Abstract: An autonomous compute storage device system includes a computing device and a storage device that is coupled to the computing device. The storage device receives a read instruction from a host processing system in the computing device that identifies data stored in a storage subsystem included in the storage device and, in response, performs a read operation to copy the data from the storage subsystem to a memory subsystem accessible to the storage device and provide the data to the host processing system. If the storage device determines that an autonomous compute signature matches the data that was copied to the memory subsystem during the performance of the read operation, it executes an autonomous compute application to perform compute operations that are associated with the data that was copied to the memory subsystem during the performance of the read operation and generate compute operation result(s).

Abstract: A resource-capability-and-connectivity-based workload performance improvement system includes a resource management device coupled to processing systems and memory systems. The resource management device receives a request to perform a first workload, identifies a DAG that includes functions for performing the first workload, and uses first parameters in the DAG to configure the processing systems and the memory subsystems to perform the functions. Based on performance of the functions, the resource management device determines function modification(s) for at least one of the functions and, based on the function modification(s), modifies the first parameters included in the DAG to provide modified parameters. When the resource management device receives a request to perform a second workload, it identifies the DAG that includes the functions for performing the second workload, and uses the modified parameters in the DAG to configure the processing systems and the memory subsystems to perform the functions.

Abstract: A data transformation/processing system includes a data transformation/processing management device coupled to a client device, processing systems, memory systems, and storage systems. The data transformation/processing management device receives a request from the client device to process first data to generate second data, determines a first subset of the processing systems for performing processing operations to process the first data, and identifies a data path for the processing operations that includes storage locations provided by subsets of the memory systems and the storage systems.

Abstract: A reconfigurable microservice storage device system includes a computing device coupled to a plurality of storage devices that each include storage device compute hardware coupled to a storage subsystem that is configured to store data. The computing device configures the storage device compute hardware in each of a first subset of the plurality of storage devices to provide a respective first storage device operating system that includes a respective first storage device management microservice that manages the storage subsystem in that storage device. The computing device also configures the storage device compute hardware in each of a second subset of the plurality of storage devices to provide a respective second storage device operating system that includes a respective second storage device management microservice that manages the storage subsystem in that storage device, and at least one respective second compute microservice that performs at least one second compute operation.

Abstract: An autonomous compute storage device system includes an autonomous compute storage device signature/application provisioning system coupled to a storage device. The storage device retrieves an autonomous compute signature from the autonomous compute storage device signature/application provisioning system and, as part of a storage operation being performed in a storage subsystem in the storage device, stores data in a memory subsystem that is accessible to the storage device. If the storage device determines that the autonomous compute signature matches the data that was stored in the memory subsystem, it retrieves an autonomous compute application from the autonomous compute storage device signature/application provisioning system, and executes the autonomous compute application to perform compute operations that are associated with the data that was stored in the memory subsystem and generate at least one compute operation result.

Abstract: A resource-capability-and-connectivity-based workload performance system includes a resource management system that is coupled to a plurality of processing systems and a plurality of memory systems. The resource management system determines resource capabilities provided by each of the plurality of processing systems, each of the plurality of memory systems, and connectivity between the plurality of processing systems and the plurality of memory systems.

Abstract: A microservice storage device system includes a computing device. A storage device is coupled to the computing device and includes storage device compute hardware coupled to a storage subsystem that is configured to store data. The storage device uses the storage device compute hardware to provide a storage device operating system. The storage device then uses the storage device operating system to provide a storage device management microservice that it uses to manage the storage subsystem. The storage device also uses the storage device operating system to provide at least one compute microservice that it uses to perform at least one compute operation. The storage device management microservice and the at least one compute microservice may each be provided in a respective container generated by the storage device operating system for each of the storage device management microservice and the at least one compute microservice.

Abstract: An autonomous compute storage device system includes a computing device and a storage device that is coupled to the computing device. The storage device receives a write instruction from a host processing system in the computing device that includes data for storage in a storage subsystem that is included in the storage device and, in response, performs a write operation to provide the data in a memory subsystem that is accessible to the storage device and store the data in the storage subsystem. If the storage device determines that an autonomous compute signature matches the data that was provided in the memory subsystem during the performance of the write operation, it executes an autonomous compute application to perform compute operations that are associated with the data that was provided in the memory subsystem during the performance of the write operation and generate at least one compute operation result.

Abstract: A zoned namespace storage device system includes a zoned namespace storage device coupled to a computing device. The zoned namespace storage device includes a zoned namespace storage subsystem that is configured to store data, and storage device compute hardware that is coupled to the zoned namespace storage subsystem and that is configured to provide a storage device operating system that includes a storage device management microservice. The storage device management microservice presents a non-zone storage service to a host subsystem in the computing device and receives, via the non-zone storage service presented to the host subsystem, a storage command from the host subsystem that is associated with a storage operation. The storage device management microservice then utilizes a zone storage service presented to the storage device management microservice by the zoned namespace storage subsystem to perform the storage operation on the zoned namespace storage subsystem.

Abstract: A method is described which included receiving (S1) a number of object images (IMn) of an object (1). Each object image (IMn) corresponds to a different view direction (n). The object images include first (IM1) and second (IM2) object images corresponding to first (n1) and second (n2) directions. The method also includes determining (S2) a mesh (20) corresponding to the target region (5) of the object (1) surface (2) based on a first subset (MESH) of the number of object images (IMn) which includes two or more object images (IMn) of the number of object images (IMn). The method also includes determining (S3) diffuse (DFUV) and specular (SPUV) maps corresponding to the target region (5) of the object (1) surface (2) based on processing a second subset (REFLECT) of the object images (IMn) using a deep learning neural network model trained to estimate diffuse (DFn) and specular (SPn) albedo components based on an input image (IMn).

