Embodiments of the present invention provide a computer system, a computer program product, and a method that comprises ingesting tabular data from at least one modality of a plurality of modalities; simultaneously extracting data and generating a prediction model for a task of a computing device from the extracted data from at least two modalities in the plurality of modalities; generating a data signature based on the generated prediction model from the at least two modalities by leveraging the generated prediction model for ingested tabular data and extracted data; comparing the generated data signature to identified data signatures stored in at least one modality in the plurality of modalities; and performing a task based on the generated data signature and a validation of the comparison of identified data signatures.

Alert correlation helps reduce the number of alerts that IT staff have to act upon. Methods include a computer program product that applies a machine driven deep learning model to effectively correlate alerts caused by a common root cause. The methods of correlation provide the user the context of the root cause for the alerts. Therefore, it helps the user to quickly identify, understand and resolve the problem thereby reducing the mean time to identification and resolution. Alerts caused by the same root cause therefor come together.

A computing device, including a processor configured to perform data transfer scheduling for a hardware accelerator including a plurality of processing areas. Performing data transfer scheduling may include receiving a plurality of data transfer instructions that encode requests to transfer data to respective processing areas. Performing data transfer scheduling may further include identifying a plurality of transfer path conflicts between the data transfer instructions. Performing data transfer scheduling may further include sorting the data transfer instructions into a plurality of transfer instruction subsets. Within each transfer instruction subset, none of the data transfer instructions have transfer path conflicts. For each transfer instruction subset, performing data transfer scheduling may further include conveying the data transfer instructions included in that transfer instruction subset to the hardware accelerator. The data transfer instructions may be conveyed in a plurality of sequential data transfer phases that correspond to the transfer instruction subsets.

A system for predictive analysis of very large data sets using an actor-driven distributed computational graph, wherein a pipeline orchestrator creates and manages individual data pipelines while providing data caching to enable interactions between specific activity actors within pipelines. Each pipeline then comprises a pipeline manager that creates and manages individual activity actors and directs operations within the pipeline while reporting back to the pipeline orchestrator.

Embodiments of the present invention provide a method and a system for managing a storage system. In one embodiment of the present invention, there is provided a method for managing a storage system, where the storage system comprises a first controller, a second controller, a first communication area as well as a second communication area. The method comprising: with respect to a storage device in the storage system, in response to the first controller successfully accessing the storage device, writing to the first communication area a first state that indicates a state relationship between the first controller and the storage device, where the first communication area is readable and writable to the first controller and readable to the second controller; reading from the second communication area a second state that indicates a state relationship between the second controller and the storage device, where the second communication area is readable to the first controller and readable and writable to the second controller; and in response to the second state indicating that the second controller successfully accesses the storage device, initializing the storage system. In one embodiment of the present invention, there is further provided a corresponding system and apparatus.

A method of managing remote direct memory access (RDMA) to a virtual computing instance includes suspending locally initiated RDMA operations of the virtual computing instance executing on a first host prior to a migration of the virtual computing instance to a second host. The first host includes a first hypervisor and the second host includes a second hypervisor. The method further includes requesting a peer to suspend remotely initiated RDMA operations that target the virtual computing instance through a first channel, establishing after the migration, a second channel between the peer and the second hypervisor that supports execution of the virtual computing instance on the second host, configuring a virtual object of the second hypervisor on the second host to use the second channel for the locally initiated RDMA operations, and requesting the peer to resume the remotely initiated RDMA operations using the second channel.

A parallel processing architecture includes a CPU, a task pool populated by the CPU, and a plurality of autonomous co-processing cells each having an agent configured to proactively interrogate the task pool to retrieve tasks appropriate for a particular so-processor. Each co-processor communicates with the task pool through a switching fabric, which facilitates connections for data transfer and arbitration between all system resources. Each so-processor notifies the task pool when a task or task thread is completed, whereupon the task pool notifies the CPU.

A method of pipelining execution stages of a pipelined application comprises an application execution program (AEP) utilizing a Pipeline Programming Interface (PPI) of a Buffer Pipelined Application computing System (BPAS). In the method the AEP uses one interface of the PPI to determine buffers, among a set of pipeline buffers stored in physical memories of the BPAS, for the BPAS to execute operations a computing application using batches of application data. The AEP uses a second interface of the PPI to load data batches into pipeline buffers, and a third interface of the PPI to input the buffers to the BPAS for executing operations of the application. The AEP can use another interface of the PPI to allocate the buffers in particular physical memories of the BPAS. A computing system can comprise the AEP and BPAS, and can perform the method.

A client device including at least one memory configured to be used at least in part as a shared cache in a distributed cache. A network interface of the client device is configured to communicate with one or more other client devices on a network with each of the one or more other client devices configured to provide a respective shared cache for the distributed cache. At least one processor of the client device is configured to execute a kernel of an Operating System (OS) for allocating resources of the client device. The kernel is configured to access data for the distributed cache in the shared cache, which is located in a kernel space of the at least one memory.

An information processing apparatus includes a storage device and a processor that runs a virtual machine. The processor detects a waiting process that is ready for execution on the virtual machine, and writes process information about the detected waiting process in a storage area of the storage device, which is accessible area to management software managing the virtual machine.