The growth in the data and computation need of today’s operations has led to technical solutions that distribute workload over several entities for better performance. To facilitate such a paradigm, research studies have been investigating efficient approaches for combining high performance computing in shared-memory with distributed-memory environments. Meanwhile, the benefits of cloud computing and its modern enhancements have created potentials for applications to leverage the powerful, ubiquitous and cheap resources of cloud infrastructures. Yet, a small portion of the work in this area addresses the programming aspects of cloud-related technologies. Despite the extensive improvements in the fundamental mechanisms of this realm, programming environments offer little support for incorporating the high performance mechanisms of shared-memory computing with cloud computing. This study proposes a solution for an unobtrusive definition and integration of cloud-based and shared-memory parallel computing, in order to further simplify the application of cloud capabilities in local systems. It does so by implementing the proposed concepts in @PT (Annotation Parallel Task), a parallel-programming environment that utilizes native Java annotations as its language constructs. The experimental evaluations discussed here demonstrate that the proposed approach facilitates achieving the potential benefits of cloud computing for performance and energy consumption in local devices.

中文翻译：

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统一编程概念，实现基于云的并行计算和本地并行计算的低调集成

当今运营的数据和计算需求的增长催生了将工作负载分配给多个实体以获得更好性能的技术解决方案。为了促进这种范例，研究一直在研究将共享内存中的高性能计算与分布式内存环境相结合的有效方法。同时，云计算的优势及其现代增强功能为应用程序利用云基础设施强大、无处不在且廉价的资源创造了潜力。然而，该领域的一小部分工作涉及云相关技术的编程方面。尽管该领域的基本机制得到了广泛的改进，但编程环境几乎不支持将共享内存计算的高性能机制与云计算相结合。本研究提出了一种基于云和共享内存并行计算的不显眼定义和集成的解决方案，以进一步简化云功能在本地系统中的应用。它通过在@PT（注释并行任务）中实现建议的概念来实现这一点，@PT 是一个利用本机 Java 注释作为其语言构造的并行编程环境。这里讨论的实验评估表明，所提出的方法有助于实现云计算在本地设备性能和能耗方面的潜在优势。