One embodiment of a computer-implemented method for processing ray tracing operations in parallel includes receiving a plurality of rays and a corresponding set of importance sampling instructions for each ray included in the plurality of rays for processing, wherein each ray represents a path from a light source to at least one point within a three-dimensional (3D) environment, and each corresponding set of importance sampling instruction is based at least in part on one or more material properties associated with at least one surface of at least one object included in the 3D environment; assigning each ray included in the plurality of rays to a different processing core included in a plurality of processing cores; and for each ray included in the plurality of rays, causing the processing core assigned to the ray to execute the corresponding set of importance sampling instructions on the ray to generate a direction for a secondary ray that is produced when the ray intersects a surface of an object within the 3D environment.

System and method for benchmarking a container orchestration platform in a computing environment uses data contained in a configuration file, which specifies at least one benchmark operation having benchmark actions for container orchestration resource types to be performed in the container orchestration platform, to create a resource manager for each container orchestration resource type specified in the configuration file. Using the created resource managers, a routine for each benchmark action is spawned and executed on container orchestration objects in the container orchestration platform. As the routines are executed, performance metrics are collected and at least some of the collected performance metrics are displayed.

Disclosed are computer-implemented methods, non-transitory computer-readable media, and systems for processing blockchain transactions. An example of a computer-implemented system includes a storage subsystem including one or more storage devices that store blockchain data, and one or more processors configured to support a first thread pool and a second thread pool. The second thread pool is dedicated to the storage subsystem. The system receives M blockchain transactions and executes N blockchain transactions out of the M blockchain transactions in parallel using K threads of the first thread pool. For blockchain transactions distributed to each one of the K threads, one or more coroutines are used for each blockchain transaction so that the blockchain transactions are executed asynchronously using the coroutines. A blockchain block is generated to include the M blockchain transactions and added to a blockchain stored in the storage subsystem.

A method for allocating data processing tasks, an electronic device, and a readable storage medium are provided, which relate to the fields of computer vision and artificial intelligence. The method includes: determining a plurality of data processing tasks of a target application for a graphics processor; and allocating, by using a load balancing strategy, the plurality of data processing tasks to a plurality of worker processes created for the target application, wherein the plurality of worker processes are pre-configured with a corresponding graphics processor resource.

Finding optimal solutions to Web Service Location Allocation Problem (WSLAP) using exhaustive algorithms and exact approaches is not practical. Computational time required to solve WSLAP using exact approaches increases exponentially with the problem size. The disclosure herein generally relates to service deployment, and, more particularly, to a method and system for web service location allocation and virtual machine deployment. The system identifies a plurality of web-services that are associated with the WSLAP and then decomposes the WSLAP to a plurality of sub-problems. For each sub-problem, the system determines at least one non-dominating solution, which are then merged to generate the solution for the WSLAP. The generated solution to the WSLAP can be used for perform Virtual Machine (VM) deployment under uncertainties, using a stochastic approach, wherein the uncertainties refer to dynamic change in requirements in terms of parameters such as but not limited to configuration, and cost.

Embodiments described herein provide a graphics, media, and compute device having a tiled architecture composed of a number of tiles of smaller graphics devices. The work distribution infrastructure for such device enables the distribution of workloads across multiple tiles of the device. Work items can be submitted to any one or more of the multiple tiles, with workloads able to span multiple tiles. Additionally, upon completion of a work item, graphics, media, and/or compute engines within the device can readily acquire new work items for execution with minimal latency.

Systems and methods for virtually partitioning an integrated circuit may include identifying dimensional attributes of a target input dataset and selecting a data partitioning scheme from a plurality of distinct data partitioning schemes for the target input dataset based on the dimensional attributes of the target dataset and architectural attributes of an integrated circuit. The methods described herein may also include disintegrating the target dataset into a plurality of distinct subsets of data based on the selected data partitioning scheme and identifying a virtual processing core partitioning scheme from a plurality of distinct processing core partitioning schemes for an architecture of the integrated circuit based on the disintegration of the target input dataset. Additionally, the architecture of the integrated circuit may be virtually partitioned into a plurality of distinct partitions of processing cores and each of the plurality of distinct subsets of data may be mapped to one of the plurality of distinct partitions of processing cores.

A method for scheduling tasks includes receiving input that was acquired using one or more data collection devices, and scheduling one or more input tasks on one or more computing resources of a network, predicting one or more first tasks based in part on the input, assigning one or more placeholder tasks for the one or more predicted first tasks to the one or more computing resources based in part on a topology of the network, receiving one or more updates including an attribute of the one or more first tasks to be executed as input tasks are executed, modifying the one or more placeholder tasks based on the attribute of the one or more first tasks to be executed, and scheduling the one or more first tasks on the one or more computing resources by matching the one or more first tasks to the one or more placeholder tasks.

The present disclosure provides a logical node distributed signature decision system for a distributed data processing system, including: an initial logical node generating assembly, configured to receive task configuration data input by a user, and generate an initial logical node topology for the distributed data processing system, wherein a source logical node has a specified logical distributed signature, each initial logical node is attached with a candidate logical distributed signature set based on the task configuration data; and a logical distributed signature selecting assembly, configured to, according to a distributed descriptor of an output end of each upstream logical node for which a logical distributed signature is already determined, for each candidate logical distributed signature of a current logical node, compute a cost of data transmission required to transform the distributed descriptor of the tensor of the output end of each upstream logical node into the distributed descriptor.

Techniques for determining reliability of a workload migration activity are disclosed. In one embodiment, sub-tasks associated with the workload migration activity may be determined. Further, statistical data associated with an execution of the sub-tasks corresponding to different instances of the workload migration activity may be retrieved. Furthermore, a reliability model may be trained through machine learning using the statistical data to determine reliability of the workload migration activity. Then, the reliability of a new workload migration activity may be determined using the trained reliability model.