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7th ACM International Conference on Computing Frontiers (CF)

作者： Dinan, James Balaji, Pavan Lusk, Ewing Sadayappan, P. Thakur, Rajeev Ohio State Univ Dept Comp Sci & Engn 2015 Neil Ave Columbus OH 43210 USA Argonne Natl Lab Div Math & Comp Sci Argonne IL 60439 USA

ISBN: (纸本)9781450300445

The Message Passing Interface (MPI) is one of the most widely used programming models for parallel computing. However, the amount of memory available to an MPI process is limited by the amount of local memory within a compute node. Partitioned Global Address Space (PGAS) models such as Unified parallel C (UPC) are growing in popularity because of their ability to provide a shared global address space that spans the memories of multiple compute nodes. However, taking advantage of UPC can require a large recoding effort for existing parallel applications. In this paper, we explore a new hybrid parallel programming model that combines MPI and UPC. This model allows MPI programmers incremental access to a greater amount of memory, enabling memory-constrained MPI codes to process larger data sets. In addition, the hybrid model offers UPC programmers an opportunity to create static UPC groups that are connected overMPI. As we demonstrate, the use of such groups can significantly improve the scalability of locality-constrained UPC codes. This paper presents a detailed description of the hybrid model and demonstrates its effectiveness in two applications: a random access benchmark and the Barnes-Hut cosmological simulation. Experimental results indicate that the hybrid model can greatly enhance performance;using hybrid UPC groups that span two cluster nodes, RA performance increases by a factor of 1.33 and using groups that span four cluster nodes, Barnes-Hut experiences a twofold speedup at the expense of a 2% increase in code size.

关键词： MPI UPC PGAS hybrid parallel programming

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MPI + MPI: a new hybrid approach to parallel programming with MPI plus shared memory

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COMPUTING 2013年第12期95卷 1121-1136页

作者： Hoefler, Torsten Dinan, James Buntinas, Darius Balaji, Pavan Barrett, Brian Brightwell, Ron Gropp, William Kale, Vivek Thakur, Rajeev ETH Zurich Switzerland Argonne Natl Lab Argonne IL 60439 USA Sandia Natl Labs Sandia NM USA Univ Illinois Urbana IL 61801 USA

hybrid parallel programming with the message passing interface (MPI) for internode communication in conjunction with a shared-memory programming model to manage intranode parallelism has become a dominant approach to scalable parallel programming. While this model provides a great deal of flexibility and performance potential, it saddles programmers with the complexity of utilizing two parallel programming systems in the same application. We introduce an MPI-integrated shared-memory programming model that is incorporated into MPI through a small extension to the one-sided communication interface. We discuss the integration of this interface with the MPI 3.0 one-sided semantics and describe solutions for providing portable and efficient data sharing, atomic operations, and memory consistency. We describe an implementation of the new interface in the MPICH2 and Open MPI implementations and demonstrate an average performance improvement of 40 % to the communication component of a five-point stencil solver.

关键词： MPI-3.0 Shared memory hybrid parallel programming

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hybrid MPI plus UPC parallel programming paradigm on an SMP cluster

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TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES 2012年第SUPPL.2期20卷 1389-1407页

作者： Bozkus, Zeki Kadir Has Univ Dept Comp Engn TR-34083 Istanbul Turkey

The symmetric multiprocessing (SMP) cluster system, which consists of shared memory nodes with several multicore central processing units connected to a high-speed network to form a distributed memory system, is the most widely available hardware architecture for the high-performance computing community. Today, the Message Passing Interface (MPI) is the most widely used parallel programming paradigm for SMP clusters, in which the MPI provides programming both for an SMP node and among nodes simultaneously. However, Unified parallel C (UPC) is an emerging alternative that supports the partitioned global address space model that can be again employed within and across the nodes of a cluster. In this paper, we describe a hybrid parallel programming paradigm that was designed to combine MPI and UPC programming models. This paradigm's objective is to mix the MPI's data locality control and scalability strengths with UPC's fine-grain parallelism and ease of programming to achieve multiple-level parallelism at the SMP cluster, which itself has multilevel parallel architecture. Utilizing a proposed hybrid model and comparing MPI-only to UPC-only implementations, this paper presents a detailed description of Cannon's algorithm benchmark application with performance results of a random-access benchmark and the Barnes-Hut N-Body simulation. Experiments indicate that the hybrid MPI+UPC model can significantly provide performance increases of up to double in comparison with UPC-only implementation, and up to 20% increases in comparison to MPI-only implementation. Furthermore, an optimization was achieved that improved the hybrid performance by an additional 20%.

