Abstract: Disclosed are an instruction execution method and apparatus for graph computation. The method includes the following steps: S1: sending operators of each node in a computational graph used for neural network computation to an operator interpreter; S2: building, by the operator interpreter, instructions in operation; S3: defining an instruction dependency relationship; S4: building an instruction dependency relationship graph; S5: building a topological order of parallel instructions; S6: scheduling the parallel instructions to hardware resources; S7: building shortest schedules for the parallel instructions: the shortest time required to execute the parallel instructions under the condition of limited hardware resources; and S8: releasing the completed instructions.

Abstract: A memory optimization method includes: compiling a neural network into a computational graph for neural network computation on a computer; transforming the computational graph into a topological graph; constructing a life cycle relationship graph of tensor variables in the computational graph; and analyzing a life cycle relationship among tensor variables in a node of the computational graph; iteratively merging those tensor variables connected by lines of the second type and caching into a memory any tensor variable that goes beyond a number of idle registers and is not allocated to a register, until all tensor variables that go beyond the number of the idle registers and are not allocated to registers are cached into the memory; caching any node of the life cycle relationship graph with a degree smaller than a number of registers into a stack.

Abstract: The present disclosure discloses a method for execution of a computational graph in a neural network model and an apparatus thereof, including: creating task execution bodies on a native machine according to a physical computational graph compiled and generated by a deep learning framework, and designing a solution for allocating a plurality of idle memory blocks to each task execution body, so that the entire computational graph participates in deep learning training tasks of different batches of data in a pipelining and parallelizing manner.

Abstract: Disclosed are an intermediate representation method and apparatus for parallel execution of graph computation. The method includes the following steps: S1: compiling a neural network into a computational graph on a computer; S2: defining branch states of tensor variables in the computational graph; S3: defining a data dependency relationship of the tensor variables in the computational graph; S4: defining a control dependency relationship of the tensor variables in the computational graph; S5: building a data dependency relationship graph of the tensor variables in the computational graph; S6: building a control dependency relationship graph of the tensor variables in the computational graph; and S7: transforming control dependencies into data dependencies. The present application derives, based on the dependency relationship, a parallel computing method that can execute the branch threads in parallel in the global computational graph, and optimizes the compilation efficiency of the computational graph.

Abstract: The present disclosure discloses a method for execution of a computational graph in a neural network model and an apparatus thereof, including: creating task execution bodies on a native machine according to a physical computational graph compiled and generated by a deep learning framework, and designing a solution for allocating a plurality of idle memory blocks to each task execution body, so that the entire computational graph participates in deep learning training tasks of different batches of data in a pipelining and parallelizing manner.

Abstract: Technologies are generally described herein for storing log records in non-volatile memory. Transaction data may be accessed that is associated with one or more transactions that modify a data storage device. The transaction data may be stored in a cache that is coupled to the data storage device. The log records corresponding to transaction data may also be stood in a non-volatile memory (NVM) that is coupled to the data storage device. Log records may be synchronized with the data storage device.