Abstract: This document relates to orchestration and scheduling of services. One example method involves obtaining dependency information for an application. The dependency information can represent data dependencies between individual services of the application. The example method can also involve identifying runtime characteristics of the individual services and performing automated orchestration of the individual services into one or more application processes based at least on the dependency information and the runtime characteristics.

Abstract: This document relates to orchestration and scheduling of services. One example method involves obtaining dependency information for an application. The dependency information can represent data dependencies between individual services of the application. The example method can also involve identifying runtime characteristics of the individual services and performing automated orchestration of the individual services into one or more application processes based at least on the dependency information and the runtime characteristics.

Abstract: Embodiments are described that leverage variability of a chip. Different areas of a chip vary in terms of reliability under a same operating condition. The variability may be captured by measuring errors over different areas of the chip. A physical factor that affects or controls the likelihood of an error on the chip can be varied. For example, the voltage supplied to a chip may be provided at different levels. At each level of the physical factor, the chip is tested for errors within the regions. Some indication of the error statistics for the regions is stored and then used to adjust power used by the chip, to adjust reliability behavior of the chip, to allow applications to control how the chip is used, to compute a signature uniquely identifying the chip, etc.

Abstract: This document relates to orchestration and scheduling of services. One example method involves obtaining dependency information for an application. The dependency information can represent data dependencies between individual services of the application. The example method can also involve identifying runtime characteristics of the individual services and performing automated orchestration of the individual services into one or more application processes based at least on the dependency information and the runtime characteristics.

Abstract: This document relates to compilation of source code into services. One example method involves receiving input source code, identifying data dependencies in the input source code, and identifying immutability points in the input source code based at least on the data dependencies. The example method also involves converting at least some of the input source code occurring after the immutability points to one or more service modules.

Abstract: Embodiments are described that leverage variability of a chip. Different areas of a chip vary in terms of reliability under a same operating condition. The variability may be captured by measuring errors over different areas of the chip. A physical factor that affects or controls the likelihood of an error on the chip can be varied. For example, the voltage supplied to a chip may be provided at different levels. At each level of the physical factor, the chip is tested for errors within the regions. Some indication of the error statistics for the regions is stored and then used to adjust power used by the chip, to adjust reliability behavior of the chip, to allow applications to control how the chip is used, to compute a signature uniquely identifying the chip, etc.

Abstract: Embodiments are described that leverage variability of a chip. Different areas of a chip vary in terms of reliability under a same operating condition. The variability may be captured by measuring errors over different areas of the chip. A physical factor that affects or controls the likelihood of an error on the chip can be varied. For example, the voltage supplied to a chip may be provided at different levels. At each level of the physical factor, the chip is tested for errors within the regions. Some indication of the error statistics for the regions is stored and then used to adjust power used by the chip, to adjust reliability behavior of the chip, to allow applications to control how the chip is used, to compute a signature uniquely identifying the chip, etc.

Abstract: Embodiments are described that leverage variability of a chip. Different areas of a chip vary in terms of reliability under a same operating condition. The variability may be captured by measuring errors over different areas of the chip. A physical factor that affects or controls the likelihood of an error on the chip can be varied. For example, the voltage supplied to a chip may be provided at different levels. At each level of the physical factor, the chip is tested for errors within the regions. Some indication of the error statistics for the regions is stored and then used to adjust power used by the chip, to adjust reliability behavior of the chip, to allow applications to control how the chip is used, to compute a signature uniquely identifying the chip, etc.

Abstract: Embodiments are described that leverage variability of a chip. Different areas of a chip vary in terms of reliability under a same operating condition. The variability may be captured by measuring errors over different areas of the chip. A physical factor that affects or controls the likelihood of an error on the chip can be varied. For example, the voltage supplied to a chip may be provided at different levels. At each level of the physical factor, the chip is tested for errors within the regions. Some indication of the error statistics for the regions is stored and then used to adjust power used by the chip, to adjust reliability behavior of the chip, to allow applications to control how the chip is used, to compute a signature uniquely identifying the chip, etc.

Abstract: Embodiments are described that leverage variability of a chip. Different areas of a chip vary in terms of reliability under a same operating condition. The variability may be captured by measuring errors over different areas of the chip. A physical factor that affects or controls the likelihood of an error on the chip can be varied. For example, the voltage supplied to a chip may be provided at different levels. At each level of the physical factor, the chip is tested for errors within the regions. Some indication of the error statistics for the regions is stored and then used to adjust power used by the chip, to adjust reliability behavior of the chip, to allow applications to control how the chip is used, to compute a signature uniquely identifying the chip, etc.

Abstract: Embodiments are described that leverage variability of a chip. Different areas of a chip vary in terms of reliability under a same operating condition. The variability may be captured by measuring errors over different areas of the chip. A physical factor that affects or controls the likelihood of an error on the chip can be varied. For example, the voltage supplied to a chip may be provided at different levels. At each level of the physical factor, the chip is tested for errors within the regions. Some indication of the error statistics for the regions is stored and then used to adjust power used by the chip, to adjust reliability behavior of the chip, to allow applications to control how the chip is used, to compute a signature uniquely identifying the chip, etc.

