A Memory Congestion-Aware MPI Process Placement for Modern NUMA Systems

Agung, Mulya; Amrizal, Muhammad Alfian; Komatsu, Kazuhiko; Egawa, Ryusuke; Takizawa, Hiroyuki

doi:10.1109/hipc.2017.00026

Cited by 5 publications

(8 citation statements)

References 20 publications

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“…It represents the baseline of our experiments. Static-best mapping is obtained by using an offline-based method proposed in our previous work [2]. It first collects communication traces by preliminary running the target application.…”

Section: Methodsmentioning

confidence: 99%

“…However, only considering the locality is not sufficient to improve performance on modern NUMA systems. Furthermore, maximizing the locality can degrade performance because it potentially increases the memory congestion [2,16].…”

Section: Introductionmentioning

confidence: 99%

“…Various MPI process mapping methods have been proposed in the related studies. Most of the methods rely on offline profiling to trace communication between processes and to analyze the communication behaviors of the applications [2,9,21,31]. The main drawback of these methods is the requirement of offline profiling, which has a high overhead and is potentially timeconsuming.…”

Section: Introductionmentioning

confidence: 99%

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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: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Agung

Amrizal

Egawa

et al. 2020

JSFI

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“…Improving the locality of communications is important because it will reduce the remote-access penalty and congestion on interconnects. However, recent work [2], [14] and our previous work [15] have shown that on modern NUMA systems, maximizing locality does not always minimize the execution time. On the contrary, locality-based mapping might reduce the performance of applications due to the memory congestion.…”

Section: Introductionmentioning

confidence: 99%

“…• A generalized task mapping method that detects implicit communications among tasks in shared-memory parallel processing. The preliminary study of this work focuses only on MPI process mapping [15].…”

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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An effective scheme for memory congestion reduction in multi-core environment

Upadhyay

Singh

2022

Journal of King Saud University - Computer and Information Scie

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A Memory Congestion-Aware MPI Process Placement for Modern NUMA Systems

Cited by 5 publications

References 20 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

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

An effective scheme for memory congestion reduction in multi-core environment

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