A method implements a dashboard runtime that comprises a custom visualization component to render a visual representation of a data items of one or more queried datasets in a GUI; at least one query associated with at least the custom component; and an API to provide communication between the custom component and the at least one query. The API detects a user interaction with of a portion of the custom component via a first message that informs the dashboard runtime of the detected user interaction; passes a second message to the at least one query instructing the at least one query to rerun to receive an updated query dataset; and passes the updated dataset via a third message to the custom component and to any other components associated with the at least one query, such that the custom component and the other components automatically render updated visual representations of the updated dataset.

Disclosed herein are a convolutional layer acceleration unit, an embedded system having the convolutional layer acceleration unit, and a method for operating the embedded system. The method for operating an embedded system, the embedded system performing an accelerated processing capability programmed using a Lightweight Intelligent Software Framework (LISF), includes initializing and configuring, by a parallelization managing function entity (FE), entities present in resources for performing mathematical operations in parallel, and processing in parallel, by an acceleration managing FE, the mathematical operations using the configured entities.

A storage controller includes a processing device to send a Non-Volatile Memory Express over Fibre Channel (NVMe/FC) command to a submission queue without routing the NVMe/FC command through a kernel space, the submission queue being reserved for direct access by an initiator device to a user space of the storage controller.

A method for use with a computing device. The method may include receiving a data set including a plurality of univariate data points and determining a target kernel bandwidth for a kernel density estimator (KDE). Determining the target kernel bandwidth may include computing a plurality of sample KDEs and selecting the target kernel bandwidth based on the sample KDEs. The method may further include computing the KDE for the data set using the target kernel bandwidth. For one or more tail regions of the data set, the method may further include computing one or more respective tail extensions. The method may further include computing and outputting a renormalized piecewise density estimator that, in each tail region, equals a renormalization of the respective tail extension for that tail region, and, outside the one or more tail regions, equals a renormalization of the KDE.

A method and a device for managing a node includes: initiating, by an application program, a first request by calling an interface function, where the first request is used to perform an operation on a feature node in a kernel; searching, based on a keyword of the interface function, a table used for node management for an entry corresponding to the feature node, where the entry includes a node identifier of the feature node and a user handle identifier of the feature node; and performing, by the user program, the operation on the feature node based on the user handle identifier. A program running in user space can be prevented from directly accessing a feature node in kernel space, thereby improving system security.

A processor core is configured to execute a parent task that is described by a data structure stored in a memory. A coprocessor is configured to dispatch a child task to the at least one processor core in response to the coprocessor receiving a request from the parent task concurrently with the parent task executing on the at least one processor core. In some cases, the parent task registers the child task in a task pool and the child task is a future task that is configured to monitor a completion object and enqueue another task associated with the future task in response to detecting the completion object. The future task is configured to self-enqueue by adding a continuation future task to a continuation queue for subsequent execution in response to the future task failing to detect the completion object.

Various techniques for accelerating Smith-Waterman sequence alignments are provided. For example, threads in a group of threads are employed to use an interleaved cell layout to store relevant data in registers while computing sub-alignment data for one or more local alignment problems. In another example, specialized instructions that reduce the number of cycles required to compute each sub-alignment score are utilized. In another example, threads are employed to compute sub-alignment data for a subset of columns of one or more local alignment problems while other threads begin computing sub-alignment data based on partial result data received from the preceding threads. After computing a maximum sub-alignment score, a thread stores the maximum sub-alignment score and the corresponding position in global memory.

Apparatuses, systems, and techniques to receive, by a processor of a computer system, one or more operations for a kernel; automatically generate, by the processor, one or more operators that perform the one or more operations on elements of one or more input data structures; and automatically generate, by the processor, the kernel that comprises the one or more operators.

A method for running an application via an operating system executing on a computing device is disclosed. In an embodiment, the method involves subjecting an API call to a complimentary application virtualization layer, after the API call is subjected to the complimentary application virtualization layer, subjecting the API call to a primary application virtualization layer, and after the API call has been subjected to the complimentary application virtualization layer and to the primary application virtualization layer, forwarding the API call to the operating system for processing in the kernel-space.

A model coordination method for a first device is provided. The first device stores at least one model segment. The at least one model segment is configured to realize a part of functions of a preset model. The method includes: determining a first model segment from the at least one model segment stored in the first device, wherein when the first model segment is executed and a second model segment is executed by a second device, a part of or all the functions of the preset model are realized, the second model segment is one of at least one model segment stored in the second device, and the at least one model segment stored in the second device is configured to realize a part of the functions of the preset model. A model coordination apparatus is also provided.