Hadoop 中的 JoGL？ Hadoop 用于图形处理？
阅读此和这篇论文，我决定要为大型应用实现分布式体渲染设置MapReduce 上的数据集作为我的本科论文工作。 Hadoop 是一个合理的选择吗？ Java 是否会扼杀一些性能提升或使与 CUDA 的集成变得困难？ Phoenix++ 会是一个更好的工具吗？
After reading this and this paper, I decided I want to implement a distributed volume rendering setup for large datasets on MapReduce as my undergraduate thesis work. Is Hadoop a reasonable choice? Wouldn't it being Java kill some performance gains or make difficult the integration with CUDA? Would Phoenix++ be a better tool for the job?
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Hadoop 还有一个名为 Hadoop Pipes 的 C++ API。 Pipes 允许您用 C++ 编写 Map 和 Reduce 代码，从而与您可用的任何 C/C++ 库进行交互。这可以让您与 CUDA 进行交互，这是有道理的。
据我了解，这只是 MapReduce 的重写，因此所有网络通信和分布式文件系统仍然由 Java 处理。 Hadoop 的目的是使任务的并行化变得简单和通用，因此它无法成为最有效的 MapReduce 实现。您对效率与可用程序员时间的要求可能是使用 Hadoop 或更高效的低级框架的决定因素。
管道中的字数统计示例。不幸的是，确实缺乏文档，但是拥有可用的源代码会使事情变得容易得多。
Hadoop also has a C++ API called Hadoop Pipes. Pipes allows you to write Map and Reduce code in C++, and thus interface with any C/C++ libraries you have available. It makes sense that this could enable you to interface with CUDA.
To my understanding, it is only a rewriting of MapReduce, thus all of the network communication and the distributed filesystem would still be handled by Java. Hadoop is intended to make parallelization of tasks simple and general, and as such it is unable to be the most efficient MapReduce implementation. Your requirements for efficiency versus available programmer time will probably be the deciding factor in using Hadoop or a more efficient, low-level framework.
Word Count in Pipes example. There is a real lack of documentation, unfortunately, but having the source available makes things a lot easier.