Abstract: A temperature-aware task scheduling method, system, and computer program product for facilitating a task in a multi-graphical processing unit (GPU) environment, includes executing the task to a GPU in the multi-GPU environment based on a thermal characteristic of the GPU as compared to the other GPUs in the multi-GPU environment.

Abstract: A temperature-aware task scheduling method, system, and computer program product, includes learning a condition to accept a task to the GPU based on a prior execution of the task on the GPU according to a varying thermal characteristic of the GPU.

Abstract: The present invention relates to a pharmaceutical composition, which is characterized in that the composition comprises an effective amount of proanthocyanidin, the monomer units of the proanthocyanidin have the chemical formula of formula (I), and a pharmaceutically acceptable carrier or salt; wherein the proanthocyanidin is a polymeric proathocyanidin with a degree of polymerization ranging from 50-65. It has good antioxidant stress and can be used to treat or prevent brain diseases and aging-related diseases caused by excessive production of ROS, or to prevent and treat aging. It can also be used for the treatment and prevention of liver disease, tumor, sarcopenia and other diseases.

Abstract: Embodiments for providing critical path analysis of active trace files in a cloud computing environment. A critical path may be identified using a trace of time spans and activities of a plurality of applications, wherein the critical path is a set of activities having time spans free of overlap with other activities.

Abstract: Transfer learning in machine learning can include receiving a machine learning model. Target domain training data for reprogramming the machine learning model using transfer learning can be received. The target domain training data can be transformed by performing a transformation function on the target domain training data. Output labels of the machine learning model can be mapped to target labels associated with the target domain training data. The transformation function can be trained by optimizing a parameter of the transformation function. The machine learning model can be reprogrammed based on input data transformed by the transformation function and a mapping of the output labels to target labels.

Abstract: A temperature-aware task scheduling method, system, and computer program product, includes learning a condition to accept a task to the GPU based on a prior execution of the task on the GPU according to a varying thermal characteristic of the GPU.

Abstract: Embodiments for providing critical path analysis of active trace files in a cloud computing environment. A critical path may be identified using a trace of time spans and activities of a plurality of applications, wherein the critical path is a set of activities having time spans free of overlap with other activities.

Abstract: Embodiments for providing direct access to non-volatile memory by a processor. One or more accelerators may be provided, via an application programming interface (“API”), direct access to non-volatile storage independent of a host central processing unit (“CPU”) on a control path or data path to perform a read operation and write operation of data.

Abstract: A temperature-aware task scheduling method, system, and computer program product, includes obtaining a temperature of the GPU and accepting and executing the task to the GPU, in response to the obtaining a temperature of the GPU.

Abstract: A method and an apparatus of allocating available resources in a cluster system with learning models and tuning methods are provided. The learning model may be trained from historic performance data of previously executed jobs and used to project a suggested amount of resources for execution of a job. The tuning process may suggest a configuration for the projected amount of resources in the cluster system for an optimal operating point. An optimization may be performed with respect to a set of objective functions to improve resource utilization and system performance while suggesting the configuration. Through many executions and job characterization, the learning/tuning process for suggesting the configuration for the projected amount of resources may be improved by understanding correlations of historic data and the objective functions.

Abstract: Embodiments for crash recoverability for graphics processing units (GPUs) by a processor. GPU application data and kernel execution state of one or more GPUs may be checkpointed. The checkpointed GPU application data and the kernel execution state may be recovered. The checkpointed GPU application data and the kernel execution state may be persisted on non-volatile memory.

Abstract: Embodiments for providing direct access to non-volatile memory by a processor. One or more accelerators may be provided, via an application programming interface (“API”), direct access to non-volatile storage independent of a host central processing unit (“CPU”) on a control path or data path to perform a read operation and write operation of data.

Abstract: Embodiments for crash recoverability for graphics processing units (GPUs) by a processor. GPU application data and kernel execution state of one or more GPUs may be checkpointed. The checkpointed GPU application data and the kernel execution state may be recovered. The checkpointed GPU application data and the kernel execution state may be persisted on non-volatile memory.

Abstract: A data-centric reduction method, system, and computer program product include configuring a similarity threshold and a correlation threshold for an entire data set from at least two back-end nodes, reducing the entire data set to a reduced data set from the at least two back-end nodes sent to a front-end node by removing data based on the similarity threshold and the correlation threshold, and after the front-end receives the reduced data set, reconstructing the entire data set from the reduced data set using the similarity threshold and correlation threshold.

Abstract: A temperature-aware task scheduling method, system, and computer program product, includes obtaining a temperature of the GPU and accepting and executing the task to the GPU, in response to the obtaining a temperature of the GPU.

Abstract: A temperature-aware task scheduling method, system, and computer program product, include determining a change in an operation intensity factor of the GPU from a previous state and modifying the operation intensity factor, in response to the determining the change in the operation intensity factor from the previous state.

Abstract: A data-centric reduction method, system, and computer program product include configuring a similarity threshold and a correlation threshold for an entire data set from at least two back-end nodes, reducing the entire data set to a reduced data set from the at least two back-end nodes sent to a front-end node by removing data based on the similarity threshold and the correlation threshold, and after the front-end receives the reduced data set, reconstructing the entire data set from the reduced data set using the similarity threshold and correlation threshold.

Abstract: Optimizing the performance of a machine learning system includes: defining an n-dimensional approximate computing configuration space, the n-dimensional approximate computing configuration space defining tuning parameters for tuning the machine learning system; setting a performance objective for the machine learning system that identifies one or more machine learning system performance criteria; collecting and monitoring performance data; comparing the performance data to the machine learning system performance objective; and dynamically updating the n-dimensional approximate computing configuration space by adjusting the at least one tuning parameter, in response to the comparison.

Abstract: A temperature-aware task scheduling method, system, and computer program product, include determining a change in an operation intensity factor of the GPU from a previous state and modifying the operation intensity factor, in response to the determining the change in the operation intensity factor from the previous state.

Abstract: A temperature-aware task scheduling method, system, and computer program product, includes the GPU, receiving a request to execute the task, collecting task information including an intensiveness factor of a computation by an arithmetic logic unit (ALU) and a memory usage of a dynamic random-access memory (DRAM) for the task, obtaining a temperature of the ALU and a temperature of the DRAM, and accepting the task to the GPU based on the intensiveness factor, the ALU temperature, and the DRAM temperature.