Vmware, Inc.

Intel Corporation

Bing Niu

Qi Liu

Sheng Fang

Jin He

Xiangjun Song

Xiangdong Che

Bin Wang

Qiang Li

Liu Yang

Xianchao Xu

An example method of offloading a compute task to an accelerator by an application executing in a host cluster includes: sending, by an application executing in an initiator host, the compute task to a virtual accelerator in the initiator host, the virtual accelerator paired with a hardware accelerator in a target host of the host cluster; forwarding, by the virtual accelerator, the compute task to the hardware accelerator in the target host over a network; and receiving, at the application via the virtual accelerator, a result of the compute task from the hardware accelerator over the network.

Virtual accelerators in a virtualized computing system

A computing environment can include a host system that maintains a guest system, and a hardware component configured to implement artificial intelligence ('AI') methods of processing and analyzing date. The guest system can provide a virtual computing environment that receives a request to implement an AI application, and utilize a framework and a guest library to convert data from the AI application into an intermediate representation (“IR”). The host system can receive the IR with a virtual device (“VD”), and utilize an IR backend to translate the IR into hardware operations for the hardware component. Translated hardware operations can be provided to, and carried out by, the hardware component to provide an implementation of the AI application. Results of the hardware operations can be transmitted from the VD of the host system to a VD driver of the guest system, virtualizing the hardware component relative to the guest system.

Virtualizing hardware components that implement AI applications

An example method of virtualizing a hardware accelerator in a host cluster of a virtualized computing system includes: commanding, at an initiator host in the host cluster, a programmable expansion bus device to reconfigure as a virtual accelerator based on specifications of a hardware accelerator in a target host of the host cluster; executing, in the programmable expansion bus device, software to emulate the virtual accelerator as connected to an expansion bus of the initiator host; receiving, at the programmable expansion bus device, compute tasks from an application executing in the initiator host; and sending, to the target host, the compute tasks for processing by the hardware accelerator.

A computing environment can include a host system that maintains a guest system, and a hardware component configured to implement artificial intelligence ('AI') methods of processing and analyzing date. The guest system can provide a virtual computing environment that receives a request to implement an AI application, and utilize a framework and a guest library to convert data from the AI application into an intermediate representation ('IR'). The host system can receive the IR with a virtual device (&#x201c;VD&#x201d;), and utilize an IR backend to translate the IR into hardware operations for the hardware component. Translated hardware operations can be provided to, and carried out by, the hardware component to provide an implementation of the AI application. Results of the hardware operations can be transmitted from the VD of the host system to a VD driver of the guest system, virtualizing the hardware component relative to the guest system.

Various embodiments are generally directed to techniques for employing a hybrid of sequential and parallel processing to perform random sample and consensus (RANSAC). A device to perform RANSAC includes a derivation component to derive a first set of proposed models in parallel from a first set of minimal sample sets of a data set; and a comparison component to recalculate a required quantity of proposed models to derive an accurate model if a proposed model of the first set of proposed models better fits the data set than any proposed model derived prior to derivation of the first set of proposed models, and to determine whether to derive a second set of proposed models following derivation of the first set of proposed models based on a comparison of the required quantity to a quantity of previously derived proposed models that includes the first set. Other embodiments are described and claimed.

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Beijing, China

Bing Niu