Abstract: In some examples, a computer identifies a plurality of memory servers accessible by the computer to perform remote access over a network of data stored by the plurality of memory servers, sends allocation requests to allocate memory segments to place interleaved data of the computer across the plurality of memory servers, and receives, at the computer in response to the allocation requests, metadata relating to the memory segments at the plurality of memory servers, the metadata comprising addresses of the memory segments at the plurality of memory servers. The computer uses the metadata to access, by the computer, the interleaved data at the plurality of memory servers, the interleaved data comprising blocks of data distributed across the memory segments.

Abstract: In some examples, a system combines modified data tracking structures in a plurality of computer nodes into a combined tracking data structure, where a modified data tracking structure includes indicators of modified data portions in a network-attached memory. The system stores the combined tracking data structure at the network-attached memory. As part of an incremental data backup operation, the system uses the combined tracking data structure to provide the modified data portions from the network-attached memory to a backup storage system.

Abstract: In some examples, a processor receives a first request to allocate a memory region for a collective operation by process entities in a plurality of computer nodes. In response to the first request, the processor creates a virtual address for the memory region and allocates the memory region in a network-attached memory coupled to the plurality of computer nodes over a network. The processor correlates the virtual address to an address of the memory region in mapping information. The processor identifies the memory region in the network-attached memory by obtaining the address of the memory region from the mapping information using the virtual address in a second request. In response to the second request, the processor performs the collective operation.

Abstract: In some examples, a system includes a plurality of memory servers managing access of data in a memory. A computer node includes a plurality of buffers associated with the memory servers. A processor executes a plurality of functions accessible by the computer node to access the data of the memory servers, the plurality of functions including associating, with the plurality of buffers, information specifying a type of an operation to be performed on the data using the plurality of buffers, queueing the operation in the plurality of buffers, initiating an execution of the operation, based on the type specified by the information, at the memory servers associated with the plurality of buffers, and providing results of the operation from the memory servers to the computer node.

Abstract: In some examples, a system having a plurality of computer nodes receives a command based on program code of a program being developed in an interactive programming session. The system distributes data items from a network-attached memory to a distributed data object having data in node memories of the plurality of computer nodes. A dataset manager performs an operation specified by the command on the distributed data object, the operation executed in parallel on the plurality of computer nodes. The dataset manager produces derived data generated by the operation on the distributed data object, the derived data accessible by a programmer in the interactive programming session.

Abstract: In accordance with example implementations, a process includes receiving, by a connector that is associated with a compute node and is associated with a fabric-attached memory (FAM), an application programming interface (API) called to perform an operation that is associated with a hierarchical data format (HDF) object of an HDF file. The API call includes a HDF object identifier, which corresponds to the HDF object. The process includes, responsive to the request, based on the HDF object identifier, accessing, by the connector, mapping information that is stored in the FAM; and using, by the connector, the mapping information to identify a FAM descriptor corresponding to a first data item that is stored in the FAM and corresponds to the HDF object. The process includes, responsive to the request, serving, by the connector, the API call responsive to the identification of the FAM descriptor.

Abstract: Some examples relate to providing a fabric-attached memory (FAM) for applications using message passing procedure. In an example, a remotely accessible memory creation function of a message passing procedure is modified to include a reference to a region of memory in a FAM. A remotely accessible memory data structure representing a remotely accessible memory is created through the remotely accessible memory creation function. When an application calls a message passing function of the message passing procedure, a determination is made whether the remotely accessible memory data structure in the message passing function includes a reference to the region of memory in the FAM. In response to a determination that the remotely accessible memory data structure includes a reference to the region of memory in the FAM, the message passing function call is routed to a FAM message passing function corresponding to the message passing function.

Abstract: In some examples, a client system, in response to a request to modify a first data page at a memory server in a remote access by a client over a network, sends, to the memory server, a request to update a data modification tracking structure stored by the memory server to indicate that the first data page is modified. The client system initiates an incremental data backup from the memory server to a backup storage system of data pages indicated as modified by the data modification tracking structure stored at the memory server.

Abstract: In some examples, a computer identifies a plurality of memory servers accessible by the computer to perform remote access over a network of data stored by the plurality of memory servers, sends allocation requests to allocate memory segments to place interleaved data of the computer across the plurality of memory servers, and receives, at the computer in response to the allocation requests, metadata relating to the memory segments at the plurality of memory servers, the metadata comprising addresses of the memory segments at the plurality of memory servers. The computer uses the metadata to access, by the computer, the interleaved data at the plurality of memory servers, the interleaved data comprising blocks of data distributed across the memory segments.

Abstract: In some examples, a client system, in response to a request to modify a first data page at a memory server in a remote access by a client over a network, sends, to the memory server, a request to update a data modification tracking structure stored by the memory server to indicate that the first data page is modified. The client system initiates an incremental data backup from the memory server to a backup storage system of data pages indicated as modified by the data modification tracking structure stored at the memory server.

