International Business Machines Corporation

Symantec Corporation

Reefedge, Inc.

Yoonho Park

Bryan Savoye Rosenburg

Carlos Henrique Andrade Costa

Chen-Yong Cher

Kyung D Ryu

Kenneth S Sabir

Eun Kyung Lee

Chitra Venkatramani

Bilge Acun

Philippe L Selo

Paul William Coteus

Charles Ray Johns

Robert William Wisniewski

Sandeep Kishan Singhal

Lisa Amini

Todd Alan Inglett

Ajei Sarat Gopal

Rangachari Anand

Abdullah Kayi

Alessandro Morari

Richard Kent Neves

Alper Buyuktosunoglu

Hubertus Franke

Eugen Schenfeld

Joel Leonard Wolf

Deepak Srinivas Turaga

Daniel Michael Kuchta

Hartmut Penner

Nagui Halim

Henrique Andrade

James R Giles

Xiaolan Zhang

Kirsten Weale Hildrum

Laurent Schares

Marc A Taubenblatt

Deepak Rajan

Rohit Wagle

I-Hsin Chung

Ranjita Bhagwan

Nikhil Bansal

Giacomo Domeniconi

Guilherme C Januario

Phillippe L Selo

Henrique C M Andrade

Navendu Jain

Based on historic job data, a computer processor can predict a configuration of a computer node for running a future computer job. The computer processor can pre-configure the computer node based on the predicted configuration. Responsive to receiving a submission of a job, the computer processor can launch the job on the pre-configured computer node.

Job-launch time reduction by node pre-configuration

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 method for managing a network queue memory includes receiving sensor information about the network queue memory, predicting a memory failure in the network queue memory based on the sensor information, and outputting a notification through a plurality of nodes forming a network and using the network queue memory, the notification configuring communications between the nodes.

Network resiliency through memory health monitoring and proactive management

Methods and systems for shuffling data are described. A processor may generate pair data from source data. The processor may insert the pair data into local tuple spaces. In response to a request for a particular key, the processor may determine a presence of the requested key in a global tuple space. The processor may, in response to a presence of the requested key in the global tuple space, update the global tuple space. The update may be based on the pair data among the local tuple spaces including the existing key. The processor may, in response to an absence of the requested key in the global tuple space, insert pair data including the missing key from the local tuple spaces into the global tuple space. The processor may fetch the requested pair data, and may shuffle the fetched data to generate a dataset.

Data shuffling with hierarchical tuple spaces

Methods and systems for shuffling data to generate a dataset are described. A first map module may generate first pair data, and a second map module may generate second pair data, from source data. The first map module may insert the first pair data into a first local tuple space accessible to the first map module. The second map module may insert the second pair data into a second local tuple space accessible to the second map module. A shuffle module may request pair data that includes a particular key. The first and second pair data may be inserted into a global tuple space accessible by the first and second map modules. The shuffle module may identify the requested pair data in the global tuple space, and may fetch the identified pair data from a memory. The shuffle module may shuffle the fetched pair data to generate the dataset.

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.

Job scheduling based on node and application characteristics

A data-centric reduction method, system, and computer program product include configuring a similarity threshold and a correlation threshold for an entire data set from at least two back-end nodes, reducing the entire data set to a reduced data set from the at least two back-end nodes sent to a front-end node by removing data based on the similarity threshold and the correlation threshold, and after the front-end receives the reduced data set, reconstructing the entire data set from the reduced data set using the similarity threshold and correlation threshold.

Data-centric reduction network for cluster monitoring

A method is disclosed, as well as an associated apparatus and computer program product, for checkpointing using a plurality of communicatively coupled compute nodes. The method comprises acquiring health information for a first node of the plurality of compute nodes, and determining a first failure probability for the first node using the health information. The first failure probability corresponds to a predetermined time interval. The method further comprises selecting a second node of the plurality of compute nodes as a partner node for the first node. The second node has a second failure probability for the time interval. A composite failure probability of the first node and the second node is less than the first failure probability. The method further comprises copying checkpoint information from the first node to the partner node.

Checkpointing using compute node health information

A method for providing selective memory error protection responsive to a predictable failure notification associated with at least one portion of a memory in a computing system includes: obtaining an active error correcting code (ECC) configuration corresponding to the portion of the memory; determining whether the active ECC configuration is sufficient to correct at least one error in the portion of the memory affected by the predictable failure notification; when the active ECC configuration is insufficient to correct the error, determining whether data corruption can be tolerated by an application running on the computing system; when data corruption cannot be tolerated by the application, determining whether a stronger ECC level is available and, if a stronger ECC level is available, increasing a strength of the active ECC configuration; and when data corruption can be tolerated, performing page reassignment and aggregation of non-critical data.

Method and apparatus for selective and power-aware memory error protection and memory management

A method for selective duplication of subtasks in a high-performance computing system includes: monitoring a health status of one or more nodes in a high-performance computing system, where one or more subtasks of a parallel task execute on the one or more nodes; identifying one or more nodes as having a likelihood of failure which exceeds a first prescribed threshold; selectively duplicating the one or more subtasks that execute on the one or more nodes having a likelihood of failure which exceeds the first prescribed threshold; and notifying a messaging library that one or more subtasks were duplicated.

Methods, apparatus and system for selective duplication of subtasks

Data processing techniques are provided to increase a computational speed of iterative computations that are performed over a domain of data points, such as stencil computations. For example, a method includes loading a set of domain data points into a cache memory; obtaining an iteration count T, and a base stencil operator having a first set of coefficients; generating a convolved stencil operator having a second set of coefficients, wherein the convolved stencil operator is generated by convolving the base stencil operator with itself at least one time; and iteratively processing the set of domain data points in the cache memory using the convolved stencil operator no more than T/2 iterations to obtain final processing results. The final processing results are similar to processing results that would be obtained by iteratively processing the set of domain data points using the base stencil operator for the iteration count T.

Kernel convolution for stencil computation optimization

A method, apparatus, and computer program product are provided for thermal- and spatial-aware task scheduling. The method may include monitoring a temperature for each core of a central processing unit having a plurality of cores; determining, from the monitoring, a set of hotspot cores from the plurality of cores determining temperature information and distance information for each hotspot core in the set of hotspot cores relative to each of the other cores on the central processing unit; calculating a placement metric for each core of the central processing unit based at least on the determined distance information and the determined temperature information; and scheduling a task by allocating the task to one or more cores of the central processing unit according to the placement metric.

Thermal-and spatial-aware task scheduling

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Chappaqua, NY, United States of America

Yoonho Park