Abstract: Example solutions for performing federated graph queries use schemas and include: generating a logical graph using a query input and generating a physical plan using a query planning schema for double-nested loops, the outer loop for each permutation of data stores in a set of data stores, and the inner loop for each data store—ceasing when the logical graph has been fully consumed. A query cost is determined for each of the permutations, and a physical plan is selected based on the query cost, for example minimizing the query cost, and the query is executed based on the selected physical plan. The cost may be determined based on a weighted combination of factors such as freshness of the data, latency, power consumption, environmental impact (e.g., carbon footprint), energy efficiency, and network burden.

Abstract: In some implementations, a storage node receives data processing instructions from a compute node of a network architecture. The storage node detects a Directed Acyclic Graph (DAG) within the data processing instructions. The DAG includes a plurality of data processing operations arranged within the DAG according to an execution. The data processing operations further include user-defined function (UDF) information associated with a UDF. The storage node may retrieve a first data object from an object store of the distributed storage node based on object keys information within the plurality of data processing operations. The storage node may execute the plurality of data processing operations using the retrieved first data object to generate a DAG execution result, the executing of the plurality of data processing operations based on the execution order. The storage node may communicate the DAG execution result to the compute node in response to the data processing instructions.

Abstract: The disclosure relates to technology for acceleration of GPUs in cloud. Instructions for a computational task are accessed. An allocation of data and instructions is calculated based on the data, the instructions, and dynamic GPU resources. The data and the instructions are provided to the GPUs in accordance with the allocation, which includes scheduling a set of instructions for parallel computation of an operation of the computational task on multiple sub-matrices of a data matrix. Separate portions of information are stored into corresponding different regions of non-transitory memory of a processor core to provide concurrent access to the multiple sub-matrices to the processor core. Each sub-matrix corresponds to a portion of the data matrix for which an operation of the computational task is to be performed. Each sub-matrix contains an element in the data matrix in common with another sub-matrix of the data matrix.