| 10 from IBM: Kernel testing, C coding, Apache Ant, Python IDEs, Grids . . . |
Jan. 10, 2004
IBM has published the following ten technical articles, tutorials, and downloads on its developerWorks and alphaWorks Websites. They cover a range of interesting (though not necessarily embedded) technical topics. Some require free registration. Enjoy . . .- Putting Linux reliability to the test -- This article documents the test results and analysis of the Linux kernel and other core OS components, including everything from libraries and device drivers to file systems and networking, all under some fairly adverse conditions, and over lengthy durations. The IBM Linux Technology Center has just finished this comprehensive testing over a period of more than three months and shares the results of their LTP (Linux Test Project) testing with developerWorks readers.
- C coding tip: Self-manage data buffer memory -- The C programming language defines two standard memory management functions: malloc() and free(). C programmers frequently use those functions to allocate buffers at run time to pass data between functions. In many situations, however, you cannot predetermine the actual sizes required for the buffers, which may cause several fundamental problems for constructing complex C programs. This article advocates a self-managing, abstract data buffer. It outlines a pseudo-C implementation of the abstract buffer and details the advantages of adopting this mechanism.
- Apache Ant 101: Make Java builds a snap -- Whether you're a veteran user of Apache Ant in need of a refresher or just starting out with this open source Java-based build tool, this tutorial provides a wealth of information. It walks you through the steps involved in writing a build file for a simple Java project, and then looks at some of Ant's other useful functions, including filesystem operations and pattern matching. You'll finish the course by writing our own Java class that extends Ant's functionality.
- Secure programmer: Keep an eye on inputs -- This article discusses various ways data gets into your program, emphasizing how to deal appropriately with them; you might not even know about them all! It first discusses how to design your program to limit the ways data can get into your program, and how your design influences what is an input. It then discusses various input channels and what to do about them, including environment variables, files, file descriptors, the command line, the graphical user interface (GUI), network data, and miscellaneous inputs.
- Charming Python: Review of Python IDEs -- This article looks at four open source development environments for working with Python code on Unix-like operating systems. He evaluates two general-purpose editors/environments and two Python-specific ones, and compares the merits of each.
- Grid watch: Open standards architecture at the GGF -- In the first installment of the "Grid Watch" column, I gave you a brief overview of the Global Grid Forum (GGF). Now I'll turn my attention to grid architecture, a topic I find extremely hard to talk about. It's not that I think architecture is boring or unnecessary. Quite the contrary. It's just a huge, rambling, complex topic, and my job here is to pick out what's important to the developer community without getting too lost in the weeds.
- A look under the hood of Grid data access -- This article looks at the internal mechanisms of Open Grid Services Architecture - Data Access and Integration (OGSA-DAI) and explains how they interact with each other as well as with the user's application. This article provides you with a better understanding of the potential of OGSA-DAI through the exposure of some of the lesser-known features.
- Build a grid app with Python, Part 3: Security -- In a grid environment, security is an issue no matter what type of grid you are producing. This tutorial, the third part in our Python grid series, focuses on the issues surrounding the security within your grid when developing a grid solution with Python. The aim of the entire series is to build a complete grid environment within Python. This tutorial covers the issues surrounding the security of a grid, from the identification of different components within the grid, to the supporting of the authority and authorization of these components in order to perform different tsaks within the grid structure.
- Build a grid app with Python, Part 4: Metadata -- This tutorial, the fourth in a series, covers the methods behind managing information and metadata within a grid. The aim of the series as a whole is to build a complete grid environment within Python, and this tutorial looks at the specific requirements of data and information within your grid. Data and metadata make up a considerable portion of any grid infrastructure, from the basics of the grid instructions and work descriptions through simple information about the grid name, services, and other information. In this tutorial, the author looks at ways to store, exchange, and manipulate data as it progresses through the grid.
- Build a grid app with Python, Part 5: Tracking and management -- This tutorial, the fifth and final part of our Python grid series, looks at the driving force behind any grid -- the management system that distributes information through the grid and makes the grid work. We'll create both a computational grid and a resource grid that build on the facilities of the past tutorials and put all the pieces together for our final system.
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