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Optimization of the hop-byte metric for effective topology aware mapping

机译:优化跳跃字节度量以实现有效的拓扑感知映射

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Suitable mapping of processes to the nodes of a massively parallel machine can substantially improve communication performance by reducing network congestion. The hop-byte metric has been used as a measure of the quality of such a mapping by several recent works. Optimizing this metric is NP hard, and thus heuristics are applied. However, the heuristics proposed so far do not directly try to optimize this metric. Rather, they use some intuitive methods for reducing congestion and use the metric just to evaluate the quality of the mapping. In fact, heuristics intending to optimize other metrics too don't directly optimize for them, but, rather, use the metric to evaluate the results of the heuristic. In contrast, we pose the mapping problem with the hop-byte metric as a quadratic assignment problem and use a heuristic to directly optimize for this metric. We evaluate our approach on realistic node allocations obtained on the Kraken system at NICS. Our approach yields values for the metric that are up to 75% lower than the default mapping and 66 % lower than existing heuristics. However, the time taken to produce the mapping can be substantially more, which makes this suitable for somewhat static, though possibly irregular, communication patterns. We introduce new heuristics that reduce the time taken to be comparable to that of existing fast heuristics, while still producing mappings of higher quality than existing ones. We also use theoretical lower bounds to suggest that our mapping may be close to optimal, at least for medium sized problems. Consequently, our work can also provide insight into the tradeoff between mapping quality and time taken by other mapping heuristics.
机译:适当地将进程映射到大型并行机的节点可以通过减少网络拥塞来显着提高通信性能。最近的一些著作已将跳字节度量标准用作这种映射质量的度量。优化此指标非常困难,因此应用了启发式方法。但是,到目前为止提出的启发式方法并未直接尝试优化此指标。相反,他们使用一些直观的方法来减少拥塞,并仅使用度量标准来评估映射的质量。实际上,打算优化其他指标的启发式方法也不会直接针对它们优化,而是使用该指标来评估启发式方法的结果。相比之下,我们将跳数字节度量的映射问题提出为二次分配问题,并使用启发式方法直接针对该度量进行优化。我们评估了在NICS的Kraken系统上获得的实际节点分配的方法。我们的方法所产生的指标值比默认映射值低75%,比现有启发式方法低66%。然而,产生映射所花费的时间可能实质上更多,这使得它适合于某种程度上是静态的,尽管可能是不规则的通信模式。我们引入了新的启发式方法,可以将所需的时间减少到与现有的快速启发式方法相当的时间,同时仍会产生比现有的启发式方法更高质量的映射。我们还使用理论下界来表明我们的映射可能接近最佳,至少对于中等规模的问题而言。因此,我们的工作还可以提供有关映射质量与其他映射启发式方法所花费时间之间的权衡的见解。

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