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.

A computer memory compression method involves analyzing computer memory content with respect to occurrence of duplicate memory objects as well as value redundancy of data values in unique memory objects. The computer memory content is encoded by eliminating the duplicate memory objects and compressing each remaining unique memory object by exploiting data value locality of the data values thereof. Metadata is provided to represent the memory objects of the encoded computer memory content. The metadata reflects eliminated duplicate memory objects, remaining unique memory objects as well as a type of compression used for compressing each remaining unique memory object. A memory object in the encoded computer memory content is located using the metadata.

A highly programmable device, referred to generally as a data processing unit, having multiple processing units for processing streams of information, such as network packets or storage packets, is described. The data processing unit includes one or more specialized hardware accelerators configured to perform acceleration for various data processing functions. This disclosure describes a programmable hardware-based data compression accelerator that includes a pipeline for performing static dictionary-based and dynamic history-based compression on streams of information, such as network packets. The search block may support single and multi-thread processing, and multiple levels of compression effort. To achieve high-compression, the search block may operate at a high level of effort that supports a single thread and use of both a dynamic history of the input data stream and a static dictionary of common words. The static dictionary may be useful in achieving high-compression where the input data stream is relatively small.

A decompression system (800; 1100; 1300) for decompressing a compressed data block that comprises a plurality of compressed data values is presented. The decompression system has a plurality of decompression devices (700; 1200A-B) in an array or chain layout (820a-820m−1; 120a-1120m−1; 1320a-1320m−1) for decompressing respective compressed data values of the compressed data block. A first decompression device (820a; 1120a; 1320a) is connected to a next decompression device (820b; 1120b; 1320b), and a last decompression device (820m−1; 120m−1; 1320m−1) is connected to a preceding decompression device (820m−2; 1120m−2; 320m−2). The first decompression device (820a; 1120a; 1320a) decompresses a compressed data value of the compressed data block and reduces the compressed data block by extracting a codeword of the compressed data value and removing the compressed data value from the compressed data block, retrieving a decompressed data value out of the extracted codeword, and passing the reduced compressed data block to the next decompression device (820b; 1120b; 320b). The last decompression device (820m−1; 1120m−1; 1320m−1) receives a reduced compressed data block as reduced by the preceding decompression device (820m−2; 1120m−2; 320m−2) and decompresses another compressed data value of the compressed data block by extracting a codeword of said another compressed data value, and retrieving another decompressed data value out of the extracted codeword.

An information handling system may include at least one processor and a memory coupled to the at least one processor. The information handling system may be configured to receive data comprising a plurality of data chunks; perform deduplication on the plurality of data chunks to produce a plurality of unique data chunks; determine a compression ratio for respective pairs of the unique data chunks; determine a desired compression order for the plurality of unique data chunks based on the compression ratios; combine the plurality of unique data chunks in the desired compression order; and perform data compression on the combined plurality of unique data chunks.

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.

A system comprises an encoder configured to compress attribute information for a point cloud and/or a decoder configured to decompress compressed attribute information for the point cloud. To compress the attribute information, a transform is applied to the attribute values to generate attribute coefficients/transformed attribute values. Points with attribute coefficients with a significant value are assigned a first binary flag value, while points with non-significant attribute coefficients are assigned a second binary flag value. A K.sup.th order exponential Golomb encoder or Golomb-Rice encoder is used to compress the run-length values, where separate states and associated contexts are maintained for funs of both the first and second binary values. A decoder uses a corresponding process to decode the compressed attribute information.

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.