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.

Technologies for an accelerator interface over Ethernet are disclosed. In the illustrative embodiment, a network interface controller of a compute device may receive a data packet. If the network interface controller determines that the data packet should be pre-processed (e.g., decrypted) with a remote accelerator device, the network interface controller may encapsulate the data packet in an encapsulating network packet and send the encapsulating network packet to a remote accelerator device on a remote compute device. The remote accelerator device may pre-process the data packet (e.g., decrypt the data packet) and send it back to the network interface controller. The network interface controller may then send the pre-processed packet to a processor of the compute device.

A method for loop escape analysis includes receiving a set of executable computer instructions stored on a storage medium, and determining a number of inputs to a loop associated with a data structure, storage space that would be saved by compressing the data structure, and a size of new elements required to compress the data structure. Upon reaching an end of the loop, the method determines whether to compress the data structure based on a comparison between the size of the new elements and the saved storage space. In response to determining to compress the data structure, the method compresses the data structure.

A method, computer program, and computer system is provided for compressing a deep neural network model. Weight coefficients associated with a deep neural network are quantize and entropy-coded. The quantized and entropy-coded weight coefficients are locally smoothed. The smoothed weight coefficients are compressed based on applying a variational dropout to the weight coefficients.

A computer implemented method of data compression using a hardware accelerator includes submitting a request to compress or decompress a data segment using a compression or decompression thread. The method also includes compressing or decompressing the data segment using a hardware accelerator, and performing inline polling of the hardware accelerator to determine whether the hardware accelerator has completed compressing or decompressing the data segment. The inline polling and the compressing or decompressing are performed in a single thread. The method also includes submitting a wakeup command to a segment thread in response to determining that the hardware accelerator has completed compressing or decompressing the data segment.

A computer-implemented method for improving compression of predictive models includes generating an unlabeled simulated data set by expanding an initial data set, and generating a labeled data set by predicting the unlabeled, simulated data set using a complex model to output a plurality of labels. The method also includes training a relatively simple neural network using the labeled data set.

A first storage system compresses data relating to read and write by a primary site and stores the data in a first physical volume. A second storage system compresses data relating to read and write by a secondary site and stores the data in a second physical volume. When performing replication for transferring the data stored in the first physical volume of the first storage system to the second storage system and storing the data in the second physical volume, the first storage system and the second storage system determine, based on a compression scheme executable by the first storage system and a compression scheme executable by the second storage system, a compression scheme to be applied to transfer target data and transfer the transfer target data compressed by the determined compression scheme.

A computer-implemented method for improving compression of predictive models includes generating an unlabeled simulated data set by expanding an initial data set, and generating a labeled data set by predicting the unlabeled, simulated data set using a complex model to output a plurality of labels. The method also includes training a relatively simple neural network using the labeled data set.

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 data compression method includes obtaining N to-be-compressed data blocks and N pieces of protection information (PI), where the N to-be-compressed data blocks are in a one-to-one correspondence with the N pieces of PI, and N is a positive integer greater than or equal to 2, compressing the N to-be-compressed data blocks to obtain a compressed data block, and compressing the N pieces of PI to obtain compressed PI.