Hewlett-packard Development Company, L.p.

Hewlett Packard Enterprise Development LP

Micro Focus Lcc

Entit Software LLC

Hewlett-packard Company

Agilent Technologies, Inc.

Hewlett-packard Devolpment Company, L.p.

Sharad Singhal

Xiaoyun Zhu

Mashood Abdulla Kodavanji

Ram Swaminathan

Jun Li

Sven Graupner

Zhikui Wang

Chinmay Ghosh

Preeti N Bhoj

Clarete Riana Crasta

Syed Ismail Faizan Barmawer

Gautham Bhat Kumbla

Dirk Manfred Beyer

Xue Liu

Sujoy Basu

Srinivas Ramanathan

Anna Maria Zara

Ramya Ahobala Rao

Siani Lynne Pearson

Mehmet Kivanc Ozonat

Cipriano A Santos

Akhil Sahai

Soumitra Chatterjee

Scott Alan Rhine

James Christopher Pruyne

Marc Brandt

Bu Sung Lee

Donald E Young

Shi Xing Yan

Guopeng Zhao

Ding Ma

Rishi Kesh K Rajak

Meichun Hsu

Ludmila Cherkasova

Dejan S Milojicic

Alan Hersh Karp

Arif Abdulhusein Merchant

Qiming Chen

Martin Arlitt

Ira Cohen

Wei Xu

Mustafa Uysal

Maria G Castellanos

Jerome Alexander Rolia

Kimberly Keeton

Pradeep Padala

Christopher H Stewart

Alex Xin Zhang

Andrew Ray Wheeler

Wenting Tang

Terence Patrick Kelly

Yin Wang

Michael S Woodacre

William H Blanding

Fred A Cummins

Julie Ward Drew

Bryan Stephenson

Thomas Guenter Bartz

Hamid Reza Motahari Nezhad

Haris Volos

Julie Anna Symons

James Benjamin Saxe

Alexandre Bronstein

Yupu Zhang

Stephen D Janes

Lyle H Ramshaw

Ramesh Chandra Chaurasiya

Kenneth Salem

Sailesh Chutani

Sanish N Suresh

Porno Shome

Soumya Palakkattiri Narayanan

Cynara Justine

Hamid Reza Molahari Nezhad

Ciprianos Santos

Joydip Das

Preeti N Bhoi

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.

Incremental data backup using a combined tracking data structure

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.

Fabric-attached memory for applications using message passing procedure

A first process executing on a first HPC compute node of an HPC cluster sends a write command, including a global identifier for a memory window that is globally accessible to processes executing in the HPC cluster, to an HPC memory node of the HPC cluster instructing the HPC memory node to write first data to the memory window allocated at the HPC memory node. The first data is sent by the first process to a memory window allocated on the HPC memory node. The first data is written by the HPC memory node to the memory window that includes randomly accessed, addressable memory locations.

Shared memory in high performance computing (HPC) messaging systems

Unused memory registrations in a remote memory system are proactively identified and freed. A memory server maintains a list of resource tracking entries for its outstanding memory registrations. The memory server continually monitors its list of resource tracking entities. When the memory server finds an unused memory registration in its list, it unregisters the corresponding shared memory region. The monitoring may be performed in the background of the memory server by, e.g., a monitor thread. As a result, unused memory registrations may be quickly released.

Memory registration for remote direct memory access

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.

Operation execution on memory servers

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.

Serving application programming interface calls directed to hierarchical data format files stored in fabric-attached memories

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.

Client allocation of memory across memory servers

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.

Client update of data modification tracking structure

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.

System and method for facilitating efficient management of data structures stored in remote memory

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.

Metadata tree with key rotation information

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.

Systems and methods for aggregate bandwidth and latency optimization

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.

Programming model and framework for providing resilient parallel tasks

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.

Parallelizing SQL user defined transformation functions

Service models may be generated in response to services exposed by two or more IaaS service providers, and an interface may be provided for selection and specification of the service models.

Provisioning IaaS services

A method performed by a processing system includes reconstructing a metadata tree of a patient from a metadata tree journal, the metadata tree including a plurality of references to a corresponding plurality of encrypted electronic health records of the patient in an encrypted data store, and validating the metadata tree by comparing first integrity information of the metadata tree to second integrity information corresponding to the metadata tree journal provided by a metadata integrity validator.

Validating a metadata tree using a metadata integrity validator

Growing community of inventors

Belmont, CA, United States of America

Sharad Singhal