Today, an ever increasing number of natural scientists use computers for data analysis, modeling, simulation and visualization of complex problems. However, in the last decade the computer architecture has changed sig...Today, an ever increasing number of natural scientists use computers for data analysis, modeling, simulation and visualization of complex problems. However, in the last decade the computer architecture has changed significantly, making it increasingly difficult to fully utilize the power of the processor, unless the scientist is a trained programmer. The reasons for this shift include the change from single-core to multi-core processors, as well as the decreasing price of hardware, which allows researchers to build cluster computers made from commodity hardware. Therefore, scientists must not only be able to handle multi-core processors, but also the problems associated with writing distributed memory programs and handle communication between hundreds of multi-core machines. Fortunately, there are a number of systems to help the scientist e.g. Message Parsing Interface (MPI) [1] for handling communication, DistNumPy [2] for handling data distribution and Communicating Sequential Processes (CSP) [3] for handling concurrency related problems. Having said that, it must be emphasized that all of these methods require that the scientists learn a new method and then rewrite their programs, which mean more work for the scientist. A solution that does not require much work for the scientists is automatic parallelization. However, research dating back three decades has yet to find fully automated parallelization as a feasible solution for programs in general, but some classes of programs can be automatically parallelized to an extent. This paper describes an external library which provides a Parallel. For loop construct, allowing the body of a loop to be run in Parallel across multiple networked machines, i.e. on distributed memory architectures. The individual machines themselves may be shared memory nodes of course. The idea is inspired by Microsoft’s Parallel Library that supplies multiple Parallel constructs. However, unlike Microsoft’s Library our library supports distributed memory architectures. Preliminary tests have shown that simple problems may be distributed easily and achieve good scalability. Unfortunately, the tests show that the scalability is limited by the number of accesses made to shared variables. Thus the applicability of the library is not general but limited to a subset of applications with only limited communication needs.展开更多
Microsoft Excel is essential for the End-User Approach (EUA), offering versatility in data organization, analysis, and visualization, as well as widespread accessibility. It fosters collaboration and informed decision...Microsoft Excel is essential for the End-User Approach (EUA), offering versatility in data organization, analysis, and visualization, as well as widespread accessibility. It fosters collaboration and informed decision-making across diverse domains. Conversely, Python is indispensable for professional programming due to its versatility, readability, extensive libraries, and robust community support. It enables efficient development, advanced data analysis, data mining, and automation, catering to diverse industries and applications. However, one primary issue when using Microsoft Excel with Python libraries is compatibility and interoperability. While Excel is a widely used tool for data storage and analysis, it may not seamlessly integrate with Python libraries, leading to challenges in reading and writing data, especially in complex or large datasets. Additionally, manipulating Excel files with Python may not always preserve formatting or formulas accurately, potentially affecting data integrity. Moreover, dependency on Excel’s graphical user interface (GUI) for automation can limit scalability and reproducibility compared to Python’s scripting capabilities. This paper covers the integration solution of empowering non-programmers to leverage Python’s capabilities within the familiar Excel environment. This enables users to perform advanced data analysis and automation tasks without requiring extensive programming knowledge. Based on Soliciting feedback from non-programmers who have tested the integration solution, the case study shows how the solution evaluates the ease of implementation, performance, and compatibility of Python with Excel versions.展开更多
