes includes nonvolatile solid-state memory for user data storage. The plurality of storage nodes is configured to distribute the user data and metadata associated with the user data throughout the plurality of storage nodes such that the plurality of storage nodes maintain the ability to read the user data, using erasure coding, despite a loss of two of the plurality of storage nodes. The plurality of storage nodes is configured to initiate an action based on the redundant copies of the metadata, responsive to achieving a level of redundancy for the redundant copies of the metadata. A method for accessing user data in a plurality of storage nodes having nonvolatile solid-state memory is also provided.

Abstract: A non-volatile solid-state storage is provided. The non-volatile solid state storage includes a non-volatile random access memory (NVRAM) addressable by a processor external to the non-volatile solid state storage. The NVRAM is configured to store user data and metadata relating to the user data. The non-volatile solid state storage includes a flash memory addressable by the processor. The flash memory is configured to store the user data responsive to the processor directing transfer of the user data from the NVRAM to the flash memory.

Abstract: A method of applying a data format in a direct memory access transfer is provided. The method includes distributing user data throughout a plurality of storage nodes through erasure coding, wherein the plurality of storage nodes are housed within a single chassis that couples the storage nodes as a cluster, each of the plurality of storage nodes having nonvolatile solid-state memory for user data storage. The method includes reading a self-describing data portion from a first memory of the nonvolatile solid-state memory and extracting a destination from the self-describing data portion. The method includes writing data, from the self-describing data portion, to a second memory of the nonvolatile solid-state memory according to the destination.

Abstract: A method for storing data in a storage system having solid-state memory is provided. The method includes determining portions of the solid-state memory that have a faster access rate and portions of the solid-state memory that have a slower access rate, relative to each other or to a threshold. The method includes writing data bits of erasure coded data to the portions of the solid-state memory having the faster access rate, and writing one or more parity bits of the erasure coded data to the portions of the solid-state memory having the slower access rate. A storage system is also provided.

Abstract: A method of applying an address space to data storage in a non-volatile solid-state storage is provided. The method includes receiving a plurality of portions of user data for storage in the non-volatile solid-state storage and assigning to each successive one of the plurality of portions of user data one of a plurality of sequential, nonrepeating addresses of an address space. The address range of the address space exceeds a maximum number of addresses expected to be applied during a lifespan of the non-volatile solid-state storage. The method includes writing each of the plurality of portions of user data to the non-volatile solid-state storage such that each of the plurality of portions of user data is identified and locatable for reading via the one of the plurality of sequential, nonrepeating addresses of the address space.

Abstract: A method of applying a data format in a direct memory access transfer is provided. The method includes distributing user data throughout a plurality of storage nodes through erasure coding, wherein the plurality of storage nodes are housed within a single chassis that couples the storage nodes as a cluster, each of the plurality of storage nodes having nonvolatile solid-state memory for user data storage. The method includes reading a self-describing data portion from a first memory of the nonvolatile solid-state memory and extracting a destination from the self-describing data portion. The method includes writing data, from the self-describing data portion, to a second memory of the nonvolatile solid-state memory according to the destination.

Abstract: A method for storing data in a storage system having solid-state memory is provided. The method includes determining a first portion of the solid-state memory having a lower read latency than a second portion of the solid-state memory, relative to each other or to a threshold. The method includes writing data bits of erasure coded data to the first portion of the solid-state memory, and writing one or more parity bits of the erasure coded data to the second portions of the solid-state memory. A storage system is also provided.

Abstract: A method for updates in a storage system is provided. The method includes writing identifiers, associated with data to be stored, to storage units of the storage system and writing trim records indicative of identifiers that are allowed to not exist in the storage system to the storage units. The method includes determining whether stored data corresponding to records of identifiers is valid based on the records of the identifiers and the trim records.

Abstract: A method of applying an address space to data storage in a non-volatile solid-state storage is provided. The method includes receiving a plurality of portions of user data for storage in the non-volatile solid-state storage and assigning to each successive one of the plurality of portions of user data one of a plurality of sequential, nonrepeating addresses of an address space. The address range of the address space exceeds a maximum number of addresses expected to be applied during a lifespan of the non-volatile solid-state storage. The method includes writing each of the plurality of portions of user data to the non-volatile solid-state storage such that each of the plurality of portions of user data is identified and locatable for reading via the one of the plurality of sequential, nonrepeating addresses of the address space.

Abstract: Data transformation caching in an artificial intelligence infrastructure that includes one or more storage systems and one or more graphical processing unit (‘GPU’) servers, including: identifying, in dependence upon one or more machine learning models to be executed on the GPU servers, one or more transformations to apply to a dataset; generating, in dependence upon the one or more transformations, a transformed dataset; storing, within one or more of the storage systems, the transformed dataset; receiving a plurality of requests to transmit the transformed dataset to one or more of the GPU servers; and responsive to each request, transmitting, from the one or more storage systems to the one or more GPU servers without re-performing the one or more transformations on the dataset, the transformed dataset.

Abstract: A non-volatile solid-state storage is provided. The non-volatile solid state storage includes a non-volatile random access memory (NVRAM) addressable by a processor external to the non-volatile solid state storage. The NVRAM is configured to store user data and metadata relating to the user data. The non-volatile solid state storage includes a flash memory addressable by the processor. The flash memory is configured to store the user data responsive to the processor directing transfer of the user data from the NVRAM to the flash memory.

Abstract: Data transformation offloading in an artificial intelligence infrastructure that includes one or more storage systems and one or more graphical processing unit (‘GPU’) servers, including: storing, within the storage system, a dataset; identifying, in dependence upon one or more machine learning models to be executed on the GPU servers, one or more transformations to apply to the dataset; and generating, by the storage system in dependence upon the one or more transformations, a transformed dataset.

Abstract: Data transformation caching in an artificial intelligence infrastructure that includes one or more storage systems and one or more graphical processing unit (‘GPU’) servers, including: identifying, in dependence upon one or more machine learning models to be executed on the GPU servers, one or more transformations to apply to a dataset; generating, in dependence upon the one or more transformations, a transformed dataset; storing, within one or more of the storage systems, the transformed dataset; receiving a plurality of requests to transmit the transformed dataset to one or more of the GPU servers; and responsive to each request, transmitting, from the one or more storage systems to the one or more GPU servers without re-performing the one or more transformations on the dataset, the transformed dataset.

Abstract: An archery bow is shown that has a modular hand grip. The bow may include a riser having an upper end, a lower end, and a grip attachment portion disposed between the upper and lower ends. The grip attachment portion may be positioned to be held by a hand of an archer and may have an opening receiving a grip insert. The grip insert may have a contoured surface configured to contact the hand of the archer and may have a protrusion configured to be received by the opening. The bow may also have upper and lower limbs each having proximal ends connected to respective ends of the riser and each having distal ends. A bowstring may extend between the distal ends of the upper and lower limbs. The modular hand grips may improve shot consistency and feel, reduce torque, and still allow the bow to be customized.