Abstract: Power management functionality is described for implementing an application in an energy-efficient manner, without substantially degrading overall performance of the application. The functionality operates by identifying at least first data and second data associated with the application. The first data is considered to have a greater potential impact on performance of the application compared to the second data. The functionality then instructs a first set of hardware-level resources to handle the first data and a second set of hardware-level resources to handle the second data. The first set of hardware-level resources has a higher reliability compared to the second set of hardware-level resources. In one case, the first and second hardware-level resources comprise DRAM memory units. Here, the first set of hardware-level resources achieves greater reliability than the second set of hardware-level resources by being refreshed at a higher rate than the second set of hardware-level resources.

Abstract: Embodiments are described that leverage variability of a chip. Different areas of a chip vary in terms of reliability under a same operating condition. The variability may be captured by measuring errors over different areas of the chip. A physical factor that affects or controls the likelihood of an error on the chip can be varied. For example, the voltage supplied to a chip may be provided at different levels. At each level of the physical factor, the chip is tested for errors within the regions. Some indication of the error statistics for the regions is stored and then used to adjust power used by the chip, to adjust reliability behavior of the chip, to allow applications to control how the chip is used, to compute a signature uniquely identifying the chip, etc.

Abstract: Embodiments are described that leverage variability of a chip. Different areas of a chip vary in terms of reliability under a same operating condition. The variability may be captured by measuring errors over different areas of the chip. A physical factor that affects or controls the likelihood of an error on the chip can be varied. For example, the voltage supplied to a chip may be provided at different levels. At each level of the physical factor, the chip is tested for errors within the regions. Some indication of the error statistics for the regions is stored and then used to adjust power used by the chip, to adjust reliability behavior of the chip, to allow applications to control how the chip is used, to compute a signature uniquely identifying the chip, etc.

Abstract: Embodiments are described that leverage variability of a chip. Different areas of a chip vary in terms of reliability under a same operating condition. The variability may be captured by measuring errors over different areas of the chip. A physical factor that affects or controls the likelihood of an error on the chip can be varied. For example, the voltage supplied to a chip may be provided at different levels. At each level of the physical factor, the chip is tested for errors within the regions. Some indication of the error statistics for the regions is stored and then used to adjust power used by the chip, to adjust reliability behavior of the chip, to allow applications to control how the chip is used, to compute a signature uniquely identifying the chip, etc.

Abstract: Typical computer programs may incur costly memory errors that result in corrupted data. A new memory model is presented wherein it may be determined that certain data is critical and critical data may be stored and protected during computer application execution. Critical Memory allows that data determined to be critical may be stored and retrieved using functions enabled to increase the reliability of the data. Critical Memory presents a memory model where a subset of memory designated as critical memory may be used to store a subset of data deemed critical data. Probabilistic guarantees of data value consistency are provided by the employment of the new memory model. The memory model and functions presented are compatible with existing third-party libraries such that third-party libraries may be compatibly called from processes using critical memory.

Abstract: Embodiments are described that leverage variability of a chip. Different areas of a chip vary in terms of reliability under a same operating condition. The variability may be captured by measuring errors over different areas of the chip. A physical factor that affects or controls the likelihood of an error on the chip can be varied. For example, the voltage supplied to a chip may be provided at different levels. At each level of the physical factor, the chip is tested for errors within the regions. Some indication of the error statistics for the regions is stored and then used to adjust power used by the chip, to adjust reliability behavior of the chip, to allow applications to control how the chip is used, to compute a signature uniquely identifying the chip, etc.

Abstract: Power management functionality is described for implementing an application in an energy-efficient manner, without substantially degrading overall performance of the application. The functionality operates by identifying at least first data and second data associated with the application. The first data is considered to have a greater potential impact on performance of the application compared to the second data. The functionality then instructs a first set of hardware-level resources to handle the first data and a second set of hardware-level resources to handle the second data. The first set of hardware-level resources has a higher reliability compared to the second set of hardware-level resources. In one case, the first and second hardware-level resources comprise DRAM memory units. Here, the first set of hardware-level resources achieves greater reliability than the second set of hardware-level resources by being refreshed at a higher rate than the second set of hardware-level resources.

Abstract: Typical computer programs may incur costly memory errors that result in corrupted data. A new memory model is presented wherein it may be determined that certain data is critical and critical data may be stored and protected during computer application execution. Critical Memory allows that data determined to be critical may be stored and retrieved using functions enabled to increase the reliability of the data. Functions are presented enabling allocation of redundant computer memory; functions are presented enabling consistently writing critical data to redundant locations; and functions are presented enabling reading critical data while ensuring that the data read is consistent with the most recent write of critical data and enabled to repair inconsistent data. The memory model and functions presented are designed to be compatible with existing third-party libraries.

Abstract: Typical computer programs may incur costly memory errors that result in corrupted data. A new memory model is presented wherein it may be determined that certain data is critical and critical data may be stored and protected during computer application execution. Critical Memory allows that data determined to be critical may be stored and retrieved using functions enabled to increase the reliability of the data. Critical Memory presents a memory model where a subset of memory designated as critical memory may be used to store a subset of data deemed critical data. Probabilistic guarantees of data value consistency are provided by the employment of the new memory model. The memory model and functions presented are compatible with existing third-party libraries such that third-party libraries may be compatibly called from processes using critical memory.