Abstract: An information handling system includes a processor, first and second plug-in connector interfaces coupled to the processor, and first and second accelerator modules installed into respective first and second plug-in connector interfaces. The processor instantiates machine learning code. The information handling system instantiates a workload on the processor. The machine learning code determines a processing need of the workload, determines a first processing capability of the first accelerator module and a second processing capability of the second accelerator module, and allocates a processing resource of the first accelerator module to the workload based upon an evaluation of the processing need, the first processing capability, and the second processing capability.

Abstract: One example method includes analyzing a load factor regarding a workload for one or more actors in a data storage platform, wherein the workload includes a group of two or more connected jobs, applying one or more criteria to an output of the load factor analyzing, based on the applying a criterion from the one or more criteria, determining how many actors should be included in a swarm of actors necessary to perform all of the connected jobs, spawning the swarm of actors, and load balancing the connected jobs across the swarm of actors.

Abstract: A plurality of virtual processing units associated with a physical processing unit is identified. Each of the plurality of virtual processing units is associated with a virtual machine of a plurality of virtual machines that run on respective virtual processing units in round-robin order using respective assigned execution time periods. A first overhead time value associated with running of the first virtual machine on a first virtual processing unit of the plurality of virtual processing units is obtained for a first virtual machine of the plurality of virtual machines. A second overhead time value associated with running of the second virtual machine on a second virtual processing unit of the plurality of virtual processing units is obtained for a second virtual machine of the plurality of virtual machines. The first overhead time value associated with the running of the first virtual machine and the second overhead time value associated with running of the second virtual machine are compared.

Abstract: Apparatuses, systems, and techniques to allocate memory based on a part of a sequence of items. In at least one embodiment, memory is allocated based on the size of a sliding window used to analyze images with neural networks.

Abstract: Distributing quantum jobs are disclosed. When a quantum processing unit is underutilized or when wait times are long, quantum jobs may be distributed from the job queue of one vendor to another vendor. This improves utilization and reduces wait times.

Abstract: Apparatuses, systems, and techniques to allocate memory based on a part of a sequence of items. In at least one embodiment, memory is allocated based on the size of a sliding window used to analyze images with neural networks.

Abstract: A system for accelerator functions as a service is disclosed. The system may receive a job that includes a computer (CPU) portion and an accelerator portion. When the accelerator portion is performed, an execution time associated with a time for the accelerator to return results is determined. Resources or a portion thereof allocated to the job, or the CPU portion are freed or reallocated to another job at least during the execution time. The job is queued to receive resources when the results are received from the accelerator.

Abstract: Systems and methods for generating runtime predictions in distributed computer architectures are disclosed. According to one embodiment, a method for generating runtime predictions in a distributed computer architectures may include: (1) receiving, by a runtime prediction computer program executed by an electronic device, training data regarding completion of a plurality of risk compute jobs; (2) extracting, by the runtime prediction computer program, bucketing statistics and instrument level features from the data; (3) training, by the runtime prediction computer program, a machine learning model with the training data, the extracted bucketing statistics, and the instrument level features; (4) receiving, by the runtime prediction computer program, a plurality of jobs for a period of time; and (5) calculating, by the runtime prediction computer program, an instrument cost for each of the jobs using the machine learning model.

Abstract: A virtual machine memory management method and device are provided. The method includes: identifying memory of a non-operating system of a virtual machine from total memory allocated to the virtual machine, where the total memory includes memory of the virtual machine and management memory of a hypervisor, and the memory of the virtual machine includes memory of an operating system of the virtual machine and the memory of the non-operating system of the virtual machine; using the memory of the non-operating system of the virtual machine as swappable memory; and storing data in the swappable memory into a nonvolatile memory. This method helps avoid a problem that computer performance significantly deteriorates in a virtual machine over-submission scenario, and improve stability in running of a virtual machine and users' use experience.

Abstract: Techniques are provided for managing load distribution in a data storage system which comprises storage server nodes and storage nodes, wherein each storage server node comprises one or more distribution targets, wherein each distribution target comprises a logical processing unit that is configured to manage input/output operations for accessing data in the storage nodes. A process for managing the load distribution comprises: partitioning a logical address space into a plurality of distribution items; mapping the logical address space to a sequence of one or more distribution templates, wherein each distribution template comprises an ordered list of the distribution targets of the storage server nodes, which is uniquely different for each of the one or more distribution templates; and utilizing the one or more distribution templates to map the distribution items of the logical address space to the distribution targets of the storage server nodes.

Abstract: A computer implemented method controls in a core dimension packing the plurality of processing unit cores to reduce partial use of the plurality of processing unit cores by concurrently loading into the plurality of processing unit cores: all computational nodes of at least one member selected from the group consisting of the first number of whole groups and the first number of partial groups; and all computation nodes of at least one member selected from the group consisting of the second number of whole groups and the second number of partial groups. According to other illustrative embodiments, a computer system and a computer program product for packing the plurality of processing cores are provided. As a result, the illustrative embodiments can provide a technical effect of increasing the performance in running jobs in a computer system in which partial use of processing unit cores can be reduced.

Abstract: Automation methods and systems include identifying common parts of consecutive pairs of consecutive screen outputs. The consecutive screen outputs are clustered responsive to a determination that a distance between the common parts of the consecutive pairs is lower than a threshold distance value. Interactions with a remote computer system are automated based on the clustered consecutive screen outputs.

Abstract: Embodiments of the present disclosure a method for dynamically generating a generative operating system based on a hardware and software environment feature, the method is configured to generate the generative operating system, and an architecture of the generative operating system includes: a hardware architecture adaptation layer and a meta kernel layer and an external library of the operating system.

Abstract: A method for resource allocation in a disaggregated data center (DDC), comprising: a reliability model to determine an achievable reliability for a service request to the DDC; a integer linear programming (ILP) model to perform a resource allocation for the service request to the DDC such that maximizing total number of service requests received by the DDC accepted for execution is maximized, while the number of the accepted service requests allocated with backup computing resources is minimized; and a heuristic process to perform a resource allocation for the service request to the DDC such that the least reliable node of each needed computing resource type is allocated but still meeting the reliability requirement of the service request.