Methods, systems, and devices for modifying subsets of memory bank operating parameters are described. First global trimming information may be configured to adjust a first subset of operating parameters for a set of memory banks within a memory system. Second global trimming information may be configured to adjust a second subset of operating parameters for the set of memory banks. Local trimming information may be used to adjust one of the subsets of the operating parameters for a subset of the memory banks. To adjust one of the subsets of the operating parameters, the local trimming information may be combined with one of the first or second global trimming information to yield additional local trimming information that is used to adjust a corresponding subset of the operating parameters at the subset of the memory banks.

A dynamic random access memory applied to an embedded display port includes a memory core unit, a peripheral circuit unit, and an input/output unit. The memory core unit is used for operating in a first predetermined voltage. The peripheral circuit unit is electrically connected to the memory core unit for operating in a second predetermined voltage, where the second predetermined voltage is lower than 1.1V. The input/output unit is electrically connected to the memory core unit and the peripheral circuit unit for operating in a third predetermined voltage, where the third predetermined voltage is lower than 1.1V.

Technologies for dividing work across one or more accelerator devices include a compute device. The compute device is to determine a configuration of each of multiple accelerator devices of the compute device, receive a job to be accelerated from a requester device remote from the compute device, and divide the job into multiple tasks for a parallelization of the multiple tasks among the one or more accelerator devices, as a function of a job analysis of the job and the configuration of each accelerator device. The compute engine is further to schedule the tasks to the one or more accelerator devices based on the job analysis and execute the tasks on the one or more accelerator devices for the parallelization of the multiple tasks to obtain an output of the job.

An approach is described that provides a distributed directory structure within a storage of an information handling system (a local node). A request is received with the request corresponding to a shared virtual address. The shared virtual address that is shared amongst a number of nodes that includes the local node and some remote nodes. A Global Address Space Directory (GASD) is retrieved that corresponds to a global virtual address space. The GASD is stored in a Coordination Namespace that is stored in a memory that is distributed amongst the nodes. A mapping that is included in the GASD is used to determine the node where the shared virtual address currently resides. The shared virtual address is then accessed from the node where it currently resides.

A method for mitigating cold starts in recommendations includes receiving a request that identifies a requested page and identifying a content vector of the requested page. The content vector is generated based on providing text of the requested page to a neural network text encoder. The method further includes selecting, based on a rank engine and the content vector, a link to a cold start page that does not satisfy a threshold level of interaction data. The rank engine ranks the selected link above a second link to a warm page that does satisfy the threshold level of the interaction data. The method further includes presenting the requested page with the selected link.

Technologies for providing accelerated functions as a service in a disaggregated architecture include a compute device that is to receive a request for an accelerated task. The task is associated with a kernel usable by an accelerator sled communicatively coupled to the compute device to execute the task. The compute device is further to determine, in response to the request and with a database indicative of kernels and associated accelerator sleds, an accelerator sled that includes an accelerator device configured with the kernel associated with the request. Additionally, the compute device is to assign the task to the determined accelerator sled for execution. Other embodiments are also described and claimed.

A compute device to manage workflow to disaggregated computing resources is provided. The compute device comprises a compute engine receive a workload processing request, the workload processing request defined by at least one request parameter, determine at least one accelerator device capable of processing a workload in accordance with the at least one request parameter, transmit a workload to the at least one accelerator device, receive a work product produced by the at least one accelerator device from the workload, and provide the work product to an application.

An embodiment of a semiconductor package apparatus may include technology to provide a first interface between a first storage device and a host device, and provide a second interface directly between the first storage device and a second storage device. Other embodiments are disclosed and claimed.

Embodiments disclosed herein provide a domain aware data migration scheme between processing elements, memory, and various caches in a CC-NUMA system. The scheme creates domain awareness in data migration operations, such as Direct Cache Transfer (DCT) operation, stashing operation, and in the allocation of policies of snoop filters and private, shared, or inline caches. The scheme defines a hardware-software interface to communicate locality information (also referred herein as affinity information or proximity information) and subsequent hardware behavior for optimal data migration, thus overcoming traditional CC-NUMA limitations.

Technologies for offloading acceleration task scheduling operations to accelerator sleds include a compute device to receive a request from a compute sled to accelerate the execution of a job, which includes a set of tasks. The compute device is also to analyze the request to generate metadata indicative of the tasks within the job, a type of acceleration associated with each task, and a data dependency between the tasks. Additionally the compute device is to send an availability request, including the metadata, to one or more micro-orchestrators of one or more accelerator sleds communicatively coupled to the compute device. The compute device is further to receive availability data from the one or more micro-orchestrators, indicative of which of the tasks the micro-orchestrator has accepted for acceleration on the associated accelerator sled. Additionally, the compute device is to assign the tasks to the one or more micro-orchestrators as a function of the availability data.