A system and method for processing data by accessing data sets for a plurality of variables in at least one data store; associating a plurality of the data sets as at least one variable type; storing in a data store a plurality of operation definitions defining a plurality of operations on at least one of said at least one variable type; receiving from a user interface a selection of at least one operation definition and at least one data set of said at least one variable type operated on by the selected at least one operation definition; and processing the at least one data set in response to the selection according to the at least one operation definition to generate a derived data set.

Specifications are input, comprising: a plurality of lanes in an environment for a controlled system; a plurality of lane maneuvers associated with the plurality of lanes; a plurality of lane subconditions associated with the controlled system; and a rule set comprising a plurality of rules, wherein a rule in the rule set specifies a rule condition and a rule action to take when the rule condition is satisfied, wherein the rule condition comprises a corresponding set of lane subconditions, and wherein the rule action comprises a corresponding lane maneuver. The controlled system is automatically navigated dynamically, at least in part by: monitoring the plurality of lane subconditions; evaluating rule conditions associated with the plurality of rules in the rule set to determine one or more rules whose corresponding rule conditions has been met; and executing one or more lane maneuvers that correspond to the one or more determined rules.

Embodiments are generally directed to a system and method for adapting executable object to a processing unit. An embodiment of a method to adapt an executable object from a first processing unit to a second processing unit, comprises: adapting the executable object optimized for the first processing unit of a first architecture, to the second processing unit of a second architecture, wherein the second architecture is different from the first architecture, wherein the executable object is adapted to perform on the second processing unit based on a plurality of performance metrics collected while the executable object is performed on the first processing unit and the second processing unit.

Disclosed herein is a method including receiving, from a user application, data to be transmitted from a source address to a destination address using a single connection through a network; and splitting the data into a plurality of packets according to a communication protocol. For each packet of the plurality of packets, a respective flowlet for the packet to be transmitted in is determined from a plurality of flowlets. Assignment of the flowlets to the packets can be dynamically adjusted based on utilization of the flowlets.

The disclosure herein describes elastically managing the execution of workers of multi-worker workloads on accelerator devices. A first worker of a workload is executed on an accelerator device during a first time interval. A first context switch point is identified when the first worker is in a first worker state. At the identified context switch point, a first memory state of the first worker is stored in a host memory and the accelerator device is configured to a second memory state of the second worker. The second worker is executed during a second time interval and a second context switch point is identified at the end of the second time interval when the second worker is in a state that is equivalent to the first worker state. During the intervals, collective communication operations between the workers are accumulated and, at the second context switch point, the accumulated operations are performed.

The disclosure herein describes elastically managing the execution of workers of multi-worker workloads on accelerator devices. A first worker of a workload is executed on an accelerator device during a first time interval. A first context switch point is identified when the first worker is in a first worker state. At the identified context switch point, a first memory state of the first worker is stored in a host memory and the accelerator device is configured to a second memory state of the second worker. The second worker is executed during a second time interval and a second context switch point is identified at the end of the second time interval when the second worker is in a state that is equivalent to the first worker state. During the intervals, collective communication operations between the workers are accumulated and, at the second context switch point, the accumulated operations are performed.

A computer implemented method of executing a software module includes a machine learning algorithm as an executable software component configurable to approximate a function relating a domain data set to a range data set; a data store; and a message handler as an executable software component arranged to receive input data and communicate output data for the module, wherein the message handler is adapted to determine domain parameters for the algorithm based on the input data and to generate the output data based on a result generated by the algorithm, the method including generating a message as input data for the module, the message including instructions for execution by the module to effect a modification of the machine learning algorithm of the module.

A computer implemented method of executing a software module includes a machine learning algorithm as an executable software component configurable to approximate a function relating a domain data set to a range data set; a data store; and a message handler as an executable software component arranged to receive input data and communicate output data for the module, wherein the message handler is adapted to determine domain parameters for the algorithm based on the input data and to generate the output data based on a result generated by the algorithm, the method including generating a message as input data for the module, the message including instructions for execution by the module to effect a modification of the machine learning algorithm of the module.

A clock and data recovery (CDR) system includes a correlator configured to receive data, determine a first value of the received data, and output a second value corresponding to the received data, an accumulator configured to generate an accumulation value by accumulating the second value output from the correlator and output the accumulation value, and a state machine configured to determine whether a repeating pattern is present in the CDR system based on the accumulation value.

Real time process control using digital twins. In more detail, the present disclosure relates to the field of modeling distributed event-discrete systems using digital twins and subsequent use of the models for real time control of distributed even-discrete systems. There is provided a virtual twin engine for control of a distributed even-discrete system in real-time. The virtual twin engine has installed at least one executable modeling software kernel which runs subsystem use models in relation to subsystem clusters of the distributed event-discrete system. Also, the virtual twin engine operates the at least one digital twin in a passive manner through real time access to the modeling software kernel modeling the subsystem use model of the at least one digital twin.