Abstract: A computer-implemented method for detecting n-level slopes for a voltage level in a processor, wherein n is greater than 1, includes monitoring a voltage in a processor for a voltage level corresponding to a predefined first edge, and monitoring for a voltage level corresponding to a predefined second edge within a first count limit from detection of the predefined first edge. In response to detecting the predefined second edge within the first count limit, the computer-implemented method includes monitoring for a voltage level corresponding to a predefined third edge within a second count limit from detection of the predefined second edge, and in response to detecting the predefined third edge within the second count limit, determining whether to adjust a power applied to the processor based on the monitored voltage levels.

Abstract: A computer-implemented method, according to one embodiment, includes determining that a first predetermined pattern is to be written to a first cache line of a cache. In response to the determination, a first bit is set in a first directory instead of writing the first predetermined pattern in the first cache line. The first bit is associated with the first cache line in the first directory. A computer program product, according to another embodiment, includes a computer readable storage medium having program instructions embodied therewith. The program instructions are readable and/or executable by a computer to cause the computer to perform the foregoing method. A system, according to another embodiment, includes a processor, and logic integrated with the processor, executable by the processor, or integrated with and executable by the processor. The logic is configured to perform the foregoing method.

Abstract: A computer implemented method handles object references. A computer system determines whether an object reference fetched by a load instruction has an expected value for heap protection for a heap in response to receiving the load instruction for execution. The computer system generates an event in response to the object reference not being the expected value, wherein the event is used to manage the object reference. According to other illustrative embodiments, a computer system and a computer program product for managing object references are provided. As a result, the illustrative embodiments can prevent a load instruction from loading an invalid object reference that does not point to a memory location in a heap.

Abstract: Embodiments include in response to monitoring a processor during operation, detecting a first number of throttling amounts in the processor, determining that the first number of throttling amounts fulfills a first condition regarding a throttling amounts threshold, and modifying a voltage level of the processor by a first amount. Embodiments include in response to modifying the voltage level of the processor by the first amount, detecting a second number of throttling amounts in the processor, determining that the second number of throttling amounts fulfills a second condition regarding the throttling amounts threshold, and modifying the voltage level of the processor by a second amount.

Abstract: A method, system and apparatus for providing bound information accesses in buffer protection, including providing one-to-one mapping between a general-purpose register and bound information in a BI (bound information) register, saving loaded bound information in the BI register for future use, providing integrity of the bound information in the BI register that is maintained along program execution, and providing a pro-active load of the bound information with one-bit extra control on load instruction of the BI register.

Abstract: Embodiments of a method are disclosed. The method includes performing distributed deep learning training on multiple batches of training data using corresponding learners. Additionally, the method includes determining training times wherein the learners perform the distributed deep learning training on the batches of training data. The method also includes modifying a processing aspect of the straggler to reduce a future training time of the straggler for performing the distributed deep learning training on a new batch of training data in response to identifying a straggler of the learners by a centralized control.

Abstract: Tasks of directed acyclic graphs (DAGs) may be dynamically scheduled based on a plurality of constraints and conditions, task prioritization policies, task execution estimates, and configurations of a heterogenous system. A machine learning component may be initialized to dynamically schedule the tasks of the DAGs.

Abstract: A method, system and apparatus for protecting against out-of-bounds references, including storing an address of a buffer in a general register and storing bounds information (BI) for the buffer in a bounds information register, and when a content of the general register is used as an address in a load or store operation, using a content of the bounds information register to determine if the load or store is out of bounds.

Abstract: Techniques facilitating hardware-based memory-error mitigation for heap-objects. In one example, a system can comprise a process that executes computer executable components stored in a non-transitory computer readable medium. The computer executable components comprise: an entry component; and a re-purpose component. The entry component can allocate an entry in a table to store bounds-information when an object is allocated in memory. The re-purpose component can re-purpose unused bits of an object address to store an index to the table entry.

Abstract: Embodiments include in response to monitoring a processor during operation, detecting a first number of core recovery events in the processor, determining that the first number of core recovery events fulfills a first condition for the first core recovery events threshold, and modifying a value of at least one droop sensor parameter of the processor by a first amount. The at least one droop sensor parameters affects a sensitivity to a voltage droop. In response to modifying the value of the droop sensor parameter by the first amount, a second number of core recovery events is detected in the processor. It is determined that the second number of core recovery events fulfills a second condition for a second core recovery events threshold, and the value of the at least one droop sensor parameter is modified by a second amount.

