International Business Machines Corporation

Burkhard Ringlein

Christoph Hermann Hagleitner

Francois Abel

Dionysios Diamantopoulos

Srikumar Venugopal

Mitra Purandare

Beat Eugen Weiss

Michael Johnston

Moazzam Khan

Bradley E Harris

James Brent Peterson

Preeti Ravindra

An embodiment includes detecting a search request of a multi-dimensional search space by a system; responsive to detecting the search request, sampling representative parameters in the multi-dimensional search space based on a sampling threshold. The embodiment includes determining a parameter range of the representative parameters in the multi-dimensional search space. The embodiment also includes transforming a part of the multi-dimensional search space based on a statistical guarantee and the parameter range, where the part of the multi-dimensional search space is a refined space for a solution of the search request.

Refinement of large multi-dimensional search spaces

The invention is notably directed to a method, computer program product, and computer system for running software inside containers. The method relies on a computerized system that includes a composable disaggregated infrastructure, in addition to general-purpose hardware. The computerized system is configured to dynamically allocate computerized resources, which include both general resources and specialized resources. The former are enabled by the general-purpose hardware, while the latter are enabled by specialized network-attached hardware components of the composable disaggregated infrastructure. The method maintains a table capturing specializations of the specialized network-attached hardware components. At runtime, software is run inside each container by executing corresponding functions. A first subset of the functions are executed using the general resources, whereas a second subset of the functions are executed using the specialized resources, by offloading the second subset of functions to respective components of the specialized network-attached hardware components, in accordance with the specializations.

Optimizing container executions with network-attached hardware components of a composable disaggregated infrastructure

The present disclosure relates to a messaging method for a hardware acceleration system. The method includes determining exchange message types to be exchanged with a hardware accelerator in accordance with an application performed by the hardware acceleration system. The exchange message types indicate a number of variables, and a type of the variables, of the messages. The method also includes selecting schemas from a schema database. The message type schemas indicates a precision representation of variables of messages associated with the schema. The selected schemas correspond to the determined exchange message types. Further, the method includes configuring a serial interface of the hardware accelerator in accordance with the selected schemas, to enable a message exchange including the messages.

Messaging for a hardware acceleration system

Methods are provided for configuring a reconfigurable hardware device to execute a user application. Such a method includes providing static shell logic on the device. The static shell logic is controlled by a primary management core for managing operation of the device, and has a predetermined hardware interface. The method includes configuring on the device, via the primary management core, dynamic shell logic for implementing dynamically-selected shell functionality. The dynamic shell logic includes a secondary management core, adapted to communicate with the primary management core via the hardware interface, for managing operation of the dynamic shell logic. The method further comprises configuring on the device, via the primary management core, application logic, having an interface with the dynamic shell logic, for executing the user application. The secondary management core uploads to the primary management core dynamic code to adapt the primary management core for use with the dynamic shell logic.

Configuration of hardware devices

Mechanisms are provided to perform security incident disposition operations. A security incident is received that includes a security incident data structure comprising metadata describing properties of the security incident, and a corresponding security knowledge graph which includes nodes representing elements associated with the security incident and edges representing relationships between the nodes. The security incident data structure and security knowledge graph are processed to extract a set of security incident features corresponding to the security incident and input the extracted set of security incident features into a trained security incident machine learning model. The model generates a disposition classification output based on results of processing the extracted set of security incident features. The disposition classification output is output to the source of the security incident data structure.

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Bülach, Switzerland

Burkhard Ringlein