MiAMI: Multi-core Aware Processor Affinity for TCP/IP over Multiple Network Interfaces

Jang, Hye-Churn; Jin, Hyun-Wook

doi:10.1109/hoti.2009.19

Cited by 21 publications

(15 citation statements)

References 17 publications

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“…We demonstrated that automatic placement of communicating tasks may significantly help performance [12]. Moreover scheduling and placing processes depending on interrupt affinity and processor topology can reduce the CPU overhead in the context of TCP/IP [6]. We feel that these ideas could be combined in a more general-purpose process placement policy for MPI applications.…”

Section: Discussion and Related Workmentioning

confidence: 99%

Dodging Non-uniform I/O Access in Hierarchical Collective Operations for Multicore Clusters

Goglin

Moreaud

2011

2011 IEEE International Symposium on Parallel and Distributed Processing Workshops and PHD Forum

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Section: Discussion and Related Workmentioning

confidence: 99%

Dodging Non-uniform I/O Access in Hierarchical Collective Operations for Multicore Clusters

Goglin

Moreaud

2011

2011 IEEE International Symposium on Parallel and Distributed Processing Workshops and PHD Forum

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“…Multiple network interfaces can provide high network bandwidth and high availability [24,21,45]. Enforcing CPU affinity for interrupts and network processing has been shown to be beneficial for SMP systems [45] and the same benefits should apply to virtualised multicore systems.…”

Section: Scalability Enhancementmentioning

confidence: 99%

“…Many benchmarking studies suggest that each individual core performs differently across the cores of one multiprocessor [24,32,21]. Veal et al [45] and Hashemian et al [21] observe a CPU single core bottleneck and suggest methods to distribute the bottleneck to achieve better performance.…”

Section: Introductionmentioning

confidence: 99%

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Understanding, modelling, and improving the performance of web applications in multicore virtualised environments

Chen

Osman

et al. 2014

Proceedings of the 5th ACM/SPEC International Conference on Performance Engineering

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As the computing industry enters the Cloud era, multicore architectures and virtualisation technologies are replacing traditional IT infrastructures. However, the complex relationship between applications and system resources in multicore virtualised environments is not well understood. Workloads such as web services and on-line financial applications have the requirement of high performance but benchmark analysis suggests that these applications do not optimally benefit from a higher number of cores.In this paper, we try to understand the scalability behaviour of network/CPU intensive applications running on multicore architectures. We begin by benchmarking the Petstore web application, noting the systematic imbalance that arises with respect to per-core workload. Having identified the reason for this phenomenon, we propose a queueing model which, when appropriately parametrised, reflects the trend in our benchmark results for up to 8 cores. Key to our approach is providing a fine-grained model which incorporates the idiosyncrasies of the operating system and the multiple CPU cores. Analysis of the model suggests a straightforward way to mitigate the observed bottleneck, which can be practically realised by the deployment of multiple virtual NICs within our VM. Next we make blind predictions to forecast performance with multiple virtual NICs. The validation results show that the model is able to predict the expected performance with relative errors ranging between 8 and 26%.

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“…Regarding this problem, multirail machines are now commonly considered as a workaround since their multiple NICs scale better with the number of cores. However, such complex architectures, interconnecting numerous hardware components, also raise the need to take affinities and locality into account when scheduling network processing [4].…”

Section: Multicore and Numa Architecturesmentioning

confidence: 99%

Adaptive MPI Multirail Tuning for Non-uniform Input/Output Access

Moreaud

Goglin

Namyst

2010

Recent Advances in the Message Passing Interface

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Abstract. Multicore processors have not only reintroduced Non-Uniform Memory Access (NUMA) architectures in nowadays parallel computers, but they are also responsible for non-uniform access times with respect to Input/Output devices (NUIOA). In clusters of multicore machines equipped with several Network Interfaces, performance of communication between processes thus depends on which cores these processes are scheduled on, and on their distance to the Network Interface Cards involved. We propose a technique allowing multirail communication between processes to carefully distribute data among the network interfaces so as to counterbalance NUIOA effects. We demonstrate the relevance of our approach by evaluating its implementation within OpenMPI on a Myri-10G + InfiniBand cluster.

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MiAMI: Multi-core Aware Processor Affinity for TCP/IP over Multiple Network Interfaces

Cited by 21 publications

References 17 publications

Dodging Non-uniform I/O Access in Hierarchical Collective Operations for Multicore Clusters

Dodging Non-uniform I/O Access in Hierarchical Collective Operations for Multicore Clusters

Understanding, modelling, and improving the performance of web applications in multicore virtualised environments

Adaptive MPI Multirail Tuning for Non-uniform Input/Output Access

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