The present disclosure relates to generating performance profiles of member nodes. A plurality of electronic activities can be accessed. A subset of electronic activities from the plurality of electronic activities can be identified. The subset of electronic activities can be parsed to identify participants of the electronic activities. A second node profile can be accessed for each participant. Participant types can be identified from each second node profiles. A distribution of the subset of electronic activities can be determined. A performance profile can be generated.

Processor(s) of a sampling profiler can identify an activity of multiple activities serviced by a central processing unit (CPU). Each activity can be performed by computing thread(s) of multiple computing threads executing various subroutines of a computer program. The processor(s) can set a target representing a total number of computing threads required to work simultaneously for a maximal use of the CPU. The processor(s) can determine a number of busy computing threads that are performing the activity by using the CPU. The processor(s) can calculate a number of wasted computing threads that are not performing the activity and not using the CPU by computing a difference between the target and the number of busy threads. The processor(s) can compute a CPU time usage for the activity by multiplying time duration of the activity by a value obtained by dividing the number of wasted threads by the number of busy threads.

Aspects of the invention include generating a vector representation of a root node of the error based on a hierarchical topology of a computing system; generating a respective vector representations of each subject matter expert of a plurality of subject matter experts based at least in part on the hierarchical topology; selecting a subject matter expert based at least in part on the vector representation of root cause of the error; and uploading a diagnostic software to the computing system.

Programmable devices, hierarchical parallel machines and methods for providing state information are described. In one such programmable device, programmable elements are provided. The programmable elements are configured to implement one or more finite state machines. The programmable elements are configured to receive an N-digit input and provide a M-digit output as a function of the N-digit input. The M-digit output includes state information from less than all of the programmable elements. Other programmable devices, hierarchical parallel machines and methods are also disclosed.

Aspects of the disclosure provide for mechanisms for memory protection of virtual machines in a computer system. A method of the disclosure includes: determining a plurality of host latency times for a plurality of processor power states of a processor of a host computer system; comparing, by a hypervisor executed on the host computer system, each of the host latency times to a target latency time associated with a virtual machine running on the host computer system; mapping the plurality of processor power states to a plurality of host power states in view of the comparison; and providing the host power states to the virtual machine.

Performance of a legacy application may be characterized for subsequent adjustment of operating parameters when running the legacy application on a newer system. The legacy application is run on an older system and one or more key performance metrics that must be met when the legacy application is run on the newer system are determined along with other performance information useful for later adjustment of operating parameters of the newer system when running the legacy application on the newer system.

An electronic apparatus is provided. The electronic apparatus includes a storage storing a matrix included in an artificial intelligence model, and a processor. The processor divides data included in at least a portion of the matrix by one of rows and columns of the matrix to form groups, clusters the groups into clusters based on data included in each of the groups, and quantizes data divided by the other one of rows and columns of the matrix among data included in each of the clusters.

A method, apparatus and system for anomaly detection in a processor based system includes training a deep learning sequence prediction model using observed baseline behavioral sequences of at least one processor behavior of the processor based system, predicting baseline behavioral sequences from the observed baseline behavioral sequences using the sequence prediction model, determining a baseline reconstruction error distribution profile using the baseline behavioral sequences and the predicted baseline behavioral sequences, predicting test behavioral sequences from observed, test behavioral sequences using the sequence prediction model, determining a testing reconstruction error distribution profile using the observed test behavioral sequences and the predicted test behavioral sequences, and comparing the baseline reconstruction error distribution profile to the testing reconstruction error distribution profile to determine if an anomaly exists in a processor behavior of the processor based system.

Retention of events of an ordered event stream is disclosed. Expiration of events stored in a segment of an ordered event stream (OES) can be desirable. New events are added to a head of an OES segment, and pruning events from a tail of the OES segment can be valuable. Processing applications can register a processing scheme for a segment, e.g., at-least-once processing, exactly-once processing, etc., and can generate checkpoints indicating a degree of advancement in processing events of the segment. The ordered event stream can determine a cut point indicative of a progress point, that before which, events of an OES can be marked as ready for expiration. However, events that are marked for expiration can be retained to allow processing based on a checkpoint, e.g., expiration of the event can be refused until there is an assurance the event was read by the processing application.

A universal device identifier (UDID) service with adjusted attribute distances. In one embodiment, a server includes an electronic processor and a memory storing the UDID service. The electronic processor is configured to receive an identification request regarding a currently-observed device having a first set of device attributes, retrieve any previously-observed devices, determine whether one or more devices are found from retrieving the any of the previously-observed devices, determine all changed device attributes for each device of the one or more devices that are found relative to the first set of device attributes, generate an adjusted attribute distance corresponding to the all changed device attributes for the each device, retrieve a universal device identifier (UDID) of a closest device having a lowest adjusted attribute distance among all adjusted attribute distances that are generated, and assign the UDID of the closest device to the currently-observed device.