Technologies for composing a managed node with multiple processors on multiple compute sleds to cooperatively execute a workload include a memory, one or more processors connected to the memory, and an accelerator. The accelerator further includes a coherence logic unit that is configured to receive a node configuration request to execute a workload. The node configuration request identifies the compute sled and a second compute sled to be included in a managed node. The coherence logic unit is further configured to modify a portion of local working data associated with the workload on the compute sled in the memory with the one or more processors of the compute sled, determine coherence data indicative of the modification made by the one or more processors of the compute sled to the local working data in the memory, and send the coherence data to the second compute sled of the managed node.

Technologies for flexibly compressing data include a computing device having an accelerator complex that is to receive a compression job request and schedule the compression job request for one or more hardware compression resources of the accelerator complex. The accelerator complex is further to perform the compression job request with the one or more hardware compression resources in response to scheduling the compression job request and to communicate uncompressed data and compressed data with an I/O subsystem of the computing device in response to performing the compression job request. Other embodiments are described and claimed.

A system includes a processor configured to receive part identification input identifying a newly installed vehicle part. The processor is also configured to transmit part identification data to a cloud server, responsive to the input and receive electronic control unit (ECU) reconfiguration instructions responsive to the transmission. The processor is further configured to execute the instructions to reconfigure a vehicle ECU identified in the instructions.

A look up table (LUT) includes a decoder configured to decode input signals and to output decoded signals, a storage unit including a plurality of magnetic elements an being configured to select one or more of the plurality of magnetic elements in response to the decoded signals and a signal input/output (TO) unit configured to output an output signal corresponding to the selected one or more magnetic elements and to program the selected one or more magnetic elements by receiving a write signal.

Technologies for offloading I/O intensive workload phases to a data storage sled include a compute sled. The compute sled is to execute a workload that includes multiple phases. Each phase is indicative of a different resource utilization over a time period. Additionally, the compute sled is to identify an I/O intensive phase of the workload, wherein the amount of data to be communicated through a network path between the compute sled and the data storage sled to execute the I/O intensive phase satisfies a predefined threshold. The compute sled is also to migrate the workload to the data storage sled to execute the I/O intensive phase locally on the data storage sled. Other embodiments as also described and claimed.

Technologies for providing data deduplication in a disaggregated architecture include a network device. The network device is to receive, from a compute sled, a request to write a data block to one or more data storage sleds and determine, for each of one or more data sub-blocks within the data block and from deduplication data indicative of physical addresses of data sub-blocks, whether each data sub-block is already stored in a data storage device of a data storage sled. Additionally, the network device is to write, in the deduplication data and in response to a determination that a data sub-block is already stored in a data storage device, a pointer to a physical address of the already-stored data sub-block in association with a logical address of the data sub-block.

Technologies for lifecycle management include multiple computing devices in communication with a lifecycle management server. On boot, a computing device loads a lightweight firmware boot environment. The lightweight firmware boot environment connects to the lifecycle management server and downloads one or more firmware images for controllers of the computing device. The controllers may include baseboard management controllers, network interface controllers, solid-state drive controllers, or other controllers. The lifecycle management server may select firmware images and/or versions of firmware images based on the controllers or the computing device. The computing device installs each firmware image to a controller memory device coupled to a controller, and in use, each controller accesses the firmware image in the controller memory device. The controller memory device may be a DRAM device or a high-performance byte-addressable non-volatile memory. Other embodiments are described and claimed.

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.

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.

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.