SNC：基于即时编译的符号-数值计算云服务平台
SNC: A Cloud Service Platform for Symbolic-Numeric Computation using Just-In-Time Compilation
摘要：
云服务在IT产业和科学研究中得到了广泛的应用。通过使用云服务，用户可以将计算任务和数据从本地计算机转移到远程数据中心。通过在轻量级和移动设备上访问基于互联网的服务，用户可以在功能强大的机器上部署多样化的云应用程序。科学计算领域实现这一范例的关键驱动因素包括大量的计算能力、按需提供和跨平台互操作性。然而，为了充分利用云服务进行科学计算，我们需要设计一个特定于应用程序的平台，以帮助用户有效地迁移他们的应用程序。在此，我们提出了一个云服务平台的符号-数值计算- SNC。SNC允许云用户通过C/C++、Python、Java API和SNC脚本将任务描述为符号表达式。通过使用LLVM/JVM的即时（JIT）编译用于将用户代码编译为机器码。我们实施了SNC设计，并在几个流行的云平台（包括Google Compute Engine，Amazon EC2，Microsoft Azure，Rackspace，HP Helion和VMWare vCloud）上测试了广泛的符号-数值计算应用程序（包括非线性最小化，Monte Carlo积分，有限元装配和多体动力学）。这些结果表明，我们的方法可以在多个云平台上工作，支持不同的语言，并显着提高使用云平台的符号-数值计算的性能。这为在科学研究领域使用云计算进行符号-数值计算提供了一种方法。
英文摘要：
Cloud services have been widely employed in IT industry and scientific research. By using Cloud services users can move computing tasks and data away from local computers to remote datacenters. By accessing Internet-based services over lightweight and mobile devices, users deploy diversified Cloud applications on powerful machines. The key drivers towards this paradigm for the scientific computing field include the substantial computing capacity, on-demand provisioning and cross-platform interoperability. To fully harness the Cloud services for scientific computing, however, we need to design an application-specific platform to help the users efficiently migrate their applications. In this, we propose a Cloud service platform for symbolic-numeric computation - SNC. SNC allows the Cloud users to describe tasks as symbolic expressions through C/C++, Python, Java APIs and SNC script. Just-In-Time (JIT) compilation through using LLVM/JVM is used to compile the user code to the machine code. We implemented the SNC design and tested a wide range of symbolic-numeric computation applications (including nonlinear minimization, Monte Carlo integration, finite element assembly and multibody dynamics) on several popular cloud platforms (including the Google Compute Engine, Amazon EC2, Microsoft Azure, Rackspace, HP Helion and VMWare vCloud). These results demonstrate that our approach can work across multiple cloud platforms, support different languages and significantly improve the performance of symbolic-numeric computation using cloud platforms. This offered a way to stimulate the need for using the cloud computing for the symbolic-numeric computation in the field of scientific research.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
