Abstract:
A system may provide a visualization function during computational functions performed by a host system. Access to a library of functions including a visualization function is provided. Then, a computing application is executed. The execution of the computing application includes generating multi-dimensional data, invoking the visualization function from the library, and providing a visual representation of at least a portion of the multi-dimensional data for display within the computing application using the visualization function.
Abstract:
A system may provide a visualization function during computational functions performed by a host system. Access to a library of functions including a visualization function is provided. Then, a computing application is executed. The execution of the computing application includes generating multi-dimensional data, invoking the visualization function from the library, and providing a visual representation of at least a portion of the multi-dimensional data for display within the computing application using the visualization function.
Abstract:
An apparatus and method thermally manage a high performance computing system having a plurality of nodes with microprocessors. To that end, the apparatus and method monitor the temperature of at least one of a) the environment of the high performance computing system and b) at least a portion of the high performance computing system. In response, the apparatus and method control the processing speed of at least one of the microprocessors on at least one of the plurality of nodes as a function of at least one of the monitored temperatures.
Abstract:
Embodiments of the invention relate to a system and method for dynamically scheduling resources using policies to self-optimize resource workloads in a data center. The object of the invention is to allocate resources in the data center dynamically corresponding to a set of policies that are configured by an administrator. Operational parametrics that correlate to the cost of ownership of the data center are monitored and compared to the set of policies configured by the administrator. When the operational parametrics approach or exceed levels that correspond to the set of policies, workloads in the data center are adjusted with the goal of minimizing the cost of ownership of the data center. Such parametrics include yet are not limited to those that relate to resiliency, power balancing, power consumption, power management, error rate, maintenance, and performance.
Abstract:
A system may provide a visualization function during computational functions performed by a host system. Access to a library of functions including a visualization function is provided. Then, a computing application is executed. The execution of the computing application includes generating multi-dimensional data, invoking the visualization function from the library, and providing a visual representation of at least a portion of the multi-dimensional data for display within the computing application using the visualization function.
Abstract:
Embodiments of the invention relate to a system and method for dynamically scheduling resources using policies to self-optimize resource workloads in a data center. The object of the invention is to allocate resources in the data center dynamically corresponding to a set of policies that are configured by an administrator. Operational parametrics that correlate to the cost of ownership of the data center are monitored and compared to the set of policies configured by the administrator. When the operational parametrics approach or exceed levels that correspond to the set of policies, workloads in the data center are adjusted with the goal of minimizing the cost of ownership of the data center. Such parametrics include yet are not limited to those that relate to resiliency, power balancing, power consumption, power management, error rate, maintenance, and performance.
Abstract:
In high performance computing, the potential compute power in a data center will scale to and beyond a billion-billion calculations per second (“Exascale” computing levels). Limitations caused by hierarchical memory architectures where data is temporarily stored in slower or less available memories will increasingly limit high performance computing systems from approaching their maximum potential processing capabilities. Furthermore, time spent and power consumed copying data into and out of a slower tier memory will increase costs associated with high performance computing at an accelerating rate. New technologies, such as the novel Zero Copy Architecture disclosed herein, where each compute node writes locally for performance, yet can quickly access data globally with low latency will be required. The result is the ability to perform burst buffer operations and in situ analytics, visualization and computational steering without the need for a data copy or movement.
Abstract:
A server is implemented within disk drive device or other drive device. The server-drive device may be used within a server tray having many disk drive devices, along with multiple other server trays in a cabinet of trays. One or more disk drive devices may be implemented in a server tray. The server-drive device may also be used in other applications. By implementing the server within the disk drive, valuable space is saved in a computing device.
Abstract:
A server is implemented within disk drive device or other drive device. The server-drive device may be used within a server tray having many disk drive devices, along with multiple other server trays in a cabinet of trays. One or more disk drive devices may be implemented in a server tray. The server-drive device may also be used in other applications. By implementing the server within the disk drive, valuable space is saved in a computing device.
Abstract:
Embodiments of the invention relate to a system and method for dynamically scheduling resources using policies to self-optimize resource workloads in a data center. The object of the invention is to allocate resources in the data center dynamically corresponding to a set of policies that are configured by an administrator. Operational parametrics that correlate to the cost of ownership of the data center are monitored and compared to the set of policies configured by the administrator. When the operational parametrics approach or exceed levels that correspond to the set of policies, workloads in the data center are adjusted with the goal of minimizing the cost of ownership of the data center. Such parametrics include yet are not limited to those that relate to resiliency, power balancing, power consumption, power management, error rate, maintenance, and performance.