Abstract: Embodiments include exerciser device placement in the development of an integrated circuit. Aspects of the invention include obtaining a design of an integrated circuit and creating a dynamic power blockage map for the integrated circuit. Aspects also include updating the integrated circuit design by placing one or more exercisers on the integrated circuit, wherein a location of the one or more exercisers on the integrated circuit is based on at least in part on the dynamic power blockage map. Based on a determination that the updated integrated circuit design complies with one or more design constraints, aspects further include outputting the updated integrated circuit design.

Abstract: A method, system and apparatus for protecting against out-of-bounds references, including storing an address of a buffer in a general register and storing bounds information (BI) for the buffer in a bounds information register, and when a content of the general register is used as an address in a load or store operation, using a content of the bounds information register to determine if the load or store is out of bounds.

Abstract: A first type memory and a second type memory may be identified in a computing system. The second type memory is slower than the first type memory while having a greater storage capacity compared to the first type memory. An application process executing in the computing system may be identified. A region of the first type memory may be provided as a cache of the second type memory for the application process.

Abstract: Various embodiments are provided herein for clearing memory of system in a computing environment. A zero-filled cache line with a single z-bit per entry in the cache directory may be defined. The “z” is a positive integer. A plurality of instruction set architecture (“ISA”) instructions are provided with a single z-bit in a cache line as defined in a cache directory to clear an entire cache line.

Abstract: A method, system, and computer program product for augmenting cache replacement operations are provided. The method identifies a set of cache lines within a first cache level of a multilevel cache. A first candidate cache line is identified based on a first replacement scheme of the first cache level. A second candidate cache line is identified based on the first replacement scheme of the first cache level. A replacement cache line is selected for replacement in the first cache level. The replacement cache line is selected from the first candidate cache line and the second candidate cache line and based on the first replacement scheme of the first cache level and a second replacement scheme of a second cache level. The method removes the replacement cache line from the first cache level.

Abstract: Embodiments of a method are disclosed. The method includes performing distributed deep learning training on a batch of training data. The method also includes determining training times representing an amount of time between a beginning batch time and an end batch time. Further, the method includes modifying a communication aspect of the communication straggler to reduce a future network communication time for the communication straggler to send a future result of the distributed deep learning training on a new batch of training data in response to the centralized parameter server determining that the learner is the communication straggler.

Abstract: Embodiments include in response to monitoring a processor during operation, detecting a first number of throttling amounts in the processor, determining that the first number of throttling amounts fulfills a first condition regarding a throttling amounts threshold, and modifying a voltage level of the processor by a first amount. Embodiments include in response to modifying the voltage level of the processor by the first amount, detecting a second number of throttling amounts in the processor, determining that the second number of throttling amounts fulfills a second condition regarding the throttling amounts threshold, and modifying the voltage level of the processor by a second amount.

Abstract: Embodiments include in response to monitoring a processor during operation, detecting a first number of core recovery events in the processor, determining that the first number of core recovery events fulfills a first condition for the first core recovery events threshold, and modifying a value of at least one droop sensor parameter of the processor by a first amount. The at least one droop sensor parameters affects a sensitivity to a voltage droop. In response to modifying the value of the droop sensor parameter by the first amount, a second number of core recovery events is detected in the processor. It is determined that the second number of core recovery events fulfills a second condition for a second core recovery events threshold, and the value of the at least one droop sensor parameter is modified by a second amount.

Abstract: Embodiments of a method are disclosed. The method includes performing decentralized distributed deep learning training on a batch of training data. Additionally, the method includes determining a training time wherein the learner performs the decentralized distributed deep learning training on the batch of training data. Further, the method includes generating a table having the training time and other processing times for corresponding other learners performing the decentralized distributed deep learning training on corresponding other batches of other training data. The method also includes determining that the learner is a straggler based on the table and a threshold for the training time. Additionally, the method includes modifying a processing aspect of the straggler to reduce a future training time of the straggler for performing the decentralized distributed deep learning training on a new batch of training data in response to determining the learner is the straggler.

Abstract: An example system includes a processor to prune a machine learning model based on an importance of neurons or weights. The processor is to further permute and pack remaining neurons or weights of the pruned machine learning model to reduce an amount of ciphertext computation under a selected constraint.