Emulating memory sub-systems that have different performance characteristics

    公开(公告)号:US11861193B2

    公开(公告)日:2024-01-02

    申请号:US18097168

    申请日:2023-01-13

    Abstract: A system and method for updating a configuration of a host system so that the memory sub-system of the host system emulates performance characteristics of a target memory sub-system. An example system includes a memory sub-system; and a processor, operatively coupled with the memory sub-system, to perform operations comprising receiving a request to emulate a characteristic of a target memory sub-system, identifying a candidate configuration that generates a load on a memory sub-system of a host system to decrease a characteristics of the memory sub-system of the host system, and updating a configuration of the host system based at least on the candidate configuration, wherein the updated configuration changes the memory sub-system of the host system to emulate the characteristic of the target memory sub-system.

    PARALLEL ITERATOR FOR MACHINE LEARNING FRAMEWORKS

    公开(公告)号:US20210191886A1

    公开(公告)日:2021-06-24

    申请号:US16722408

    申请日:2019-12-20

    Abstract: A request to retrieve data from a memory device of a memory sub-system can be received from a machine learning (ML) framework executing on a host system, where the data comprises a plurality of logical partitions. A set of parallel I/O threads can be initiated to retrieve the data from the memory device, where each I/O thread of the set of parallel I/O threads retrieves a different portion of the data from a different corresponding logical partition and stores the different portion of the data in a I/O buffer of a set of I/O buffers corresponding to the set of I/O threads in parallel. The different portion of the data can be successively provided from each I/O buffer to the ML framework, where the set of parallel I/O threads is to continually retrieve the data from the memory device until all of the data from the logical partitions has been provided to the ML framework.

    Parallel iterator for machine learning frameworks

    公开(公告)号:US11734205B2

    公开(公告)日:2023-08-22

    申请号:US17573545

    申请日:2022-01-11

    CPC classification number: G06F13/1668 G06F9/466 G06N3/04 G06N3/08

    Abstract: A request to retrieve data from a memory device of a memory sub-system can be received from a machine learning (ML) framework executing on a host system, where the data comprises a plurality of logical partitions. A set of parallel I/O threads can be initiated to retrieve the data from the memory device, where each I/O thread of the set of parallel I/O threads retrieves a different portion of the data from a different corresponding logical partition and stores the different portion of the data in a I/O buffer of a set of I/O buffers corresponding to the set of I/O threads in parallel. The different portion of the data can be successively provided from each I/O buffer to the ML framework, where the set of parallel I/O threads is to continually retrieve the data from the memory device until all of the data from the logical partitions has been provided to the ML framework.

    EMULATING MEMORY SUB-SYSTEMS THAT HAVE DIFFERENT PERFORMANCE CHARACTERISTICS

    公开(公告)号:US20230168824A1

    公开(公告)日:2023-06-01

    申请号:US18097168

    申请日:2023-01-13

    Abstract: A system and method for updating a configuration of a host system so that the memory sub-system of the host system emulates performance characteristics of a target memory sub-system. An example system includes a memory sub-system; and a processor, operatively coupled with the memory sub-system, to perform operations comprising receiving a request to emulate a characteristic of a target memory sub-system, identifying a candidate configuration that generates a load on a memory sub-system of a host system to decrease a characteristics of the memory sub-system of the host system, and updating a configuration of the host system based at least on the candidate configuration, wherein the updated configuration changes the memory sub-system of the host system to emulate the characteristic of the target memory sub-system.

    OPTIMIZING DATABASE CURSOR OPERATIONS IN KEY-VALUE STORES

    公开(公告)号:US20240143585A1

    公开(公告)日:2024-05-02

    申请号:US18497145

    申请日:2023-10-30

    CPC classification number: G06F16/2453 G06F16/215 G06F16/24562

    Abstract: A system includes a memory device a processing device, operatively coupled to the memory device. The processing device is configured to receive a request to identify a target key in a key-value store based on a specified key; identify, in at least one of a plurality of sequences of memory keys, the target key based on the specified key, where the plurality of sequences of memory keys includes a sequence of memory keys and a sequence of media keys that comprises designated media keys, where each of the designated media keys is designated as being deleted, where the identifying comprises comparing each of the designated media keys to the specified key; and perform a database operation using the target key.

    Emulating memory sub-systems that have different performance characteristics

    公开(公告)号:US11556259B1

    公开(公告)日:2023-01-17

    申请号:US17464912

    申请日:2021-09-02

    Abstract: A system and method for updating a configuration of a host system so that the memory sub-system of the host system emulates performance characteristics of a target memory sub-system. An example system determining a configuration of the host system, the host system comprising a memory sub-system; receiving, by a processing device, a request to emulate a characteristic of a target memory sub-system; analyzing a plurality of candidate configurations for the host system, wherein the plurality of candidate configurations comprises a candidate configuration that generates a load on the memory sub-system to decrease characteristics of the memory sub-system; and updating the configuration of the host system based on the plurality of candidate configurations, wherein the updated configuration changes the memory sub-system to emulate the characteristic of the target memory sub-system.

    PARALLEL ITERATOR FOR MACHINE LEARNING FRAMEWORKS

    公开(公告)号:US20220138127A1

    公开(公告)日:2022-05-05

    申请号:US17573545

    申请日:2022-01-11

    Abstract: A request to retrieve data from a memory device of a memory sub-system can be received from a machine learning (ML) framework executing on a host system, where the data comprises a plurality of logical partitions. A set of parallel I/O threads can be initiated to retrieve the data from the memory device, where each I/O thread of the set of parallel I/O threads retrieves a different portion of the data from a different corresponding logical partition and stores the different portion of the data in a I/O buffer of a set of I/O buffers corresponding to the set of I/O threads in parallel. The different portion of the data can be successively provided from each I/O buffer to the ML framework, where the set of parallel I/O threads is to continually retrieve the data from the memory device until all of the data from the logical partitions has been provided to the ML framework.

    Parallel iterator for machine learning frameworks

    公开(公告)号:US11221973B2

    公开(公告)日:2022-01-11

    申请号:US16722408

    申请日:2019-12-20

    Abstract: A request to retrieve data from a memory device of a memory sub-system can be received from a machine learning (ML) framework executing on a host system, where the data comprises a plurality of logical partitions. A set of parallel I/O threads can be initiated to retrieve the data from the memory device, where each I/O thread of the set of parallel I/O threads retrieves a different portion of the data from a different corresponding logical partition and stores the different portion of the data in a I/O buffer of a set of I/O buffers corresponding to the set of I/O threads in parallel. The different portion of the data can be successively provided from each I/O buffer to the ML framework, where the set of parallel I/O threads is to continually retrieve the data from the memory device until all of the data from the logical partitions has been provided to the ML framework.

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