To more properly size resources in a destination to which IT resources will be migrated, a system for appraising a resource configuration estimates a source's load model representing a load of first resources in a first computer system which is the source of migration and estimates a destination's load model representing a load of second resources to be built by migrating the first resources to a second computer system based on the source's load model. The system compares performance requirements of the first resources against the destination's load model and finds the destination's load model that is conformable to the performance requirements. When determining design values of the second resources' configuration, the system corrects those design values based on the destination's load model estimated conformable to the performance requirements to decrease design margins of the resource configuration using a design correction value defined to meet a service level requested.

Embodiments include an asymmetric multiprocessing (AMP) system having a first central processing unit (CPU) cluster comprising a first core type, and a second CPU cluster comprising a second core type, where the AMP system can update a thread metric for a first thread running on the first CPU cluster based at least on: a past shared resource overloaded metric of the first CPU cluster, and on-core metrics of the first thread. The on-core metrics of the first thread can indicate that first thread contributes to contention of the same shared resource corresponding to the past shared resource overloaded metric of the first CPU cluster. The AMP system can assign the first thread to a different CPU cluster while other threads of the same thread group remain assigned to the first CPU cluster. The thread metric can include a Matrix Extension (MX) thread flag or a Bus Interface Unit (BIU) thread flag.

A technique for operating a computer system to support an application, a first application server environment, and a second application server environment includes intercepting a work request relating to the application issued to the first application server environment prior to execution of the work request. A thread adapted for execution in the first application server environment is created. A context is attached to the thread that non-disruptively modifies the thread into a hybrid thread that is additionally suitable for execution in the second application server environment. The hybrid thread is returned to the first application server environment.

Systems and methods are presented for cross-fading (or other multiple clip processing) of information streams on a user or client device, such as a telephone, tablet, computer or MP3 player, or any consumer device with audio playback. Multiple clip processing can be accomplished at a client end according to directions sent from a service provider that specify a combination of (i) the clips involved; (ii) the device on which the cross-fade or other processing is to occur and its parameters; and (iii) the service provider system. For example, a consumer device with only one decoder, can utilize that decoder (typically hardware) to decompress one or more elements that are involved in a cross-fade at faster than real time, thus pre-fetching the next element(s) to be played in the cross-fade at the end of the currently being played element. The next elements(s) can, for example, be stored in an input buffer, then decoded and stored in a decoded sample buffer, all prior to the required presentation time of the multiple element effect. At the requisite time, a client device component can access the respective samples of the decoded audio clips as it performs the cross-fade, mix or other effect. Such exemplary embodiments use a single decoder and thus do not require synchronized simultaneous decodes.

A device implementing adaptive memory performance control by thread group may include a memory and at least one processor. The at least one processor may be configured to execute a group of threads on one or more cores. The at least one processor may be configured to monitor a plurality of metrics corresponding to the group of threads executing on one or more cores. The metrics may include, for example, a core stall ratio and/or a power metric. The at least one processor may be configured to determine, based at least in part on the plurality of metrics, a memory bandwidth constraint with respect to the group of threads executing on the one or more cores. The at least one processor may be configured to, in response to determining the memory bandwidth constraint, increase a memory performance corresponding to the group of threads executing on the one or more cores.

In one embodiment, an apparatus comprises a memory and a processor. The memory is to store visual data associated with a visual representation captured by one or more sensors. The processor is to: obtain the visual data associated with the visual representation captured by the one or more sensors, wherein the visual data comprises uncompressed visual data or compressed visual data; process the visual data using a convolutional neural network (CNN), wherein the CNN comprises a plurality of layers, wherein the plurality of layers comprises a plurality of filters, and wherein the plurality of filters comprises one or more pixel-domain filters to perform processing associated with uncompressed data and one or more compressed-domain filters to perform processing associated with compressed data; and classify the visual data based on an output of the CNN.

Systems and methods are provided for improving compute job distribution using federated computing nodes. This includes identifying a plurality of independently controlled computing nodes which then receive a token such that they can each be identified as being authorized to participate in a federated computing node cluster. Metrics associated with the particular nodes are then received and based on the received metrics compute jobs are assigned to the particular node by assembling a compute job data packet comprising the one or more compute jobs and transmitting the assembled compute job data packet to the particular node. Other features are also described in which assigned compute jobs and/or unrelated compute tasks can be dynamically modified in order to optimize compute job completion based on the received metrics.

Embodiments relate to a computing system for solving differential equations. The system is configured to receive problem packages corresponding to problems to be solved, each comprising at least a differential equation and a domain, and to select a solver of a plurality of solvers, based upon availability of each of the plurality of solvers. Each solver comprises a coordinator that partitions the domain of the problem into a plurality of sub-domains, and assigns each of the plurality of sub-domains to a differential equation accelerator (DEA) of a plurality of DEAs. Each DEA comprises at least two memory units, and processes the sub-domain data over a plurality of time-steps by passing the sub-domain data through a selected systolic array from one memory unit, and storing the processed sub-domain data in the other memory unit, and vice versa.

Provided are a Central Processing Unit (CPU) resource reservation method, apparatus, and device, and a computer-readable memory medium. The method includes: selecting a target working node according to a received Virtual Machine (VM) startup request; obtaining a total number of virtual cores and a number of allocatable physical cores in the target working node statistically; performing calculation to obtain an available CPU quota according to the total number of virtual cores and the number of allocatable physical cores; and performing CPU resource reservation configuration on the target working node by use of the available CPU quota. According to the CPU resource reservation method, the reservation of CPU resources in a VM system may be implemented more flexibly and efficiently.

A technical injection system for injecting a retrained machine learning model is provided, including a. a first computing unit including a first storage medium, wherein the first computing unit is configured for providing the retrained machine learning model; and preprocessing the retrained machine learning model; wherein the retrained machine learning model is stored in the first storage medium; b. a second computing unit comprising a second storage medium and an injection interface, wherein the injection interface is configured for injecting at least one relevant part of the retrained machine learning model after processing from the first storage medium of the first computing unit into the second storage medium of the second computing unit by the injection interface at runtime.