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    Fault-tolerant parallel computation
    1.
    发明授权
    Fault-tolerant parallel computation 有权

    公开(公告)号：US10936432B1

    公开(公告)日：2021-03-02

    申请号：US14495408

    申请日：2014-09-24

    Applicant: Amazon Technologies, Inc.

    Inventor： Tin-Yu Lee ,  Rejith George Joseph ,  Scott Michael Le Grand ,  Saurabh Dileep Baji

    IPC: G06F11/00 ,  G06F11/14

    Abstract: Methods, systems, and computer-readable media for implementing a fault-tolerant parallel computation framework are disclosed. Execution of an application comprises execution of a plurality of processes in parallel. Process states for the processes are stored during the execution of the application. The processes use a message passing interface for exchanging messages with one other. The messages are exchanged and the process states are stored at a plurality of checkpoints during execution of the application. A final successful checkpoint is determined after the execution of the application is terminated. The final successful checkpoint represents the most recent checkpoint at which the processes exchanged messages successfully. Execution of the application is resumed from the final successful checkpoint using the process states stored at the final successful checkpoint.

    Executing parallel jobs with message passing on compute clusters
    2.
    发明授权
    Executing parallel jobs with message passing on compute clusters 有权

    公开(公告)号：US10148736B1

    公开(公告)日：2018-12-04

    申请号：US14281582

    申请日：2014-05-19

    Applicant: Amazon Technologies, Inc.

    Inventor： Tin-Yu Lee ,  Rejith George Joseph ,  Scott Michael Le Grand ,  Saurabh Dileep Baji ,  Peter Sirota

    IPC: H04L29/08 ,  G06F9/50

    Abstract: A client may submit a job to a service provider that processes a large data set and that employs a message passing interface (MPI) to coordinate the collective execution of the job on multiple compute nodes. The framework may create a MapReduce cluster (e.g., within a VPC) and may generate a single key pair for the cluster, which may be downloaded by nodes in the cluster and used to establish secure node-to-node communication channels for MPI messaging. A single node may be assigned as a mapper process and may launch the MPI job, which may fork its commands to other nodes in the cluster (e.g., nodes identified in a hostfile associated with the MPI job), according to the MPI interface. A rankfile may be used to synchronize the MPI job and another MPI process used to download portions of the data set to respective nodes in the cluster.

    CONDITIONAL PARALLEL PROCESSING IN FULLY-CONNECTED NEURAL NETWORKS
    5.
    发明申请
    CONDITIONAL PARALLEL PROCESSING IN FULLY-CONNECTED NEURAL NETWORKS 审中-公开

    公开(公告)号：US20170193368A1

    公开(公告)日：2017-07-06

    申请号：US14984510

    申请日：2015-12-30

    Applicant: Amazon Technologies, Inc.

    Inventor： Scott Michael Le Grand ,  Rejith George Joseph

    IPC: G06N3/08

    Abstract: The present disclosure is directed to parallelization of artificial neural network processing by conditionally synchronizing, among multiple computer processors, either the input or output of individual operations, and by conditionally using either rows or columns of certain matrices used in the operations. The conditional processing may depend upon the relative sizes of the input and output of the specific operations to be performed. For example, if a current layer matrix of values is larger than a next layer matrix of values to be computed, then rows of a weight matrix may be used by the computer processors to compute the next layer matrix. If the current layer matrix is smaller than the next layer matrix, then columns of the weight matrix may be used by the computer processors to compute the next layer matrix.

    Recommendation system using improved neural network
    6.
    发明授权
    Recommendation system using improved neural network 有权

    公开(公告)号：US10896459B1

    公开(公告)日：2021-01-19

    申请号：US16842432

    申请日：2020-04-07

    Applicant: Amazon Technologies, Inc.

    Inventor： Rejith George Joseph ,  Oleg Rybakov

    IPC: G06Q30/00 ,  G06Q30/06 ,  G06N3/08

    Abstract: Some aspects of the present disclosure relate to generating and training a neural network by separating historical item interaction data into both inputs and outputs. This may be done, for example, based on date. For example, a neural network machine learning technique may be used to generate a prediction model using a set of inputs that includes both a number of items purchased by a number of users before a certain date as well as some or all attributes of those items, and a set of outputs that includes the items purchased after that date. The items purchased before that date and the associated attributes can be subjected to a time-decay function.

    Conditional parallel processing in fully-connected neural networks
    7.
    发明授权
    Conditional parallel processing in fully-connected neural networks 有权

    公开(公告)号：US10482380B2

    公开(公告)日：2019-11-19

    申请号：US14984510

    申请日：2015-12-30

    Applicant: Amazon Technologies, Inc.

    Inventor： Scott Michael Le Grand ,  Rejith George Joseph

    IPC: G06N3/08

    Abstract: The present disclosure is directed to parallelization of artificial neural network processing by conditionally synchronizing, among multiple computer processors, either the input or output of individual operations, and by conditionally using either rows or columns of certain matrices used in the operations. The conditional processing may depend upon the relative sizes of the input and output of the specific operations to be performed. For example, if a current layer matrix of values is larger than a next layer matrix of values to be computed, then rows of a weight matrix may be used by the computer processors to compute the next layer matrix. If the current layer matrix is smaller than the next layer matrix, then columns of the weight matrix may be used by the computer processors to compute the next layer matrix.

    Isolating compute clusters created for a customer
    9.
    发明授权
    Isolating compute clusters created for a customer 有权

    公开(公告)号：US10659523B1

    公开(公告)日：2020-05-19

    申请号：US14286724

    申请日：2014-05-23

    Applicant: Amazon Technologies, Inc.

    Inventor： Rejith George Joseph ,  Tin-Yu Lee ,  Scott Michael Le Grand ,  Saurabh Dileep Baji

    IPC: G06F15/16 ,  H04L29/08

    Abstract: At the request of a customer, a distributed computing service provider may create multiple clusters under a single customer account, and may isolate them from each other. For example, various isolation mechanisms (or combinations of isolation mechanisms) may be applied when creating the clusters to isolate a given cluster of compute nodes from network traffic from compute nodes of other clusters (e.g., by creating the clusters in different VPCs); to restrict access to data, metadata, or resources that are within the given cluster of compute nodes or that are associated with the given cluster of compute nodes by compute nodes of other clusters in the distributed computing system (e.g., using an instance metadata tag and/or a storage system prefix); and/or restricting access to application programming interfaces of the distributed computing service by the given cluster of compute nodes (e.g., using an identity and access manager).

    Recommendation system using improved neural network
    10.
    发明授权
    Recommendation system using improved neural network 有权

    公开(公告)号：US10650432B1

    公开(公告)日：2020-05-12

    申请号：US15362585

    申请日：2016-11-28

    Applicant: Amazon Technologies, Inc.

    Inventor： Rejith George Joseph ,  Oleg Rybakov

    IPC: G06Q30/00 ,  G06Q30/06 ,  G06N3/08

    Abstract: Some aspects of the present disclosure relate to generating and training a neural network by separating historical item interaction data into both inputs and outputs. This may be done, for example, based on date. For example, a neural network machine learning technique may be used to generate a prediction model using a set of inputs that includes both a number of items purchased by a number of users before a certain date as well as some or all attributes of those items, and a set of outputs that includes the items purchased after that date. The items purchased before that date and the associated attributes can be subjected to a time-decay function.

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