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首页> 外文期刊>Future generation computer systems >Parallel patterns for heterogeneous CPU/GPU architectures: Structured parallelism from cluster to cloud
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Parallel patterns for heterogeneous CPU/GPU architectures: Structured parallelism from cluster to cloud

机译：异构CPU / GPU架构的并行模式：从集群到云的结构化并行

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摘要

The widespread adoption of traditional heterogeneous systems has substantially improved the computing power available and, in the meantime, raised optimisation issues related to the processing of task streams across both CPU and GPU cores in heterogeneous systems. Similar to the heterogeneous improvement gained in traditional systems, cloud computing has started to add heterogeneity support, typically through GPU instances, to the conventional CPU-based cloud resources. This optimisation of cloud resources will arguably have a real impact when running on-demand computationally-intensive applications. In this work, we investigate the scaling of pattern-based parallel applications from physical, "local" mixed CPU/GPU-clusters to a public cloud CPU/GPU infrastructure. Specifically, such parallel patterns are deployed via algorithmic skeletons to exploit a peculiar parallel behaviour while hiding implementation details. We propose a systematic methodology to exploit approximated analytical performance/cost models, and an integrated programming framework that is suitable for targeting both local and remote resources to support the offloading of computations from structured parallel applications to heterogeneous cloud resources, such that performance values not available on local resources may be actually achieved with the remote resources. The amount of remote resources necessary to achieve a given performance target is calculated through the performance models in order to allow any user to hire the amount of cloud resources needed to achieve a given target performance value. Thus, it is therefore expected that such models can be used to devise the optimal proportion of computations to be allocated on different remote nodes for Big Data computations. We present different experiments run with a proof-of-concept implementation based on FastFlow on small departmental clusters as well as on a public cloud infrastructure with CPU and GPU using the Amazon Elastic Compute Cloud. In particular, we show how CPU-only and mixed CPU/GPU computations can be offloaded to remote cloud resources with predictable performances and how data intensive applications can be mapped to a mix of local and remote resources to guarantee optimal performances.
机译：传统异构系统的广泛采用已大大提高了可用的计算能力，与此同时，提出了与异构系统中CPU和GPU内核之间的任务流处理相关的优化问题。与传统系统中获得的异构改进相似，云计算已开始通过常规的基于GPU的实例向传统的基于CPU的云资源添加异构支持。当运行按需计算密集型应用程序时，这种对云资源的优化可能会产生真正的影响。在这项工作中，我们研究了基于模式的并行应用程序的扩展，从物理的“本地”混合CPU / GPU集群到公共云CPU / GPU基础架构。具体而言，此类并行模式是通过算法框架部署的，以利用特殊的并行行为，同时隐藏实现细节。我们提出了一种系统方法论，以利用近似的分析性能/成本模型，以及一个集成的编程框架，该框架适合于同时针对本地和远程资源，以支持将计算从结构化并行应用程序卸载到异构云资源，从而无法获得性能值在本地资源上使用远程资源实际上可以实现。通过性能模型计算实现给定性能目标所需的远程资源量，以允许任何用户租用达到给定目标性能值所需的云资源量。因此，因此期望可以使用此类模型来设计要在大数据计算的不同远程节点上分配的最佳计算比例。我们展示了在小型部门集群以及使用Amazon Elastic Compute Cloud的具有CPU和GPU的公共云基础架构上，基于概念流实施的不同实验，该实验基于FastFlow。特别是，我们展示了如何将纯CPU和CPU / GPU混合计算卸载到具有可预测性能的远程云资源，以及如何将数据密集型应用程序映射到本地和远程资源的混合以确保最佳性能。

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