Emc IP Holding Company LLC

Dell Products Inc.

Emc Corporation

Junping Zhao

Kun Wang

Yu Cao

Kevin Xu

Sanping Li

Layne Lin Peng

Kenneth Durazzo

Ricky Yuxi Sun

Xiangping Chen

Kenneth J Taylor

Zhi Ying

Accela Yilong Zhao

Fan Guo

Fenghao Zhang

Gang Xie

Wei Cui

Randall Shain

Philippe Armangau

Jinpeng Liu

John T Cardente

Xiaoyan Guo

Zhe Dong

Jun Tao

Mohamed Sohail

Yifan Sun

Adrian Michaud

Wei Qi

Dragan Savic

Jie Bao

Stephen Wing-Kin Au

Jialin Yang

Anton Kucherov

Pengfei Wu

Robert Anthony Lincourt, Jr

Fei Chen

Chen Gong

Lester Zhang

Walter Wang

Denny Dengyu Wang

Sen Zhang

Yunfan Han

Joe Jian Liu

Fenghua Hu

Xinlei Xu

William C Davenport

Jun Wu

Ivan Bassov

John S Harwood

Colin Yong Zou

Man Lv

Ivan Basov

Huan Chen

Kun Peng Wang

Yining Si

Qiaosheng Zhou

Fucai Liu

Wilson Guoyu Hu

Lin Peng

Andrew Anzhou Hou

Yongjun Shi

Sean Cheng Ye

Hu Chenxi

Brown Zan Liu

An apparatus in one embodiment comprises a processing platform configured to communicate over a network with a plurality of Internet of Things (IoT) devices. The processing platform receives at least a first intermediate message from a first gateway of the network, receives one or more additional intermediate messages from each of one or more additional gateways of the network, associates the first and additional intermediate messages with one another based at least in part on a common message identifier detected in each such intermediate message, and processes the associated first and additional intermediate messages to recover a device message from a given one of the IoT devices. The first intermediate message is based at least in part on at least one application of a designated cryptographic function to the device message utilizing a corresponding key. At least one of the one or more additional intermediate messages provides at least a portion of the key.

Lightweight security for internet of things messaging

Systems and methods are provided for optimizing GPU memory allocation for high-performance applications such as deep learning (DL) computing. For example, a DL task is executed using GPU resources (GPU device and GPU memory) to process a DL model having functional layers that are processed in a predefined sequence. A current functional layer of the DL model is invoked and processed using the GPU device. In response to the invoking, a data compression operation is performed to compress data of a previous functional layer of the DL model, and store the compressed data in the GPU memory. Responsive to the invoking, compressed data of a next functional layer of the DL model is accessed from the GPU memory and a data decompression operation is performed to decompress the compressed data for subsequent processing of the next functional layer of the DL model by the GPU device.

Optimization of graphics processing unit memory for deep learning computing

A resource management method comprises: in response to receiving, from an application operating on a client, a resource allocation request indicating an amount of dedicated processing resources required by the application, acquiring a mapping between a group of physical dedicated processing resources provided by a group of servers and a group of logical dedicated processing resources, the group of physical dedicated processing resources being divided into the group of logical dedicated processing resources; determining allocation statuses of the group of logical dedicated processing resources; determining, based at least on the mapping and the allocation statuses, a first amount of logical dedicated processing resources to be allocated to the application from the group of logical dedicated processing resources; and indicating the first amount of logical dedicated processing resources to the application, to allow the application to utilize physical dedicated processing resources provided by at least one of the group of servers.

Method, device and computer program product for managing dedicated processing resources

Embodiments of the present disclosure relate to a method, device and computer program product for determining a resource amount of dedicated processing resources. The method comprises obtaining a structural representation of a neural network for deep learning processing, the structural representation indicating a layer attribute of the neural network that is associated with the dedicated processing resources; and determining the resource amount of the dedicated processing resources required for the deep learning processing based on the structural representation. In this manner, the resource amount of the dedicated processing resources required by the deep learning processing may be better estimated to improve the performance and resource utilization rate of the dedicated processing resource scheduling.

Method, device and computer program product for determining resource amount for dedicated processing resources

Systems and methods are provided for implementing an intelligent data management system for data storage and data management in a cloud computing environment. For example, a system includes an application server, a distributed data storage system, and an intelligent data management system. The application server is configured to host a data processing application. The distributed data storage system is configured to store data generated by a network of devices associated with the data processing application. The intelligent data management system is configured to manage data storage operations for storing the data generated by the network of devices in the distributed data storage system. For example, the intelligent data management system is configured to determine one or more data types of the data generated by the network of devices and select one of a plurality of repositories within the distributed data storage system to store the data based on the determined data types.

Intelligent data storage and management for cloud computing

Embodiments of the present disclosure relate to a method, device and computer program product for deploying a machine learning model. The method comprises: receiving an intermediate representation indicating processing of a machine learning model, learning parameters of the machine learning model, and a computing resource requirement for executing the machine learning model, the intermediate representation, the learning parameters, and the computing resource requirement being determined based on an original code of the machine learning model, the intermediate representation being irrelevant to a programming language of the original code; determining, at least based on the computing resource requirement, a computing node and a parameter storage node for executing the machine learning model; storing the learning parameters in the parameter storage node; and sending the intermediate representation to the computing node for executing the machine learning model with the stored learning parameters.

