Enhanced memory management for scalable MPI intra-node communication on many-core processor

Cho, Joong-Yeon; Jin, Hyun-Wook; Nam, Dukyun

doi:10.1145/3127024.3127035

Cited by 5 publications

(1 citation statement)

References 11 publications

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“…The HBM-DRAM system was configured in flat (as an additional NUMA node), cache and hybrid mode. If the intra-node communicator includes a sufficient number of processes or if the buffer size requested is large enough, Enhanced memory management schemes for HBM based on MCDRAM presented in [37] allocate user and shared buffers to the high-bandwidth on-package memory. In addition to this, they reduce the overheads of memory mapping.…”

Section: F High Bandwidth Memorymentioning

confidence: 99%

Survey of Memory Management Techniques for HPC and Cloud Computing

Pupykina

Agosta

2019

IEEE Access

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The emergence of new classes of HPC applications and usage models, such as real-time HPC and cloud HPC, coupled with the increasingly heterogeneous nature of HPC architectures, requires a renewed investigation of memory management solutions. Traditionally, memory is shared by an operating system using segmentation and paging techniques. At the same time, new classes of applications require Quality of Service (QoS) guarantees. As such, the typical practice of reserving a subset of the supercomputer to a single application becomes less attractive, leading to the exploration of cloud technologies. In this context, a viable scenario is that of multiple applications, with different QoS levels, coexisting on the same deeply heterogeneous HPC infrastructure and sharing resources. However, for this scenario to succeed in practice, resources, including memory, need to be allocated with a vision that includes both the application requirements and the current and future state of the overall system. In this survey, challenges of memory management in HPC and Cloud Computing, different memory management systems and optimisation techniques to increase memory utilisation are discussed in detail. INDEX TERMS Clouds, high performance computing, memory management, resource management. ANNA PUPYKINA received the higher education degree in data processing and control systems engineering from Samara State Aerospace University, Russia, in 2009. She is currently pursuing the Ph.D. degree with the Politecnico di Milano, under the supervision of Prof. G. Agosta. She was with Russian State Humanitarian University (RSUH), Togliatti, and also with the Department of Applied Mathematics and Informatics, Togliatti State University (TSU). Her main research interests include runtime management algorithms and programming model extensions for deeply heterogeneous systems. GIOVANNI AGOSTA received the Laurea degree in computer engineering and the Ph.D. degree in information technology from the Politecnico di Milano, Italy, in 2000 and 2004, respectively. He is currently a tenured Researcher. He has authored more than 80 articles in international journals, conferences, and workshops. His main research interest includes compiler technology. He was a recipient of four HiPEAC Awards and three Best Paper Awards. He has been involved in eight European funded projects, since 2010, holding workpackage and task leader responsibilities. He is a Full Member of the HiPEAC Network and the Organizer of two workshops colocated with the HiPEAC Conference.

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Section: F High Bandwidth Memorymentioning

confidence: 99%

Survey of Memory Management Techniques for HPC and Cloud Computing

Pupykina

Agosta

2019

IEEE Access

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Optimizing point‐to‐point communication between adaptive MPI endpoints in shared memory

White

Kalé

2018

Concurrency and Computation

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Adaptive MPI is an implementation of the MPI standard that supports the virtualization of ranks as user-level threads, rather than OS processes. In this work, we optimize the communication performance of AMPI based on the locality of the endpoints communicating within a cluster of SMP nodes. We differentiate between point-to-point messages with both endpoints co-located on the same execution unit and point-to-point messages with both endpoints residing in the same process but not on the same execution unit. We demonstrate how the messaging semantics of Charm++ enable and hinder AMPI's implementation in different ways, and motivate extensions to Charm++ to address the limitations. Using the OSU microbenchmark suite, we show that our locality-aware design offers lower latency, higher bandwidth, and reduced memory footprint for applications.

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