Summary Parallelization is an optimization technique that is playing an increasingly vital role in software applications. Discovering the potential parallelism in sequential programs is of primary importance for parallelization. To this end, various tools have been created to obtain parallelizable targets by analyzing the dependencies in sequential programs. However, the majority are constructed based on manually designed dependence analysis rules. Consequently, they must follow some constraints, which limit the scope of their program analysis capability. Furthermore, constructing a dependency analysis rule is a complicated and highly sophisticated endeavor. In this study, we exploit the fact that neural network models can learn the potential features of programs to tackle the parallelism discovery task in an end‐to‐end manner. Specifically, we developed a multigraph learning architecture on top of multiple abstract code representations in a complementary manner. In the architecture, we equip code representations with different neural network models in a targeted manner, including a deep convolutional neural network for analyzing both control and data flows and a novel neural network for learning the abstract syntax trees. Experimental results obtained on a common parallelism discovery dataset indicate that the developed multigraph learning architecture can learn potential parallelism patterns in sequential programs with high accuracy and efficiency.

Measurements of vital signs can be translated into accurate predictors of pregnant diseases, even at an early stage. They can also be combined with alarm-triggering systems to initiate the appropriate actions. Because of the emphasis on healthcare awareness and their specific needs, gravidae prefer regular vital signs monitoring in a flexible manner. Thus, different types of sensors are used that involve complex operations, networks, and results. To enhance usability and feasibility, we propose a mobile vital signs monitoring system based on multi-communication fusion and the Android OS so pregnant women can monitor maternal and fetal information anywhere they want. They can also access comprehensive care by transferring data to the server for further processing and remote diagnosis. The accuracy of remote diagnosis is also improved.

The performance of a given cache architecture is largely determined by the behavior of the application using the cache. Reconfigurable cache is an effective low-power technique. Using the technique, microprocessor's cache can be configured dynamically to adapt itself to the requirement of running program, and minimize the energy consumption and performance loss. We introduce a phase-based self-tuning algorithm (PBSTA), which can automatically, transparently, and dynamically manage the reconfigurable cache on a per-phase basis. In contrast with previous works, the algorithm seeks not only to lower the cache's energy consumption effectively, but also reduce the performance loss due to unnecessary reconfigurations. By simulating numerous MiBench benchmarks, the results show that the PBSTA, when applied to reconfigurable cache, saves on average 40% of total memory access energy compared with a conventional cache and the associated performance loss is close to 1.8%.

Reconfigurable cache with a set of adjustable configurations can be configured dynamically to adapt itself to the change in program characteristics and has tremendous benefits for performance and energy. However, how to dynamically manage reconfigurable cache is still a cumbersome task left for designers. We introduce a self-tuning algorithm (ETCA), which can dynamically manage reconfigurable cache on a per-phase basis. In contrast with previous works, ETCA seeks not only to lower cache's energy consumption effectively, but also reduce the performance loss due to unnecessary reconfigurations. By simulating numerous MiBench benchmark, the results show that ETCA, when applied to reconfigurable cache, saves on average 38% of total memory access energy compared with a conventional cache and the associated performance loss is close to 1.8%