Methods and systems for performing automated keyboard mapping for virtual desktops are described herein. A system may generate a keyboard mapping table containing a plurality of keyboard inputs. The system may simulate a keyboard input locally at the system and using a virtual desktop hosted by a remote desktop platform to generate keyboard simulation outputs in response to a first keyboard input. In response to determining a discrepancy between the keyboard simulation outputs, the system may modify a key value in the keyboard mapping table resulting in a corrected keyboard mapping table, where the key value corresponds to the first keyboard input. In response to receiving a second keyboard input, the system may apply the corrected keyboard mapping table to display a keyboard output at the virtual desktop hosted by the remote desktop platform, where the second keyboard input has the same key value as the first keyboard input.

Disclosed are aspects of interference-aware virtual machine assignment for systems that include graphics processing units (GPUs) that are virtual GPU (vGPU) enabled. In some examples, an interference function is used to predict interference for assignment of a workload to a graphics processing unit (GPU). The interference function outputs a predicted interference to place the workload on the GPU. The workload is assigned to the GPU based on a comparison of the predicted interference to a plurality of predicted interferences for the workload on various GPUs.

A method includes defining a plurality of variables to modify in a control loop; collecting first data using a first variable of the plurality of variables while executing the control loop, generating a first result based on the collecting first data step, substituting a second variable of the plurality of variables for the first variable, collecting second data using the second variable while executing the control loop, generating a second result based on the collecting second data step, comparing the first result and the second result; and taking an action based on the comparing step.

A method is provided for identifying a data transfer mismatch between a sender and a receiver from among units of a software simulator of a hardware processor. The simulator runs the plurality of the units which communicate with each other via First-In First-Outs (FIFOs). The method counts amounts of data the sender writes to the FIFOs and the receiver reads from the FIFOs for a given data transfer. The method avoids blocking during FIFO reading and writing operations by (i) reading dummy data by the receiver, even if the FIFOs are empty, when the receiver tries reading from the FIFOs, and (ii) discarding written data if the FIFOs are full, when the sender tries writing to the FIFOs. The method identifies mismatches in the amount of data the sender writes to the FIFOs versus the amount of data the receiver reads from the FIFOs for the given data transfer.

Systems and methods for checking program code are disclosed. The method includes executing a first check of the program code, and making a first determination about the first check. Metadata is stored based on making the first determination. A second check of the program code is run based on the metadata, and a result of the second check is stored in memory. The program code is executed based on the second check. In some embodiments, the method for checking program code includes running a simulation of a check of the program code by a processing device; storing a result of the simulation in memory; executing the check of the program code outside of the simulation; making a determination about the check outside of the simulation; retrieving a result stored in the memory based on the determination; and executing the program code based on the check.

Profiling operating efficiency deviations of a computing system includes: generating a profile of expected operating efficiency for a computing system in an ideal configuration; for each of a plurality of alternative configurations of the computing system, wherein each of the alternative configurations includes a variation of the ideal configuration that introduces a deviation in operating efficiency of the computing system, said variation comprising a root cause of the deviation: monitoring operating efficiency of the computing system identifying, from the monitored operating efficiency, a deviation of operating efficiency from the expected operating efficiency; and recording, in a data structure, an association of the deviation and the root cause of the deviation.

An example system includes memory; a central processing unit (CPU) to execute first operations; in-memory execution circuitry in the memory; and detector software to cause offloading of second operations to the in-memory execution circuitry, the in-memory execution circuitry to execute the second operations in parallel with the CPU executing the first operations.

Embodiments determine manikin posture for simulations of real-world environments. An embodiment automatically generates a phrase by performing a hierarchical analysis using data regarding the real-world environment. According to an embodiment, the generated phrase describes a task, to be simulated, performed by a manikin in an environment. In turn, one or more posture engine inputs are determined based on the generated phrase. The posture for the manikin in a simulation of the manikin performing the task in the environment is then automatically determined based on the determined one or more posture engine inputs.

The disclosure provides for repositioning applications from physical devices to a cloud location without removing the applications from the physical devices. This provides advantages of cloud-based availability for the applications while preserving device configurations. Thus, a user may continue to use the local version during transition to cloud usage so that if a problem arises during transition, adverse effects on user productivity are mitigated. Examples include generating, on a device, a first virtualization layer, and uninstalling an application from the first virtualization layer while capturing uninstallation traffic within the first virtualization layer. Examples further include generating, on the device, a second virtualization layer, installing the application in the second virtualization layer, and generating, from the second virtualization layer with the installed application, an application package. Examples are able to position the application package on a remote node for execution.

Various embodiments are generally directed to techniques for dynamic diagnosis and/or prediction of trouble events in a storage cluster system and automated selection of solutions thereto. An apparatus includes a retrieval component to, in response to a trouble event with a first component of a storage cluster system at a usage level under a first usage type, retrieve a component model of a second component associated with a second usage type from a model database, wherein the second usage type comprises operations that differ from operations of the first usage type by no more than a predetermined threshold of difference; and a selection component to apply the first usage level to the component model to derive a resulting level of performance and determine whether to recommend installation of the second component in the first storage cluster system to address the trouble event based on the resulting level of performance.