Method, device and computer program product for deploying a machine learning model

A method comprises: compiling the code segment with a compiler; and determining, based on an intermediate result of the compiling, a resource associated with a dedicated processing unit and for executing the code segment. As such, the resource required for executing a code segment may be determined quickly without actually executing the code segment and allocating or releasing the resource, which helps subsequent resource allocation and further brings about a better user experience.

Method, a device, and a computer program product for determining a resource required for executing a code segment

Embodiments of the present disclosure provide methods, apparatuses and computer program products for transmitting data. A method comprises determining, at a source node, a traffic type of a packet to be sent to a destination node, the source node and the destination node having therebetween a plurality of network paths for different traffic types. The method further comprises including a mark indicating the traffic type in the packet. In addition, the method further comprises sending the packet including the mark to the destination node such that the packet is forwarded along one of the plurality of network paths specific to the traffic type. Embodiments of the present disclosure can transmit data using different network paths based on different traffic types of data so as to optimize network performance for different network requirements.

Methods, apparatuses and computer program products for transmitting data

Techniques are provided for implementing a parameter server within a networking infrastructure of a computing system to reduce the communication bandwidth and latency for performing communication synchronization operations of the parameter server. For example, a method includes executing a distributed deep learning (DL) model training process to train model parameters of a DL model using a plurality of worker nodes executing on one or more server nodes of a computing system, and executing a parameter server within a networking infrastructure of the computing system to aggregate local model parameters computed by the plurality of worker nodes and to distribute aggregated model parameters to the plurality of worker nodes using the networking infrastructure of the computing system.

Implementing parameter server in networking infrastructure for high-performance computing

A method for processing a computing task comprises: dividing multiple computing resources into multiple groups on the basis of topology information describing a connection relationship between the multiple computing resources; selecting at least one computing resource from at least one group of the multiple groups; determining processing performance of processing the computing task with the selected at least one computing resource; and allocating the at least one computing resource on the basis of the processing performance to process the computing task. Accordingly, the multiple computing resources can be utilized sufficiently, so that the computing task can be processed with better processing performance.

Method, apparatus, and computer program product for selecting computing resources for processing computing task based on processing performance

A method comprises determining, in a process of storing data for a computing task of a first dedicated processing resource of a set of dedicated processing resources to the first dedicated processing resource, a size of an available space of a memory of the first dedicated processing resource; in response to the size of the available space of the memory of the first dedicated processing resource being lower than a predetermined threshold value, determining a second dedicated processing resource of the set of dedicated processing resources, a size of an available space of a memory of the second dedicated processing resource is greater than the predetermined threshold value; and causing at least one portion of the data not stored on the memory of the first dedicated processing resource to be stored on the memory of the second dedicated processing resource.

Method, device and computer program product for managing memory of dedicated processing resource

Implementations of the present disclosure relate to a method, apparatus and computer program product for processing a computing task. The method comprises: obtaining status information of multiple computing resources; in response to receiving a neural network model-based computing task, determining configuration information of multiple layers associated with the neural network model; obtaining parameter data associated with at least one part of the multiple layers on the basis of the configuration information; and based on the status information and the parameter data, selecting from the multiple computing resources a group of computing resources for processing the computing task. According to the example implementations of the present disclosure, multiple computing resources may be utilized sufficiently, and it may be guaranteed that a load balance may be stricken between the multiple computing resources.

Method, apparatus, and computer program product for processing computing task

Embodiments of the present disclosure relate to a method and a device for managing a stateful application on a server. The method includes, in response to receiving a first request from a client for initializing the stateful application, allocating a storage resource to the stateful application. The method further includes, in response to receiving a second request from the client for processing data, storing the data in the storage resource. The method also includes enabling the stateful application to process the stored data.

Method and device for managing stateful application on server

A method comprises obtaining hardware information of a plurality of dedicated processing resources, wherein the plurality of dedicated processing resources comprises a first dedicated processing resource and a second dedicated processing resource, and the hardware information comprises first hardware information of the first dedicated processing resource and second hardware information of the second dedicated processing resource. The method further comprises generating a first task based on the first hardware information and a second task based on the second hardware information, and allocating the first task to the first dedicated processing resource and the second task to the second dedicated processing resource. For task scheduling in heterogeneous dedicated processing resources (for example, accelerator devices) scenario, the method generates corresponding kernel codes according to different hardware capabilities. Thus, dynamic optimization for the heterogeneous dedicated processing resources is implemented, thereby improving resource utilization rate and execution efficiency.

Method, device, and computer program product for assigning tasks to dedicated processing resources

Embodiments of the present disclosure provide a method and apparatus for determining a cause of performance degradation of a storage system. The method comprises: monitoring performance of the storage system according to a predetermined policy; generating a performance degradation event from a result of the monitoring based on system performance baseline; in response to performance degradation event, obtaining information about the performance degradation; and offline analyzing the information to determine the cause of the performance degradation. Compared with the prior art, embodiments of the present disclosure can manage the system performance degradation effectively and continuously to minimize running costs and enable current performance profiling tools in the manner of insertion.

Method and device for determining causes of performance degradation for storage systems

Growing community of inventors

Beijing, China

Junping Zhao