Abstract: A physical system is simulated using a model including a plurality of elements in a mesh or grid. The elements are divided into partitions processed by different processing units. For some time steps, state data is transmitted between partitions and used to calculate flux data for updating the state of edge elements of the partitions. Periodically, transmission of state data is suppressed, and flux data is obtained by linear interpolation based on past flux data. Alternatively, flux data is obtained by processing state variables of an edge element and past flux data using a machine learning model, such as a DNN. Whether to suppress transmission of state data may be determined based on one or both of (a) uncertainty in an output of the machine learning model (e.g., Bayesian neural network) and (b) complexity of model of the physical system (e.g., spatial or temporal gradients).

Abstract: An approach is provided for managing near-memory processing commands (“PIM commands”) from multiple processor threads in a manner to prevent interference and maintain correctness at near-memory processing elements. A memory controller uses thread identification information and last command information to issue a PIM command sequence from a first processor thread, directed to a PIM-enabled memory element, while deferring the issuance of PIM command sequences from other processor threads, directed to the same PIM-enabled memory element. After the last PIM command in the PIM command sequence for the first processor thread has been issued, a PIM command sequence for another processor thread is issued, and so on. The approach allows multiple processor threads to concurrently issue fine grained PIM commands to the same PIM-enabled memory element without having to be aware of address-to-memory element mapping, and without having to coordinate with other threads.

Abstract: A physical system is simulated using a model including a plurality of elements in a mesh or grid. The elements are divided into partitions processed by different processing units. For some time steps, flux data is transmitted between partitions for updating the state of edge elements of the partitions. Periodically, transmission of flux data is suppressed and flux data is obtained by linear interpolation based on past flux data. Alternatively, flux data is obtained by processing state variables of an edge element and past flux data using a machine learning model, such as a DNN.

Abstract: An approach is provided for using machine learning to provide compensation for roundoff error in algorithmic computations. The approach includes training a machine learning model based low precision data and corresponding high precision data. The low precision data includes pairs of low precision values of a specific datatype that correspond to pairs of high precision values from the high precision data. The high precision data includes pairs of high precision values of a specific datatype that correspond to the pairs of low precision values from the low precision data. When the machine learning model has been trained, the machine learning model is used as a basis for determining a compensation value is used to compensate for roundoff error in a particular algorithmic computation. Techniques discussed herein provide compensation for roundoff error during otherwise unstable computations, enabling high-performance computing and other scientific applications to use lower precision data types more readily.