Communication-aware process and thread mapping using online communication detection

Diener, Matthias; Cruz, Eduardo H. M.; Navaux, Philippe O. A.; Busse, Anselm; Heiíß, Hans-Ulrich

doi:10.1016/j.parco.2015.01.005

Cited by 17 publications

(12 citation statements)

References 50 publications

(71 reference statements)

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“…Performance results on Xeon2, normalized to the baseline The online-based mapping method used for the evaluation is CDSM-mod, which is a modified version of CDSM. We modify CDSM because our experimental results show that it significantly degrades the performance of all the tested applications, which are contrast with the results shown in the related work [13]. Figure 5(a) shows the performance results of CDSM, compared with the baseline and a random mapping method.…”

Section: Methodsmentioning

confidence: 91%

“…An online communication detection method, called CDSM, has been proposed in a related study [13]. It works on the operating system level during the execution of the parallel application.…”

Section: Related Workmentioning

confidence: 99%

“…Each processor consists of one or several groups of processor cores, each of which is physically associated with one or more memory controllers and memory devices. Such a group of processor cores is referred to as a NUMA node [13,16,25]. Although the NUMA nodes are generally connected by high-speed interconnect links such as QuickPath Interconnect (QPI) [32] and HyperTransport [25], accessing a remote NUMA node still needs a longer latency than accessing the data of the local memory device.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Online MPI Process Mapping for Coordinating Locality and Memory Congestion on NUMA Systems

Agung

Amrizal

Egawa

et al. 2020

JSFI

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Mapping MPI processes to processor cores, called process mapping, is crucial to achieving the scalable performance on multi-core processors. By analyzing the communication behavior among MPI processes, process mapping can improve the communication locality, and thus reduce the overall communication cost. However, on modern non-uniform memory access (NUMA) systems, the memory congestion problem could degrade performance more severely than the locality problem because heavy congestion on shared caches and memory controllers could cause long latencies. Most of the existing work focus only on improving the locality or rely on offline profiling to analyze the communication behavior. We propose a process mapping method that dynamically performs the process mapping for adapting to communication behaviors while coordinating the locality and memory congestion. Our method works online during the execution of an MPI application. It does not require modifications to the application, previous knowledge of the communication behavior, or changes to the hardware and operating system. Experimental results show that our method can achieve performance and energy efficiency close to the best static mapping method with low overhead to the application execution. In experiments with the NAS parallel benchmarks on a NUMA system, the performance and total energy improvements are up to 34% (18.5% on average) and 28.9% (13.6% on average), respectively. In experiments with two GROMACS applications on a larger NUMA system, the average improvements in performance and total energy consumption are 21.6% and 12.6%, respectively.

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Section: Methodsmentioning

confidence: 91%

“…An online communication detection method, called CDSM, has been proposed in a related study [13]. It works on the operating system level during the execution of the parallel application.…”

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Online MPI Process Mapping for Coordinating Locality and Memory Congestion on NUMA Systems

Agung

Amrizal

Egawa

et al. 2020

JSFI

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“…Several works also tried to do online pro ling, that is, to collect information during the execution and to adapt thread or memory page mapping based on this information [9,13,15,18,22]. To the best of our knowledge, only a few of them also tackle the issue of characterizing the need for placement optimization [13].…”

Section: Related Workmentioning

confidence: 99%

Refactoring and Optimizing WRF Model on Sunway TaihuLight

Song

Chan

et al. 2019

Proceedings of the 48th International Conference on Parallel Processing

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Nowadays, NUMA architectures are common in compute-intensive systems. Achieving high performance for multi-threaded application requires both a careful placement of threads on computing units and a thorough allocation of data in memory. Finding such a placement is a hard problem to solve, because performance depends on complex interactions in several layers of the memory hierarchy. In this paper we propose a black-box approach to decide if an application execution time can be impacted by the placement of its threads and data, and in such a case, to choose the best placement strategy to adopt. We show that it is possible to reach near-optimal placement policy selection. Furthermore, solutions work across several recent processor architectures and decisions can be taken with a single run of low overhead pro ling.

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“…As the communication performance varies greatly on NUMA systems, use of the communication behavior to optimize the task mapping is important in increasing performance. These task mapping methods are called communication-aware task mapping [6]- [8].…”

Section: Introductionmentioning

confidence: 99%

DeLoc: A Locality and Memory-Congestion-Aware Task Mapping Method for Modern NUMA Systems

et al. 2020

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The mapping of tasks to processor cores, called task mapping, is crucial to achieving scalable performance on multicore processors. On modern NUMA (non-uniform memory access) systems, the memory congestion problem could degrade the performance more severely than the data locality problem because heavy congestion on shared caches and memory controllers could cause long latencies. Conventional work on task mapping mostly focuses on improving the locality of memory accesses. However, our previous work showed that on modern NUMA systems, maximizing the locality can degrade the performance due to memory congestion. In this work, we propose a task mapping method that addresses the locality and the memory congestion problems to improve the performance of parallel applications. In the proposed method, first, the spatial and temporal communication behaviors of the applications are analyzed from the timeseries dataset of communications among the parallel tasks. Then, a data clustering technique is employed to detect groups of tasks that potentially cause the memory congestion. Finally, this information is used to compute the task mapping to improve the locality and reduce the memory congestion. We also provide a set of metrics to describe the communication behaviors and to evaluate if the target application can benefit from our method. The proposed method is evaluated with the NPB and PARSEC applications on a real NUMA system and a multicore simulator. A detailed analysis of the sources of performance gain is also provided. Experimental results show that our method can achieve up to a 61% performance improvement compared with the state-of-the-art locality-based method.

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Communication-aware process and thread mapping using online communication detection

Cited by 17 publications

References 50 publications

Online MPI Process Mapping for Coordinating Locality and Memory Congestion on NUMA Systems

Online MPI Process Mapping for Coordinating Locality and Memory Congestion on NUMA Systems

Refactoring and Optimizing WRF Model on Sunway TaihuLight

DeLoc: A Locality and Memory-Congestion-Aware Task Mapping Method for Modern NUMA Systems

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