Microsoft.NET主要由Windows.NET、.NET框架、.NET企业服务器、Orchestration和Visual Studio.NET组成。Microsoft.NET框架是一个多语言组件开发和执行环境,由公共语言运行库(CLR Common Language Runtime);统一的编程类;ASP.NET三部分...Microsoft.NET主要由Windows.NET、.NET框架、.NET企业服务器、Orchestration和Visual Studio.NET组成。Microsoft.NET框架是一个多语言组件开发和执行环境,由公共语言运行库(CLR Common Language Runtime);统一的编程类;ASP.NET三部分组成。 Microsoft.NET主要特点。①基于.NET平台的完善软件服务。随着.NET平台的推进,软件将逐渐从产品形式向服务形式转化,软件应用是以Web服务的形式出现并在Internet上发布的,这也是整个IT行业的大势所趋。基于.NET平台的用户、开发人员只需要定制服务。展开更多
文摘Today, an ever increasing number of natural scientists use computers for data analysis, modeling, simulation and visualization of complex problems. However, in the last decade the computer architecture has changed significantly, making it increasingly difficult to fully utilize the power of the processor, unless the scientist is a trained programmer. The reasons for this shift include the change from single-core to multi-core processors, as well as the decreasing price of hardware, which allows researchers to build cluster computers made from commodity hardware. Therefore, scientists must not only be able to handle multi-core processors, but also the problems associated with writing distributed memory programs and handle communication between hundreds of multi-core machines. Fortunately, there are a number of systems to help the scientist e.g. Message Parsing Interface (MPI) [1] for handling communication, DistNumPy [2] for handling data distribution and Communicating Sequential Processes (CSP) [3] for handling concurrency related problems. Having said that, it must be emphasized that all of these methods require that the scientists learn a new method and then rewrite their programs, which mean more work for the scientist. A solution that does not require much work for the scientists is automatic parallelization. However, research dating back three decades has yet to find fully automated parallelization as a feasible solution for programs in general, but some classes of programs can be automatically parallelized to an extent. This paper describes an external library which provides a Parallel. For loop construct, allowing the body of a loop to be run in Parallel across multiple networked machines, i.e. on distributed memory architectures. The individual machines themselves may be shared memory nodes of course. The idea is inspired by Microsoft’s Parallel Library that supplies multiple Parallel constructs. However, unlike Microsoft’s Library our library supports distributed memory architectures. Preliminary tests have shown that simple problems may be distributed easily and achieve good scalability. Unfortunately, the tests show that the scalability is limited by the number of accesses made to shared variables. Thus the applicability of the library is not general but limited to a subset of applications with only limited communication needs.
文摘Microsoft Excel is essential for the End-User Approach (EUA), offering versatility in data organization, analysis, and visualization, as well as widespread accessibility. It fosters collaboration and informed decision-making across diverse domains. Conversely, Python is indispensable for professional programming due to its versatility, readability, extensive libraries, and robust community support. It enables efficient development, advanced data analysis, data mining, and automation, catering to diverse industries and applications. However, one primary issue when using Microsoft Excel with Python libraries is compatibility and interoperability. While Excel is a widely used tool for data storage and analysis, it may not seamlessly integrate with Python libraries, leading to challenges in reading and writing data, especially in complex or large datasets. Additionally, manipulating Excel files with Python may not always preserve formatting or formulas accurately, potentially affecting data integrity. Moreover, dependency on Excel’s graphical user interface (GUI) for automation can limit scalability and reproducibility compared to Python’s scripting capabilities. This paper covers the integration solution of empowering non-programmers to leverage Python’s capabilities within the familiar Excel environment. This enables users to perform advanced data analysis and automation tasks without requiring extensive programming knowledge. Based on Soliciting feedback from non-programmers who have tested the integration solution, the case study shows how the solution evaluates the ease of implementation, performance, and compatibility of Python with Excel versions.
文摘Microsoft.NET主要由Windows.NET、.NET框架、.NET企业服务器、Orchestration和Visual Studio.NET组成。Microsoft.NET框架是一个多语言组件开发和执行环境,由公共语言运行库(CLR Common Language Runtime);统一的编程类;ASP.NET三部分组成。 Microsoft.NET主要特点。①基于.NET平台的完善软件服务。随着.NET平台的推进,软件将逐渐从产品形式向服务形式转化,软件应用是以Web服务的形式出现并在Internet上发布的,这也是整个IT行业的大势所趋。基于.NET平台的用户、开发人员只需要定制服务。
