Parallel training of machine learning models

    公开(公告)号:US11573803B2

    公开(公告)日:2023-02-07

    申请号:US16405334

    申请日:2019-05-07

    IPC分类号: G06F9/38 G06N20/00 G06F9/50

    摘要: Parallel training of a machine learning model on a computerized system is described. Computing tasks of a system can be assigned to multiple workers of the system. Training data can be accessed. The machine learning model is trained, whereby the training data accessed are dynamically partitioned across the workers of the system by shuffling subsets of the training data through the workers. As a result, different subsets of the training data are used by the workers over time as training proceeds. Related computerized systems and computer program products are also provided.

    Adaptive, proactive raid rebuild
    5.
    发明授权

    公开(公告)号:US11567673B1

    公开(公告)日:2023-01-31

    申请号:US17405672

    申请日:2021-08-18

    IPC分类号: G06F3/06 G06F11/07 G06F11/10

    摘要: A data storage system includes a plurality of storage devices organized as a redundant array of inexpensive disks (RAID) storage array and a RAID controller. The RAID controller monitors the plurality of storage devices in the RAID storage array. The RAID controller also detects that a host read request of a host has a latency exceeding a latency threshold. Based on the monitoring, the RAID controller determines whether a proactive rebuild of a data requested by the host read request in absence of a data error would likely be beneficial to performance. Based on determining that a proactive rebuild of the data requested by the host read request would likely be beneficial to performance, the RAID controller initiates the proactive rebuild of the data and sends the requested data to the host.

    BREADTH-FIRST, DEPTH-NEXT TRAINING OF COGNITIVE MODELS BASED ON DECISION TREES

    公开(公告)号:US20210334709A1

    公开(公告)日:2021-10-28

    申请号:US16858900

    申请日:2020-04-27

    IPC分类号: G06N20/20 G06N3/00 G06N5/00

    摘要: The present invention is notably directed to a computer-implemented method of training a cognitive model. The cognitive model includes decision trees as base learners. The method is performed using processing means to which a given cache memory is connected, so as to train the cognitive model based on training examples of a training dataset. The cognitive model is trained by running a hybrid tree building algorithm, so as to construct the decision trees and thereby associate the training examples to leaf nodes of the constructed decision trees, respectively. The hybrid tree building algorithm involves a first routine and a second routine. Each routine is designed to access the cache memory upon execution. The first routine involves a breadth-first search tree builder, while the second routine involves a depth-first search tree builder.

    Dynamically adjusting block mode pool sizes

    公开(公告)号:US11126360B2

    公开(公告)日:2021-09-21

    申请号:US16660627

    申请日:2019-10-22

    IPC分类号: G06F12/00 G06F3/06

    摘要: A computer-implemented method, according to one embodiment, is for managing a plurality of blocks of memory in two or more pools. The computer-implemented method includes: maintaining a first subset of the plurality of blocks in a first pool, where the blocks maintained in the first pool are configured in single-level cell (SLC) mode. A second subset of the plurality of blocks is also maintained in a second pool, where the blocks maintained in the second pool are configured in multi-bit-per-cell mode. Current workload input/output (I/O) metrics are also identified during runtime. Moreover, a size of the first subset of blocks in the first pool and a size of the second subset of blocks in the second pool are adjusted based on the current workload I/O metrics.

    Selectively storing parity data in different types of memory

    公开(公告)号:US11119855B2

    公开(公告)日:2021-09-14

    申请号:US16663196

    申请日:2019-10-24

    摘要: A computer-implemented method, according to one embodiment, is for selectively storing parity data in different types of memory which include a higher performance memory and a lower performance memory. The computer-implemented method includes: receiving a write request, and determining whether the write request includes parity data. In response to determining that the write request includes parity data, a determination is made as to whether a write heat of the parity data is in a predetermined range. In response to determining that that write heat of the parity data is in the predetermined range, another determination is made as to whether the parity data has been read since a last time the parity data was updated. Furthermore, in response to determining that the parity data has been read since a last time the parity data was updated, the parity data is stored in the higher performance memory.