Abstract: According to an example, memory integrity checking may include receiving computer program code, and using a loader to load the computer program code in memory. Memory integrity checking may further include verifying the integrity of the computer program code by selectively implementing synchronous verification and/or asynchronous verification. The synchronous verification may be based on loader security features associated with the loading of the computer program code. Further, the asynchronous verification may be based on a media controller associated with the memory containing the computer program code.

Abstract: A color image is processed into a renderable image. The color image comprises a plurality of pixels. Each pixel has colorimetry defined in a first color space. The renderable image comprises a plurality of renderable pixels defined by a device-vector in a second color space. For each pixel: a device-vector defined in the second color space is selected (301) based on the colorimetry defined in a first color space of the pixel. The device-vector comprises a plurality of elements. Each element includes an identifier and an accumulated weighting. An element of the selected device-vector is reselected (303) until the accumulated weighting (a) is greater than a threshold value (t) associated with the pixel (305). The levels for each color of the second color space (or mappings) for the currently selected (307) element of the selected device-vector is determined (309) to convert the pixel into a renderable pixel.

Abstract: According to an example, memory traffic including memory access commands is routed between compute nodes and memory nodes in a memory network. Other traffic is also routed in the memory network. The other traffic may include input/output traffic between the compute nodes and peripherals connected to the memory network.

Abstract: According to an example, a resiliency group for a memory node in a memory network can provide error correction for a memory access in the memory node. The memory access may be received from a main memory controller of a processor connected to the memory network. The memory access may be executed by a memory controller of the memory node.

Abstract: A method for performing memory operations is provided. One or more processors can determine that at least a portion of data stored in a cache memory of the one or more processors is to be stored in the main memory. One or more ranges of addresses of the main memory is determined that correspond to a plurality of cache lines in the cache memory. A set of cache lines corresponding to addresses in the one or more ranges of addresses is identified, so that data stored in the identified set can be stored in the main memory. For each cache line of the identified set having data that has been modified since that cache line was first loaded to the cache memory or since a previous store operation, data stored in that cache line is caused to be stored in the main memory.

Abstract: According to an example, a memory network includes memory nodes. The memory nodes may each include memory and control logic. The control logic may operate the memory node as a destination for a memory access invoked by a processor connected to the memory network and may operate the memory node as a router to route data or memory access commands to a destination in the memory network.

Abstract: Systems and methods of vertically aggregating tiered servers in a data center are disclosed. An example method includes partitioning a plurality of servers in the data center to form an array of aggregated end points (AEPs). Multiple servers within each AEP are connected by an intra-AEP network fabric and different AEPs are connected by an inter-AEP network. Each AEP has one or multiple central hub servers acting as end-points on the inter-AEP network. The method includes resolving a target server identification (ID). If the target server ID is the central hub server in the first AEP, the request is handled in the first AEP. If the target server ID is another server local to the first AEP, the request is redirected over the intra-AEP fabric. If the target server ID is a server in a second AEP, the request is transferred to the second AEP.

Abstract: Methods, systems, and machine-readable and executable instructions are provided for balancing the allocation of a number of virtual machines (VMs) in clouds systems. Balancing the allocation of VMs in cloud systems can include determining a number of relationships of a plurality of nodes in a cloud system. Balancing the allocation of VMs in cloud systems can include assigning a number of VMs to the plurality of nodes in response to a request based on a criteria of the request and the number of relationships of the plurality of nodes to balance VMs in the cloud system, where the criteria include a type and a priority for each of the number of VMs.

Abstract: Example embodiments relate to autonomous metric tracking and adjustment. In some examples, a computing node may include a processor to run a main operating system and an application that runs on top of the main operating system. The computing node may include a hardware-level controller that dynamically adjusts individual hardware components of the computing node via control signals that do not pass through the main operating system. The adjustments may be based on a target metric from a scheduling service external to the computing node and individual performance metrics from the computing node.

Abstract: Example methods for finding a text reading order in a document are described in which text zones are determined, the text zones are clustered using semantic measure and correlation and a reading order is found within each of the clusters.

