We disclose a system that executes an inferential model in VRAM that is embedded in a set of graphics-processing units (GPUs). The system obtains execution parameters for the inferential model specifying: a number of signals, a number of training vectors, a number of observations and a desired data precision. It also obtains one or more formulae for computing memory usage for the inferential model based on the execution parameters. Next, the system uses the one or more formulae and the execution parameters to compute an estimated memory footprint for the inferential model. The system uses the estimated memory footprint to determine a required number of GPUs to execute the inferential model, and generates code for executing the inferential model in parallel while efficiently using available memory in the required number of GPUs. Finally, the system uses the generated code to execute the inferential model in the set of GPUs.

Adaptive storage processing for storage-as-a-service, including detecting, by a cloud-based monitoring system, a storage system state for a storage system by monitoring the storage system in real-time remotely via a network; selecting, by the cloud-based monitoring system based on the storage system state, an entry in a tunables repository, wherein the entry in the tunables repository comprises a tunable parameter for the storage system state; accessing, by the cloud-based monitoring system via the network, a gateway for the storage system; and modifying, by the cloud-based monitoring system via the gateway, the tunable on the storage system based on the tunable parameter for the storage system state.

Systems, computer-implemented methods and/or computer program products that facilitate management of resources are provided. In one embodiment, a computer-implemented method comprises: employing, by a system operatively coupled to a processor, at least one model to predict respective token needs by a set of processing elements during execution of a workload; and exchanging, by the system, one or more tokens between a subset of the processing elements as a function of the predicted token needs.

A method, system, and apparatus determines that one or more tasks should be relocated from a first processor to a second processor by comparing performance metrics to associated thresholds or by using other indications. To relocate the one or more tasks from the first processor to the second processor, the first processor is stalled and state information from the first processor is copied to the second processor. The second processor uses the state information and then services incoming tasks instead of the first processor.

A chip includes a dedicated scheduler, a general scheduler, and a plurality of hardware accelerators. The hardware accelerators are connected, at least one hardware accelerator is connected to the dedicated scheduler, and at least one hardware accelerator is connected to the general scheduler.

Adaptive storage processing for storage-as-a-service, including detecting, by a cloud-based monitoring system, a storage system state for a storage system by monitoring the storage system in real-time remotely via a network; selecting, by the cloud-based monitoring system based on the storage system state, an entry in a tunables repository, wherein the entry in the tunables repository comprises a tunable parameter for the storage system state; accessing, by the cloud-based monitoring system via the network, a gateway for the storage system; and modifying, by the cloud-based monitoring system via the gateway, the tunable on the storage system based on the tunable parameter for the storage system state.

The invention provides a chip frequency modulation method and apparatus of a computing device, a hash board, a computing device and a storage medium. The chip frequency modulation method comprises: setting a plurality of working frequencies for the operational chip and causing the plurality of cores work at the respective working frequencies; analyzing a computing performance indicator of each core at its current working frequency; and modulating the current working frequency of the core up or down according to the computing performance indicator of the core modulating the frequency of a core with high computing performance up and modulating the frequency of a core with low computing performance down. Therefore, the invention can automatically modulate a frequency corresponding to each core according to the actual computing performance of each core in the operational chip of the computing device, thereby maximizing the computing performance of the cores.

Technologies for dynamic accelerator selection include a compute sled. The compute sled includes a network interface controller to communicate with a remote accelerator of an accelerator sled over a network, where the network interface controller includes a local accelerator and a compute engine. The compute engine is to obtain network telemetry data indicative of a level of bandwidth saturation of the network. The compute engine is also to determine whether to accelerate a function managed by the compute sled. The compute engine is further to determine, in response to a determination to accelerate the function, whether to offload the function to the remote accelerator of the accelerator sled based on the telemetry data. Also the compute engine is to assign, in response a determination not to offload the function to the remote accelerator, the function to the local accelerator of the network interface controller.

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.

Methods and systems for providing services using mixed instance catalogs are described herein. A catalog may comprise a plurality of first virtual machines and a plurality of second virtual machines. The capacity of a first virtual machine may be larger than the capacity of a second virtual machine. Connection requests to access a service associated with the catalog may be distributed among the plurality of first virtual machines and the plurality of second virtual machines.