Abstract: In response to a graphics memory allocation request generated during the running of a target task and for graphics memory needed during running of the target task, target data generated during running of each sub-task of multiple sub-tasks is classified, where a type of the target data comprises at least first data, and where the first data is not used by a subsequent sub-task. Multiple target graphics memory pools are allocated to the multiple sub-tasks. Each target graphics memory pool of the multiple target graphics memory pools is divided into at least one graphics memory block based on a type of the target data, where the at least one graphics memory block includes at least a first graphics memory block corresponding to the first data, and where multiple first graphics memory blocks corresponding to the multiple sub-tasks are mapped to a same target physical memory address.

Abstract: 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

Abstract: Described herein are glue degrader compounds, their various targets, their preparation, pharmaceutical compositions comprising them, and their use in the treatment or prevention of conditions, diseases, and disorders mediated by various target proteins.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Embodiments of the present disclosure relate to a method and an apparatus for data backup. The method comprises selecting, from a service store, a backup service for an application. The method further comprises setting, for the application, configuration information associated with the selected backup service. Additionally, the method further comprises in response to the backup service being enabled, deploying a backup service agent corresponding to the backup service to a source node associated with the application based on the configuration information, such that data of the application is backed up from the source node to a target node via the backup service agent.

Abstract: 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.

Abstract: Systems and methods are provided to configure a replication system to reduce latency of data read access operations in a cloud computing environment. Intelligent routing decision systems and methods are implemented in conjunction with a distributed replication system in a cloud computing environment to direct write requests to a primary site (e.g., production site) by default, and to automatically and intelligently direct read requests to a target site (e.g., primary site or replica site) to reduce data read latency and/or bandwidth. A reduction in read delay is achieved, in part, by intelligently selecting a target site (e.g., data center) in a cloud computing environment which is close or closest in distance to a requesting entity (e.g., user or application) requesting data, thereby reducing a propagation delay and/or latency in accessing and downloading data from the target site.

Abstract: A method for processing a computing task comprises: determining parameter data of multiple layers associated with a neural network model in response to receiving a computing task based on the neural network model; ranking at least one part of the multiple layers on the basis of the parameter data so as to obtain a layer list; ranking multiple computing resources on the basis of status information of the multiple computing resources so as to obtain a resource list; and determining a mapping between a corresponding layer among the multiple layers and a corresponding computing resource among the multiple computing resources on the basis of the layer list and the resource list, where the mapping indicates one computing resource among the multiple computing resources is to process parameters associated with one layer among the multiple layers.

Abstract: A method, apparatus and computer program product for managing memories of computing resources is disclosed. In the method, a computing task processed by a first computing resource in a group of computing resources is determined. In response to a second memory of a second computing resource other than the first computing resource in the group of computing resources being allocated to the computing task, a second access speed with which the first computing resource accesses the second memory is determined. A target computing resource is selected from the group of computing resources based on an access speed with which the first computing resource accesses a target memory of the target computing resource, where the access speed is higher than the second access speed. At least one part of data in the second memory is migrated to the target memory.

Abstract: The present disclosure relates to compositions and methods for treating APOC3-related diseases such as: hypertriglyceridemia (e.g., Type V Hypertriglyceridemia), abnormal lipid metabolism, abnormal cholesterol metabolism, atherosclerosis, hyperlipidemia, diabetes, including Type 2 diabetes, obesity, cardiovascular disease, and coronary artery disease, among other disorders relating to abnormal metabolism or otherwise, using a therapeutically effective amount of a RNAi agent to APOC3.

Abstract: A method of scheduling a dedicated processing resource includes: obtaining source code of an application to be compiled; extracting, during compiling of the source code, metadata associated with the application, the metadata indicating an amount of the dedicated processing resource required by the application; and obtaining, based on the metadata, the dedicated processing resource allocated to the application. In this manner, performance of the dedicated processing resource scheduling system and resource utilization is improved.

Abstract: Techniques for managing a network system are disclosed. For example, a method comprises: receiving, at a network adapter, an access request from a client, the access request including first header information and first payload information; transmitting the first header information to a load balancing server associated with the network adapter; receiving the updated first header information from the load balancing server, wherein the updated first header information is determined by the load balancing server based on the received first header information and an identifier of the load balancing server, and wherein the updated first header information indicates a destination server to which the access request is to be transmitted; and, generating, based on the updated first header information and the first payload information, an updated access request. Accordingly, the processing capability of load balancing servers and the performance of load balancing servers in a network system may be improved effectively.