Abstract: The mapping of High Performance Computing (“HPC”) applications to platforms is provided. An HPC application characterization module determines an HPC application signature to characterize the HPC application. An HPC application mapping module selects a platform from a plurality of platforms to execute the HPC application based on the HPC application signature and a set of benchmarks. An HPC application monitoring module monitors the execution of the HPC application on the selected platform.

Abstract: Versioned memories using a multi-level cell (MLC) are disclosed. An example method includes comparing a global memory version to a block memory version, the global memory version corresponding to a plurality of memory blocks, the block memory version corresponding to one of the plurality of memory blocks. The example method includes determining, based on the comparison, which level in a multi-level cell of the one of the plurality of memory blocks stores checkpoint data.

Abstract: An automated document processing system is configured to normalize zones obtained from a document, and to extract articles from the normalized zones. In one configuration, the system receives at least one zone from the document, and applies at least one zone-breaking factor, thereby creating normalized sub-zones within which text lines are consistent with the at least one zone-breaking factor. The normalized sub-zones may be evaluated to obtain a reading order. Adjacent sub-zones are joined if text similarity exceeds a threshold value. Weakly joined sub-zones are separated where indicated by a topic vectors analysis of the weakly joined sub-zones.

Abstract: Examples of the present disclosure include methods, devices, and/or systems. Identifying network communication patterns can include analyzing a distributed computer program of a network, estimating virtual network communication traffic based on the analysis, and mapping the virtual network communication traffic to a physical network link. Identifying network communications patterns can also include identifying the network communication pattern and categorizing the physical communication network link based on an estimated communication intensity of the mapped communication traffic and the network communication pattern. Identifying network communication patterns can further include optimizing an energy used by the network based on the categorization.

Abstract: Example embodiments relate to autonomous metric tracking and adjustment. In some examples, a computing node may include a processor to run a main operating system and an application that runs on top of the main operating system. The computing node may include a hardware-level controller that dynamically adjusts individual hardware components of the computing node via control signals that do not pass through the main operating system. The adjustments may be based on a target metric from a scheduling service external to the computing node and individual performance metrics from the computing node.

Abstract: Local checkpointing using a multi-level call is described herein. An example method includes storing a first datum in a first level of a multi-level cell. A second datum is stored in a second level of the multi-level cell, the second datum representing a checkpoint of the first datum. The first datum is copied from the first level to the second level of the multi-level cell to create the checkpoint.

Abstract: In a method for managing a memory segment through use of a memory virtual appliance, data is encapsulated with the memory virtual appliance, in which the memory virtual appliance comprises a virtual machine configured to manage a memory segment in a physical memory. In addition, the memory virtual appliance is implemented using a virtualization wrapper comprising computer readable code enabling the encapsulated data to be shared among a plurality of clients. Moreover, the encapsulated data is stored in the memory segment controlled by the memory virtual appliance.

Abstract: Methods, systems, and machine-readable and executable instructions are provided for balancing the allocation of a number of virtual machines (VMs) in clouds systems. Balancing the allocation of VMs in cloud systems can include determining a number of relationships of a plurality of nodes in a cloud system. Balancing the allocation of VMs in cloud systems can include assigning a number of VMs to the plurality of nodes in response to a request based on a criteria of the request and the number of relationships of the plurality of nodes to balance VMs in the cloud system, where the criteria include a type and a priority for each of the number of VMs.

Abstract: Systems, methods, and machine-readable and executable instructions are provided for dynamically balancing execution resources to meet a budget and/or a QoS of projects. An example method can include analyzing a submitted program for a project, where the program comprises data to execute the project and a specification for the project, determining a computing resource allocation based upon the submitted data and the specification, and deploying for execution the submitted data to the determined computing resource allocation. The method can include monitoring progress during the execution of the data to determine a probability of project completion satisfying the specification, and dynamically balancing the execution resources to meet the budget and/or the QoS of the project to satisfy the specification.

Abstract: Methods, apparatus and articles of manufacture to secure non-volatile memory regions are disclosed. An example method disclosed herein comprises associating a first key pair and a second key pair different than the first key pair with a process, using the first key pair to secure a first region of a non-volatile memory for the process, and using the second key pair to secure a second region of the non-volatile memory for the same process, the second region being different than the first region.