Teaching MPI from Mental Models

V. Eijkhout
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引用次数: 5

Abstract

The Message Passing Interface (MPI) is the de facto standard for programming large scale parallelism, with up to millions of individual processes. Its dominant paradigm of Single Program Multiple Data (SPMD) programming is different from threaded and multicore parallelism, to an extent that students have a hard time switching models. In contrast to threaded programming, which allows for a view of the execution with central control and a central repository of data, SPMD programming has a symmetric model where all processes are active all the time, and none is priviliged in any sense, and where data is distributed.This model is counterintuitive to the novice parallel programmer, so care needs to be taken how to instill the proper ‘mental model'.We identify problems with the currently common way of teaching MPI, and propose a way that is geared to explicit reinforcing the symmetric model. Additionally, we teach starting from realistic scenarios, rather than writing artificial code just to exercise a newly-learned routine.This motivation implies that we reverse the commonly used order of presenting MPI routines, starting with collectives, and later introducing point-to-point routines only as support for certain symmetric operations, avoiding the process-to-process model.
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从心理模型教授MPI
消息传递接口(Message Passing Interface, MPI)是具有多达数百万个单独进程的大规模并行性编程的事实上的标准。其主要的单程序多数据(SPMD)编程范式不同于线程和多核并行,以至于学生很难切换模型。线程编程允许通过中央控制和中央数据存储库查看执行情况,与此相反,SPMD编程具有对称模型,其中所有进程始终处于活动状态,没有任何特权,并且数据是分布式的。对于新手并行程序员来说,这个模型是违反直觉的,所以需要注意如何灌输正确的“心智模型”。我们发现了目前常见的MPI教学方法存在的问题,并提出了一种适合于显式强化对称模型的方法。此外,我们从现实的场景开始教学,而不是编写人工代码只是为了练习新学的例程。这个动机意味着我们颠倒了通常使用的呈现MPI例程的顺序,从集合开始,然后引入点对点例程,仅作为对某些对称操作的支持,避免了进程对进程模型。
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