Abstract: A method and system for thread aware, class aware, and topology aware memory allocations. Embodiments include a compiler configured to generate compiled code (e.g., for a runtime) that when executed allocates memory on a per class per thread basis that is system topology (e.g., for non-uniform memory architecture (NUMA)) aware. Embodiments can further include an executable configured to allocate a respective memory pool during runtime for each instance of a class for each thread. The memory pools are local to a respective processor, core, etc., where each thread executes.

Abstract: A method and system for thread aware, class aware, and topology aware memory allocations. Embodiments include a compiler configured to generate compiled code (e.g., for a runtime) that when executed allocates memory on a per class per thread basis that is system topology (e.g., for non-uniform memory architecture (NUMA)) aware. Embodiments can further include an executable configured to allocate a respective memory pool during runtime for each instance of a class for each thread. The memory pools are local to a respective processor, core, etc., where each thread executes.

Abstract: System and method for improved transferring of data involving memory device systems. A memory appliance (MA) comprising a plurality of memory modules is configured to store data within the plurality of memory modules and further configured to receive data commands from the first server and a second server coupled to the MA. The data commands may include direction memory access commands such that the MA can service the data commands while bypassing a host controller of the MA.

Abstract: Methods and systems for managing data storage and compute resources. The data can be stored a multiple locations allowing compute operations to be performed in a distributed manner in one or more locations. The cloud storage and cloud compute resources can be dynamically scaled based on the locations of the data and based on the cloud storage and/or cloud computing budgets. Dynamic reconfiguration of reconfigurable processors (e.g., FPGA) can further be used to accelerate compute operations.

Abstract: System and method for improved transferring of data involving memory device systems. A memory appliance (MA) comprising a plurality of memory modules is configured to store data within the plurality of memory modules and further configured to receive data commands from first server and a second server coupled to the MA. The data commands may include direction memory access commands such that the MA can service the data commands while bypassing a host controller of the MA.

Abstract: Embodiments described herein include a system, a computer-readable medium and a computer-implemented method for processing a system call (SYSCALL) request. The SYSCALL request from an invisible processing device is stored in a queueing mechanism that is accessible to a visible processing device, where the visible processing device is visible to an operating system and the invisible processing device is invisible to the operating system. The SYSCALL request is processed using the visible processing device, and the invisible processing device is notified using a notification mechanism that the SYSCALL request was processed.

Abstract: When requested content is available at a data center, the data center returns the requested content to the data center. When the requested content is locally unavailable at the data center, the requested content is retrieved from an origin server. The retrieval of the content from the origin server may be delayed based on the processing load at the origin server. When retrieval of the content is delayed, the request is prioritized and placed in a queue for handling by the origin server based on the priority of the request. Also, when retrieval of the content is delayed, a status page may be communicated to the browser to inform a user of the delay and provide alternate content and status information related to the request determined as a function of the request or the current state of the origin server.

Abstract: Methods, systems and computer-readable mediums for task scheduling on an accelerated processing device (APD) are provided. In an embodiment, a method comprises: enqueuing one or more tasks in a memory storage module based on the APD; using a software-based enqueuing module; and dequeuing the one or more tasks from the memory storage module using a hardware-based command processor, wherein the command processor forwards the one or more tasks to the shader cote.

Abstract: When requested content is available at a data center, the data center returns the requested content to the data center. When the requested content is locally unavailable at the data center, the requested content is retrieved from an origin server. The retrieval of the content from the origin server may be delayed based on the processing load at the origin server. When retrieval of the content is delayed, the request is prioritized and placed in a queue for handling by the origin server based on the priority of the request. Also, when retrieval of the content is delayed, a status page may be communicated to the browser to inform a user of the delay and provide alternate content and status information related to the request determined as a function of the request or the current state of the origin server.

Abstract: A method and system for thread aware, class aware, and topology aware memory allocations. Embodiments include a compiler configured to generate compiled code (e.g., for a runtime) that when executed allocates memory on a per class per thread basis that is system topology (e.g., for non-uniform memory architecture (NUMA)) aware. Embodiments can further include an executable configured to allocate a respective memory pool during runtime for each instance of a class for each thread. The memory pools are local to a respective processor, core, etc., where each thread executes.