Abstract: A system and method are provided for facilitating efficient management of data structures stored in remote memory. During operation, the system receives a request to allocate memory for a first part in a data structure stored in a remote memory associated with a compute node in a network. The system pre-allocates a buffer in the remote memory for a plurality of parts in the data structure and stores a first local descriptor associated with the buffer in a local worker table stored in a volatile memory of the compute node. The first local descriptor facilitates servicing future access requests to the first and other parts in the data structure. The system stores a first global descriptor for the buffer in a shared global table stored in the remote memory and generates a first reference corresponding to the first part, thereby facilitating faster traversals of the data structure.

Abstract: One aspect of the disclosure relates to the use of derivatives of dichlorophenyl urea for treating cancers.

Abstract: A system and method are provided for facilitating efficient management of data structures stored in remote memory. During operation, the system receives a request to allocate memory for a first part in a data structure stored in a remote memory associated with a compute node in a network. The system pre-allocates a buffer in the remote memory for a plurality of parts in the data structure and stores a first local descriptor associated with the buffer in a local worker table stored in a volatile memory of the compute node. The first local descriptor facilitates servicing future access requests to the first and other parts in the data structure. The system stores a first global descriptor for the buffer in a shared global table stored in the remote memory and generates a first reference corresponding to the first part, thereby facilitating faster traversals of the data structure.

Abstract: A method performed by a processing system includes identifying a first node in a metadata tree of a patient that corresponds to an encrypted electronic health record in an encrypted data store and preventing a portion of the first node from being decrypted with a node key of a first healthcare participant in response to a second node of the metadata tree including key rotation information that indicates that the node key has been revoked by a second healthcare participant.

Abstract: One aspect of the disclosure relates to the use of derivatives of dichlorophenyl urea for treating cancers.

Abstract: Systems and methods for dynamically and programmatically controlling hardware and software to optimize bandwidth and latency across partitions in a computing system are discussed herein. In various embodiments, performance within a partitioned computing system may be monitored and used to automatically reconfigure the computing system to optimize aggregate bandwidth and latency. Reconfiguring the computing system may comprise reallocating hardware resources among partitions, programming firewalls to enable higher bandwidth for specific inter-partition traffic, switching programming models associated with individual partitions, starting additional instances of one or more applications running on the partitions, and/or one or more other operations to optimize the overall aggregate bandwidth and latency of the system.

Abstract: Exemplary embodiments herein describe programming models and frameworks for providing parallel and resilient tasks. Tasks are created in accordance with predetermined structures. Defined tasks are stored as data objects in a shared pool of memory that is made up of disaggregated memory communicatively coupled via a high performance interconnect that supports atomic operations as descried herein. Heterogeneous compute nodes are configured to execute tasks stored in the shared memory. When compute nodes fail, they do not impact the shared memory, the tasks or other data stored in the shared memory, or the other non-failing compute nodes. The non-failing compute nodes can take on the responsibility of executing tasks owned by other compute nodes, including tasks of a compute node that fails, without needing a centralized manager or schedule to re-assign those tasks. Task processing can therefore be performed in parallel and without impact from node failures.

Abstract: Example embodiments relate to parallelizing structured query language (SQL) user defined transformation functions. In example embodiments, a subquery of a query is received from a query engine, where each of the subqueries is associated with a distinct magic number in a magic table. A user defined transformation function that includes local, role-based functionality may then be executed, where the magic number triggers parallel execution of the user defined transformation function. At this stage, the results of the user defined transformation function are sent to the query engine, where the query engine unions the results with other results that are obtained from the other database nodes.

Abstract: Exemplary embodiments herein describe programming models and frameworks for providing parallel and resilient tasks. Tasks are created in accordance with predetermined structures. Defined tasks are stored as data objects in a shared pool of memory that is made up of disaggregated memory communicatively coupled via a high performance interconnect that supports atomic operations as descried herein. Heterogeneous compute nodes are configured to execute tasks stored in the shared memory. When compute nodes fail, they do not impact the shared memory, the tasks or other data stored in the shared memory, or the other non-failing compute nodes. The non-failing compute nodes can take on the responsibility of executing tasks owned by other compute nodes, including tasks of a compute node that fails, without needing a centralized manager or schedule to re-assign those tasks. Task processing can therefore be performed in parallel and without impact from node failures.

Abstract: Systems and methods for dynamically and programmatically controlling hardware and software to optimize bandwidth and latency across partitions in a computing system are discussed herein. In various embodiments, performance within a partitioned computing system may be monitored and used to automatically reconfigure the computing system to optimize aggregate bandwidth and latency. Reconfiguring the computing system may comprise reallocating hardware resources among partitions, programming firewalls to enable higher bandwidth for specific inter-partition traffic, switching programming models associated with individual partitions, starting additional instances of one or more applications running on the partitions, and/or one or more other operations to optimize the overall aggregate bandwidth and latency of the system.