International Business Machines Corporation

Bilge Acun

Yoonho Park

Eun Kyung Lee

Alessandro Morari

Paul William Coteus

Alper Buyuktosunoglu

Guojing Cong

Chih-Chieh Yang

An application to run on a hardware processor comprising a plurality of cores may be received. Hardware resource utilization data associated with the application may be obtained. A trained neural network with the hardware resource utilization data associated with the application is run, the trained neural network predicting core temperature associated with running the application on a core of the hardware processor. Based on the core temperature predicted by the trained neural network, the plurality of cores may be controlled to run selective tasks associated with the application.

Learning-based thermal estimation in multicore architecture

A device can be identified using a manufacturing characteristic in the device. Power consumption data associated with a device is received. A power model can be fitted to the power consumption data and at least one parameter is determined based on fitting. A fingerprint of the device can be created based on at least one parameter.

Device identification via chip manufacturing related fingerprints

A data index sequence indexing a dataset is received. A location of a data sample identified by a data index in the data index sequence is determined. A scheme is generated for specifying a data movement based on the location. Responsive to determining that the location is a cache of a process, the data sample in the cache can be reused without having to load the data sample from a storage device.

Locality aware data loading for machine learning

Sub-components assembled into a computer are selected based on sub-component power efficiency levels (for example, low, medium, high) and/or anticipated usage of the computer. Multiple units of each type of sub-component (for example, a CPU) are tested to determine a power efficiency level of each unit. Computers in which sub-component efficiency levels are desired to match an overall computer efficiency level, receive sub-component units of corresponding efficiency level. Computers anticipated to run applications that make intensive use of a given type of sub-component receive the given units having a higher efficiency level. Computers anticipated to run applications that make little use of a given type of sub-component receive a physical unit having a lower efficiency level. Computers anticipated to run a wide variety of applications of no particular usage intensity for a given type of sub-component, receive a unit having an average efficiency level.

Power efficiency-aware node component assembly

An application to run on a computer node comprising a plurality of hardware components is received. Expected performance of the hardware components is received. A power shifting ratio associated with each of the plurality of hardware components for each phase of the application is determined. Power between the hardware components is dynamically shifted based on the power shifting ratio at different phases of the application.

Variation-aware intra-node power shifting among different hardware components

Aspects of the present invention disclose a method, computer program product, and system for scheduling an application. The method includes one or more processors receiving a task, the task includes instructions indicating desired nodes to perform the task through programs. The method further includes one or more processors identifying application characteristic information and node characteristic information associated with nodes within a data center composed of nodes. The application characteristic information includes resource utilization information for applications on nodes within the data center. The method further includes one or more processors determining that the nodes reach a threshold level of power consumption. The threshold level is a pre-set maximum amount of power utilized by a node within the data center. The method further includes one or more processors determining a node consuming an amount of power that is below a threshold level of power consumption in the data center.

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White Plains, NY, United States of America

Bilge Acun