Abstract: Disclosed are an optimization method and apparatus for compiling computation graph. The optimization method includes the following steps: step S1: converting a computation graph into an intermediate representation; step S2: analyzing a dependency relationship; step S3: constructing a work stack; step S4: performing initialization to achieve a nonactivated state; step S5: popping out stack top node elements, and updating an input node set in a current round of iteration; step S6: adding the stack top node elements that depend on step S5 to a stack top position in sequence until the work stack is empty; step S7: implementing an intermediate representation in a fixed node state using a bit vector; and step S8: allocating registers for effective tensor variables contained in nodes of the intermediate representation in the fixed node state.

Abstract: The present disclosure provides a neural network computing-oriented modeling method and apparatus for distributed data routing. The method includes the following steps: S1, designing the distributed attribute of a physical tensor: abstracting a mapping relationship between a logic tensor and the physical tensor into three distributed attributes including a broadcast attribute, a scatter attribute and a local reduction attribute; S2, deducing the distributed attribute of an output tensor: specifying the distributed attribute of an input tensor, and then deducing the legal distributed attribute of the output tensor according to the known distributed attribute of the input tensor; and S3, judging, according to the distributed attribute situation, whether an intermediate communication primitive needs to be inserted to obtain the distributed attribute of a local physical tensor.

Abstract: The present disclosure provides a neural network computing-oriented modeling method and apparatus for distributed data routing. The method includes the following steps: S1, designing the distributed attribute of a physical tensor: abstracting a mapping relationship between a logic tensor and the physical tensor into three distributed attributes including a broadcast attribute, a scatter attribute and a local reduction attribute; S2, deducing the distributed attribute of an output tensor: specifying the distributed attribute of an input tensor, and then deducing the legal distributed attribute of the output tensor according to the known distributed attribute of the input tensor; and S3, judging, according to the distributed attribute situation, whether an intermediate communication primitive needs to be inserted to obtain the distributed attribute of a local physical tensor.

Abstract: A method for managing an object-based storage system is provided. The object-based storage system includes root objects, partition objects, collection objects, user objects and function objects. The function objects include user function objects and system function objects. The user function objects respond to the client request, so that the existing data-processing tasks requested by the client are moved to the OBSD to be completed, so as to achieve the purposes for saving the network storage bandwidth and improving the security of the stored data, etc. The system function objects execute the system management function, so that the management of the bottom of disks management of the file system and database system is completed by the OBSD. That can be easily realized in automatic data backup, load balancing and other functions in the storage system. The user function objects and system function objects can be customized by the user.

Abstract: A method for managing an object-based storage system is provided. The object-based storage system includes root objects, partition objects, collection objects, user objects and function objects. The function objects include user function objects and system function objects. The user function objects respond to the client request, so that the existing data-processing tasks requested by the client are moved to the OBSD to be completed, so as to achieve the purposes for saving the network storage bandwidth and improving the security of the stored data, etc. The system function objects execute the system management function, so that the management of the bottom of disks management of the file system and database system is completed by the OBSD. That can be easily realized in automatic data backup, load balancing and other functions in the storage system. The user function objects and system function objects can be customized by the user.