Some embodiments provide novel circuits for augmenting the functionality of a data plane circuit of a forwarding element with one or more field programmable circuits and external memory circuits. The external memories in some embodiments serve as deep buffers that receive through one or more FPGAs a set of data messages from the data plane (DP) circuit to store temporarily. In some of these embodiments, one or more of the FPGAs implement schedulers that specify when data messages should be retrieved from the external memories and provided back to the data plane circuit for forwarding through the network. For instance, in some embodiments, a particular FPGA can perform a scheduling operation for a first set of data messages stored in its associated external memory, and can direct another FPGA to perform the scheduling operation for a second set of data messages stored in the particular FPGA's associated external memory. Specifically, in these embodiments, the particular FPGA determines when the first subset of data messages stored in its associated external memory should be forwarded back to the data plane circuit to forward to data messages in the network, while directing another FPGA to determine when a second subset of data messages stored in the particular FPGA's external memory should be forwarded back to the data plane circuit.

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.

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.

Technologies for lifecycle management include multiple computing devices in communication with a lifecycle management server. On boot-up, 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 includes baseboard management controllers, network interface controllers, solid-state drive controllers, or other controllers. The lifecycle management server selects 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.

Technologies for lifecycle management include multiple computing devices in communication with a lifecycle management server. On boot-up, 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 includes baseboard management controllers, network interface controllers, solid-state drive controllers, or other controllers. The lifecycle management server selects 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.

Racks and rack pods to support a plurality of sleds are disclosed herein. Switches for use in the rack pods are also disclosed herein. A rack comprises a plurality of sleds and a plurality of electromagnetic waveguides. The plurality of sleds are vertically spaced from one another. The plurality of electromagnetic waveguides communicate data signals between the plurality of sleds.

Technologies for generating manifest data for a sled include a sled to generate manifest data indicative of one or more characteristics of the sled (e.g., hardware resources, firmware resources, a configuration of the sled, or a health of sled components). The sled is also to associate an identifier with the manifest data. The identifier uniquely identifies the sled from other sleds. Additionally, the sled is to send the manifest data and the associated identifier to a server. The sled may also detect a change in the hardware resources, firmware resources, the configuration, or component health of the sled. The sled may also generate an update of the manifest data based on the detected change, where the update specifies the detected change in the hardware resources, firmware resources, the configuration, or component health of the sled. The sled may also send the update of the manifest data to the server.

Technologies for generating manifest data for a sled include a sled to generate manifest data indicative of one or more characteristics of the sled (e.g., hardware resources, firmware resources, a configuration of the sled, or a health of sled components). The sled is also to associate an identifier with the manifest data. The identifier uniquely identifies the sled from other sleds. Additionally, the sled is to send the manifest data and the associated identifier to a server. The sled may also detect a change in the hardware resources, firmware resources, the configuration, or component health of the sled. The sled may also generate an update of the manifest data based on the detected change, where the update specifies the detected change in the hardware resources, firmware resources, the configuration, or component health of the sled. The sled may also send the update of the manifest data to the server.

A network storage device connected with a network fabric includes a network storage controller that performs interfacing with the network fabric and translates and processes a command provided through the network fabric, and a nonvolatile memory cluster that exchanges data with the network storage controller under control of the network storage controller. The nonvolatile memory cluster includes a first nonvolatile memory array connected with the network storage controller through a first channel, a nonvolatile memory switch connected with the network storage controller through a second channel, and a second nonvolatile memory array communicating with the network storage controller under control of the nonvolatile memory switch.

An endpoint group (EPG) can be stretched between the sites so that endpoints at different sites can be assigned to the same stretched EPG. Because the sites can use different bridge domains when establishing the stretched EPGs, the first time a site transmits a packet to an endpoint in a different site, the site learns or discovers a path to the destination endpoint. The site can use BGP to identify the site with the host and use a multicast tunnel to reach the site. A unicast tunnel can be used to transmit future packets to the destination endpoint. Additionally, a stretched EPG can be segmented to form a micro-stretched EPG. Filtering criteria can be used to identify a subset of the endpoints in the stretched EPG that are then assigned to the micro-stretched EPG, which can have different policies than the stretched EPG.