The invention discloses a prefetch-adaptive intelligent cache replacement policy for high performance, in the presence of hardware prefetching, a prefetch request and a demand request are distinguished, a prefetch predictor based on an ISVM (Integer Support Vector Machine) is used for carrying out re-reference interval prediction on a cache line of prefetching access loading, and a demand predictor based on an ISVM is utilized to carry out re-reference interval prediction on a cache line of demand access loading. A PC of a current access load instruction and PCs of past load instructions in an access historical record are input, different ISVM predictors are designed for prefetch and demand requests, reuse prediction is performed on a loaded cache line by taking a request type as granularity, the accuracy of cache line reuse prediction in the presence of prefetching is improved, and performance benefits from hardware prefetching and cache replacement is better fused.

A console includes a CPU and a GPU. Of the CPU and GPU, the CPU acquires an image to be processed, which is an object to be subjected to a support process that is a diagnosis support process or an imaging support process, and distributes a process to any one of the CPU, the GPU, or another GPU to execute the support process according to the content of the support process executed for the image to be processed.

Technologies for switching network traffic include a network switch. The network switch includes one or more processors and communication circuitry coupled to the one or more processors. The communication circuitry is capable of switching network traffic of multiple link layer protocols. Additionally, the network switch includes one or more memory devices storing instructions that, when executed, cause the network switch to receive, with the communication circuitry through an optical connection, network traffic to be forwarded, and determine a link layer protocol of the received network traffic. The instructions additionally cause the network switch to forward the network traffic as a function of the determined link layer protocol. Other embodiments are also described and claimed.

This application provides an application program management method and apparatus. The method is performed in a database cluster system including at least two database nodes, at least one database object is stored in each database node, and the method includes: running an application program on a first database node in a first time period; determining a target database node based on at least one historical database object accessed by the application program in the first time period, where the target database node stores the historical database object; and running the application program on the target database node in a second time period. According to this application, a database node on which an application program runs can be dynamically adjusted, to avoid overload of the database node.

Provided is a neural network accelerator which performs a calculation of a neural network provided with layers, the neural network accelerator including a kernel memory configured to store kernel data related to a filter, a feature map memory configured to store feature map data which are outputs of the layers, and a Processing Element (PE) array including PEs arranged along first and second directions, wherein each of the PEs performs a calculation using the feature map data transmitted in the first direction from the feature map memory and the kernel data transmitted in the second direction from the kernel memory, and transmits a calculation result to the feature map memory in a third direction opposite to the first direction.

Systems and methods for scheduling usage time for programs that can be executed on a hybrid computing system including a quantum processing unit (QPU) and a central processing unit (CPU). Programs can comprise both QPU-executable tasks and CPU-executable tasks. Some programs can be considered high performance programs that are intolerant of interruptions to QPU-executable tasks and some programs can be considered low performance programs that are tolerant of interruptions to QPU-executable tasks. After a high performance program finishes executing QPU-executable tasks on a QPU, a low performance program may execute QPU-executable tasks on the QPU while the high performance program executes CPU-executable tasks on a CPU. Execution of QPU-executable tasks of a low performance program on a QPU can pause or stop if a high performance program is queued.

Examples described herein can be used to determine and suggest a computing resource allocation for a workload request made from an edge gateway. The computing resource allocation can be suggested using computing resources provided by an edge server cluster. Telemetry data and performance indicators of the workload request can be tracked and used to determine the computing resource allocation. Artificial intelligence (AI) and machine learning (ML) techniques can be used in connection with a neural network to accelerate determinations of suggested computing resource allocations based on hundreds to thousands (or more) of telemetry data in order to suggest a computing resource allocation. Suggestions made can be accepted or rejected by a resource allocation manager for the edge gateway and the edge server cluster.

A physical server with an offload card including a SoC (system-on-chip) and a FPGA (field programmable gate array) is disclosed. According to one set of embodiments, the SoC can be configured to offload one or more hypervisor functions from a CPU complex of the server that are suited for execution in software, and the FPGA can be configured to offload one or more hypervisor functions from the CPU complex that are suited for execution in hardware.

An instruction offload manager receives, by a processing device, a first request to execute a program, identifies one or more instructions of the program to be offloaded to a second processing device, where the second processing device includes a same instruction set architecture as the processing device, and provides the one or more instructions to a memory module comprising the second processing device. Responsive to detecting an indication to execute the one or more instructions, the instruction offload manager provides an indication to the second processing device to cause the second processing device to execute the one or more instructions, the one or more instructions to update a portion of a memory space associated with the memory module.

Techniques for determining host computing systems to deploy virtual machines based on disk specifications are disclosed. In one example, a blueprint to deploy a virtual machine in a cloud computing environment may be received. Further, disk specifications required to deploy the virtual machine may be retrieved from the blueprint. Furthermore, candidate storage entities that support the retrieved disk specifications may be determined. A host computing system that has connectivity to the candidate storage entities may be determined. the determined host computing system may be recommended to deploy the virtual machine.