Abstract: A data center determines whether requested content is available at the data center. The content is available when the content is both present at the data center and current. When the requested content is available at the data center, the data center returns the requested content to the browser. When the requested content is locally unavailable at the data center, the requested content is retrieved from an origin server. When retrieval of the content is delayed, the request is prioritized and placed in a queue for handling by the origin server based on the priority of the request. A status page may be communicated to the browser to inform a user of the delay and provide alternate content and status information related to the request determined as a function of the request or the current state of the origin server.

Abstract: A memory appliance system is described that includes a memory unit comprising a memory unit controller and a plurality of memory devices. A reconfigurable memory structure is stored in the plurality of memory devices, wherein the memory structure comprises a plurality of variably sized containers. Each container of data includes metadata, payload, and relationship information that associates a corresponding container with one or more other containers stored in the memory structure. The controller is data structure aware such that the controller is configured to traverse the memory structure and perform operations on the memory structure based on the metadata and relationship information.

Abstract: A memory appliance system is described and includes a processor coupled to one or more communication channels with a command interface, wherein the processor is configured for communicating commands over the communication channels. A plurality of Smart Memory Cubes (SMCs) is coupled to the processor through the communication channels. Each of the SMCs includes a controller that is programmable, and a plurality of memory devices. The controller is configured to respond to commands from the command interface to access content stored in one or more of the plurality of memory devices and to perform data operations on content accessed from the plurality of memory devices.

Abstract: A memory appliance system is described and includes a plurality of memory devices storing data in a plurality of containers and a controller. The containers include metadata, relationship information associating a respective container with related containers, and a payload. The controller is configured to perform data operations on the payload of one of the containers, and based on the relationship information associating the respective container with related containers and the payload of related containers.

Abstract: The present disclosure relates to a method and system for configuring a computing system, such as a cloud computing system. A method includes providing a user interface comprising selectable boot-time configuration data and selecting, based on at least one user selection of the boot-time configuration data, a boot-time configuration for at least one node of a cluster of nodes of the computing system. The method further includes configuring the at least one node of the cluster of nodes with the selected boot-time configuration to modify at least one boot-time parameter of the at least one node.

Abstract: A method, system and article of manufacture for balancing a workload on heterogeneous processing devices. The method comprising accessing a memory storage of a processor of one type by a dequeuing entity associated with a processor of a different type, identifying a task from a plurality of tasks within the memory that can be processed by the processor of the different type, synchronizing a plurality of dequeuing entities capable of accessing the memory storage, and dequeuing the task form the memory storage.

Abstract: A method and system for dynamic distributed data caching is presented. The system includes one or more peer members and a master member. The master member and the one or more peer members form cache community for data storage. The master member is operable to select one of the one or more peer members to become a new master member. The master member is operable to update a peer list for the cache community by removing itself from the peer list. The master member is operable to send a nominate master message and an updated peer list to a peer member selected by the master member to become the new master member.

Abstract: A data center determines whether requested content is available at the data center. The content is available when the content is both present at the data center and current. When the requested content is available at the data center, the data center returns the requested content to the browser. When the requested content is locally unavailable at the data center, the requested content is retrieved from an origin server. When retrieval of the content is delayed, the request is prioritized and placed in a queue for handling by the origin server based on the priority of the request. A status page may be communicated to the browser to inform a user of the delay and provide alternate content and status information related to the request determined as a function of the request or the current state of the origin server.

Abstract: Methods, systems and computer-readable mediums for task scheduling on an accelerated processing device (APD) are provided. In an embodiment, a method comprises: enqueuing one or more tasks in a memory storage module based on the APD; using a software-based enqueuing module; and dequeuing the one or more tasks from the memory storage module using a hardware-based command processor, wherein the command processor forwards the one or more tasks to the shader cote.

Abstract: Methods and systems for managing data storage and compute resources. The data can be stored a multiple locations allowing compute operations to be performed in a distributed manner in one or more locations. The cloud storage and cloud compute resources can be dynamically scaled based on the locations of the data and based on the cloud storage and/or cloud computing budgets. Dynamic reconfiguration of reconfigurable processors (e.g., FPGA) can further be used to accelerate compute operations.