5 results about "Workload scheduling" patented technology

Systems and methods are provided for using historic input power periodic data from a server in an IT data center to train a machine learning (ML) model to obtain forecasted power consumption data of the server for a future time period. Time windows of hotspots or coldspots are then identified in the forecasted power consumption data, hotspots being defined as areas or regions of over-utilization in a time series data, and coldspots being defined as areas or regions of under-utilization in a time series data. The hotspots and coldspots are identified by calculating an exponential mean average (EMA) of the forecasted power consumption data, taking points above the EMA as hotspots and points below the EMA as coldspots. The identified hotspots and coldspots can be used to schedule workloads for a server or a data center, to more efficiently plan existing workloads, or to introduce new workloads at more optimal time periods.

To provide a method for generating a task graph for workload scheduling based on a task graph specification program.SOLUTION: The method includes a task graph specification processor receiving 902 a task graph specification program for execution, executing 904 control flow instructions specified in the task graph specification program to traverse the task graph specification program, generating 906 nodes based on path instructions of the task graph specification program, generating 908, in a task graph, resources and directed edges between the generated nodes based on resource utilization of each node, and outputting 910 the task graph to a command scheduler for scheduling on an acceleratedprocessingdevice (APD) or other device.SELECTED DRAWING: Figure 9

A graphics processing apparatus includes a workload execution circuit to execute workloads and a performance counting circuit to count instances of performance metrics. A workload handling circuit receives commands and responds to performance counter sampling commands that indicate performance counter sampling contexts comprising performance metrics to be sampled and sampling intervals. The workload handling circuit monitors sampling intervals and triggers the workload execution circuit to write out sample values for performance metrics upon interval elapse. A driver receives performance metric sampling indications, allocates memory for sample values, generates performance counter sampling commands, and provides these to the workload handling circuit. The workload handling circuit writes out workload scheduling metadata, configures sampling according to sampling contexts, and manages the writing of sample values either directly to memory or back to the workload handling circuit with associated timestamp information.

Aspects of the present disclosure relate to an apparatus comprising a plurality of processing elements having a spatial layout, and control circuitry to assign workloads to said plurality of processing elements. The control circuitry is configured to, based on a timing parameter, determine one or more active processing elements to deactivate; determine, based on the spatial layout, one or more inactive processing elements to activate; and deactivate said one or more active processing elements and activate said one or more inactive processing elements.

Disclosed are systems and methods for workload scheduling in compute clusters using coolant health monitoring to optimize performance. In-situ sensors measure coolant properties in liquid cooling loops of compute nodes. A processing unit analyzes sensor data to determine coolant health levels and reallocates workloads from nodes with degraded coolant to nodes with higher coolant health levels, preempting thermal failures. A machine learning model processes coolant sensor data and performance metrics to generate cooling efficiency scores for each node. A cluster management module dynamically distributes computational tasks based on cooling system assessments, optimizing cluster efficiency and maintaining performance.