Many embedded applications such as multimedia, signal processing and wireless communications present a streaming processing behavior. In order to take full advantage of modern multi-and many-core embedded platforms, t...
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ISBN:
(纸本)9781479989379
Many embedded applications such as multimedia, signal processing and wireless communications present a streaming processing behavior. In order to take full advantage of modern multi-and many-core embedded platforms, these applications have to be parallelized by describing them in a given parallel Model of Computation (MoC). One of the most prominent MoCs is Kahn Process Network (KPN) as it allows to express multiple forms of parallelism and it is suitable for efficient mapping and scheduling onto parallel embedded platforms. However, describing streaming applications manually in a KPN is a challenging task. Especially, since they spend most of their execution time in loops with unbounded number of iterations. These loops are in several cases implemented as while loops, which are difficult to analyze. In this paper, we present an approach to guide the derivation of KPNs from embedded streaming applications dominated by multiple types of while loops. We evaluate the applicability of our approach on an eight DSP core commercial embedded platform using realistic benchmarks. Results measured on the platform showed that we are able to speedup sequential benchmarks on average by a factor up to 4.3x and in the best case up to 7.7x. Additionally, to evaluate the effectiveness of our approach, we compared it against a state-of-the-art parallelization framework.
High Performance Computing Clusters are designed to host highly parallelized applications, often in excess of thousands of nodes allocated to a job. These jobs, especially those that require a high level of synchronou...
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ISBN:
(纸本)9781479955480
High Performance Computing Clusters are designed to host highly parallelized applications, often in excess of thousands of nodes allocated to a job. These jobs, especially those that require a high level of synchronous communication, can be greatly affected by a single poor, or even sub-standard performing component. These components, often referred to as a node, are typically comprised of CPUs, accelerator processors, memory, a communication bus, and so on. Consequently it is important to identify and eliminate these sub-standard performing nodes before a job is scheduled onto them. In this paper we will describe the process used to measure and the methodology used to quantify poor performing nodes or classify suspect performing nodes into groups, or clusters, that can be later used to identify future performance issues. This process is more involved than simply running a scientific calculation across all the nodes, finding one that was "slow", and labeling it as a bad node. At Los Alamos, this methodology has been used successfully to find problem nodes and has helped characterize the components of other clusters to aid in the proactive elimination of potential problems.
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