Abstract: Methods and systems for managing data access based threats are disclosed. To manage the data access based threats, a data processing system may include a network interface controller (NIC). The network interface controller may present emulated storages that may be used for data storage. The emulated storage devices may utilize storage resources of storage devices. The NIC may actively screen for access patterns in use of the emulated storage devices that indicate compute complexes may be compromised. When doing so, the processing may be done locally on the NIC.

Abstract: An autonomous compute storage device system includes a computing device and a storage device that is coupled to the computing device. The storage device identifies a storage operation for a storage subsystem that is included in the storage device and, in response, performs the storage operation and stores data in a memory subsystem that is accessible to the storage device as part of the performance of the storage operation. If the storage device determines that an autonomous compute signature matches the data that was stored in the memory subsystem, it executes an autonomous compute application to perform compute operations that are associated with the data that was stored in the memory subsystem and generate at least one compute operation result.

Abstract: A shared memory fabric workload performance system includes a resource orchestrator device coupled to processing systems and memory systems that are configured to provide a shared memory fabric to each of the processing systems. The resource orchestrator device receives a request to perform a workload, identifies functions for performing the workload, and generates a DAG that identifies a respective processing resource type and a respective memory requirement for performing each of the functions. For each of the functions, the resource orchestrator device determines a respective processing resource provided by the processing systems that includes the processing resource type identified in the DAG for performing that function, and a respective memory resource provided by the memory systems that is accessible to that respective processing resource, and maps that respective memory resource to that respective processing resource based on the memory requirement identified in the DAG for performing that function.

Abstract: A tiered memory fabric workload performance optimization system includes a workload management device coupled to a processing fabric and a memory fabric. The workload management system receives a workload request to perform a workload including sub-workloads, and identifies a respective processing system in the processing fabric for performing each of the sub-workloads. The workload management device then determines, for use by each respective processing system identified for performing the sub-workloads, a respective memory system in the memory fabric to provide memory systems in different memory tiers in the memory fabric that optimize characteristic(s) of a workload performance pipeline provided by the respective processing systems identified for performing the sub-workloads.

Abstract: A process for facilitating autoscaling in a stateful system is described herein. In embodiments, a set of metrics associated with a set of components of a stateful service is obtained. The set of metrics may generally indicate a utilization or a load of each of the components of the set of components (e.g., message managers and/or data stores). Thereafter, it is determined to initiate a scaling event at the stateful service in association with the set of components of the stateful service based on at least a portion of the set of metrics attaining a metric threshold indicating a threshold value for determining whether to scale stateful service components. A scaling request can then be provided to the stateful service to initiate the scaling event at the stateful service in association with the set of components of the stateful service.

Abstract: A distributed function data transformation system includes a distributed function packet provisioning device coupled to compute systems. The distributed function packet provisioning device receives a request to perform a data transformation including data transformation operations, and determines a respective function for performing each data transformation operation. The distributed function packet provisioning device generates a distributed function packet including a function list identifying the respective functions, a data identifier identifying data upon which the respective functions should be performed, and a function performance identifier configured to identify one of the respective functions to perform on the data.

Abstract: A Directed Acyclic Graph (DAG) performance system includes a DAG management system that is coupled to each of a plurality of compute systems. The DAG management system stores a dynamic DAG, receives a request to perform the dynamic DAG, and identifies a plurality of DAG operations included in the dynamic DAG. The DAG management system then determines a respective compute system type that is configured to perform each of the plurality of DAG operations, and identifies a subset of the plurality of compute systems that each include one of the respective compute system types. The DAG management system then selects a respective compute system from the subset of the plurality of compute systems to perform each of the plurality of DAG operations, and transmits a respective instruction to perform one of the plurality of DAG operations to each respective compute system that was selected to perform that DAG operation.

Abstract: A data storage transformation system includes a data storage management device that is coupled to a data provisioning device and a storage system. The data storage management device receives first format data that includes a first data format from the data provisioning device, and predicts at least one processing operation that will be performed on the first format data. The data storage management device then determines a second data format for performing the at least one processing operation, and transforms the first format data to second format data that includes the second data format. The data storage management device then transmits the second format data for storage in the storage system.

Abstract: A data storage placement system includes a data storage management device that is coupled to a data provisioning device, a storage system, and a plurality of compute systems. The data storage management device receives data from the data provisioning device, predicts at least one processing operation that will be performed on the data, determines a first storage subsystem type based on the at least one processing operation, and determines a first compute system type based on the at least one processing operation. The data storage management device then identifies a first storage subsystem that is included in the storage system, that includes the first storage subsystem type, and that is proximate a first compute system in the plurality of compute systems that includes the first compute system type. The data storage management device then transmits the data for storage in the first storage subsystem.

Abstract: A computing system includes a DPU subsystem coupled to a host subsystem. The DPU subsystem receives an instruction to provide a service, and determines a host operating load of the host subsystem and a DPU operating load of the DPU subsystem during subsequent time periods. Based on the host operating load and/or the DPU operating load during a first time period, the DPU subsystem configures the DPU subsystem to provide the service, and performs service requests for the service from client system(s). Based on the host operating load and/or the DPU operating load during a second time period that is subsequent to the first time period, the DPU subsystem configures the host subsystem to provide the service in order to migrate the service from the DPU subsystem to the host subsystem, and proxies service requests for the service between the client system(s) and the host subsystem providing the service.