Towards Parallel Programming Models for Predictability
Towards Parallel Programming Models for Predictability
复制标题
走向可预测性的并行编程模型
DOI：
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发表时间：
2012
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通讯作者：
B. Lisper
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作者：
B. Lisper
用于性能需求应用程序的未来嵌入式系统将非常平行。高性能任务将是在多个内核上运行的并行程序，而不是在单个内核上运行的单个线程。对于硬实时应用程序，必须对此类任务进行WCET。基于并发线程的低级平行编程模型由于其固有的非确定性而难以使用。因此，平行处理社区
长期以来考虑了高级平行编程模型，该模型限制了低级模型以恢复确定性。在这个立场上，我们认为这种并行编程模型也对并行程序的WCET分析也有益。我们回顾一些提出的模型，并讨论它们对定时可预测性的影响。特别是我们将数据并行编程确定为合适的范式，因为它是确定性的，并允许WCET的当前方法
分析将扩展到并行代码。 GPU越来越多地用于高性能应用：我们讨论当前的GPU体系结构，我们认为它提供了平行的平台
对于计算密集型应用程序，似乎可以构建精确的计时模型。因此，未来的有希望的途径是为在GPU上运行的数据并行软件开发WCET分析。
Future embedded systems for performance-demanding applications will be massively parallel. High performance tasks will be parallel programs, running on several cores, rather than single threads running on single cores. For hard real-time applications, WCETs for such tasks must be bounded. Low-level parallel programming models, based on concurrent threads, are notoriously hard to use due to their inherent nondeterminism. Therefore the parallel processing community
has long considered high-level parallel programming models, which restrict the low-level models to regain determinism. In this position paper we argue that such parallel programming models are beneficial also for WCET analysis of parallel programs. We review some proposed models, and discuss their influence on timing predictability. In particular we identify data parallel programming as a suitable paradigm as it is deterministic and allows current methods for WCET
analysis to be extended to parallel code. GPUs are increasingly used for high performance applications: we discuss a current GPU architecture, and we argue that it offers a parallel platform
for compute-intensive applications for which it seems possible to construct precise timing models. Thus, a promising route for the future is to develop WCET analyses for data-parallel software running on GPUs.