关键词： hybrid parallel programming UPC MPI

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Communication and optimization aspects of parallel programming models on hybrid architectures

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INTERNATIONAL JOURNAL OF HIGH PERFORMANCE COMPUTING APPLICATIONS 2003年第1期17卷 49-62页

作者： Rabenseifner, R Wellein, G Univ Stuttgart D-7000 Stuttgart Germany Reg Rechenzentrum Erlangen Erlangen Germany

Most HPC systems are clusters of shared memory nodes. parallel programming must combine the distributed memory parallelization on the node interconnect with the shared memory parallelization inside each node. The hybrid MPI+OpenMP programming model is compared with pure MPI, compiler based parallelization, and other parallel programming models on hybrid architectures. The paper focuses on bandwidth and latency aspects, and also on whether programming paradigms can separate the optimization of communication and computation. Benchmark results are presented for hybrid and pure MPI communication. This paper analyzes the strengths and weaknesses of several parallel programming models on clusters of SMP nodes.

关键词： OpenMP MPI hybrid parallel programming threads and MPI HPC

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Automated Models for Predicting Software Defects in hybrid Message Passing Interface (MPI) and Open Multi-Processing (OpenMP) parallel Programs Using Deep Learning

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IEEE ACCESS 2025年 13卷 65939-65954页

作者： Althiban, Amani Saad Alharbi, Hajar M. Al Khuzayem, Lama A. Eassa, Fathy Elbouraey King Abdulaziz Univ Fac Comp & Informat Technol Dept Comp Sci Jeddah 21589 Saudi Arabia

hybrid Message Passing Interface (MPI) and Open Multi-Processing (OpenMP) parallel programs are pivotal for scalability and efficiency in high-performance computing (HPC), especially as systems approach exa-scale operations. These programs leverage distributed and shared memory systems, making them indispensable for scientific simulations, data analysis, and numerical computations. However, synchronization defects such as deadlocks and race conditions pose significant challenges to reliability and performance, often eluding traditional static and dynamic analysis tools due to the complexity of hybrid systems. This study introduces a deep learning-based models for automated defect prediction in hybrid MPI and OpenMP programs. Using a balanced dataset of 1,500 C++ files, three neural architectures-Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and a hybrid CNN-LSTM model-were evaluated. To preprocess the code, Abstract Syntax Tree (AST)-based token extraction was employed, capturing structural and semantic relationships critical for detecting defects. Token extraction methods included detailed C++ syntax analysis via the Clang library and a custom regular expression approach. The results reveal that Clang-token-based representation provided the most effective input for defect prediction, enabling CNN models to achieve an accuracy of 97%. By integrating AST-based structural insights with the predictive power of deep learning, this research offers a scalable solution for enhancing the reliability of hybrid parallel programs. The findings establish a foundation for future advancements in automated defect detection, addressing the pressing needs of high-performance computing systems.

关键词： hybrid parallel programming MPI OpenMP OpenMP deep learning deep learning software defect prediction software defect prediction race conditions race conditions deadlocks deadlocks deadlocks

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15+years of joint parallel application performance analysis/tools training with Scalasca/Score-P and Paraver/Extrae toolsets

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2025年 162卷

作者： Wylie, Brian J. N. Gimenez, Judit Feld, Christian Geimer, Markus Llort, German Mendez, Sandra Mercadal, Estanislao Visser, Anke Garcia-Gasulla, Marta Forschungszentrum Julich Julich Supercomp Ctr D-52425 Julich NRW Germany Barcelona Supercomp Ctr Placa Eusebi Guell 1-3 Barcelona 08034 Catalunya Spain Univ Politecn Catalunya BarcelonaTech Carrer de Jordi Girona 31 Barcelona 08034 Catalunya Spain

The diverse landscape of distributed heterogeneous computer systems currently available and being created to address computational challenges with the highest performance requirements presents daunting complexity for application developers. They must effectively decompose and distribute their application functionality and data, efficiently orchestrating the associated communication and synchronisation, on multi/manycore CPU processors with multiple attached acceleration devices structured within compute nodes with interconnection networks of various topologies. Sophisticated compilers, runtime systems and libraries are (loosely) matched with debugging, performance measurement and analysis tools, with proprietary versions by integrators/vendors provided exclusively for their systems complemented by portable (primarily) open-source equivalents developed and supported by the international research community over many years. The Scalasca and Paraver toolsets are two widely employed examples of the latter, installed on personal notebook computers through to the largest leadership HPC systems. Over more than fifteen years their developers have worked closely together in numerous collaborative projects culminating in the creation of a universal parallel performance assessment and optimisation methodology focused on application execution efficiency and scalability, and the associated training and coaching of application developers (often in teams) in its productive use, reviewed in this article with lessons learnt therefrom.

关键词： hybrid parallel programming MPI message-passing OpenMP multithreading OpenACC device offload acceleration HPC application execution performance measurement & analysis Performance assessment & optimisation methodology & tools Hands-on training & coaching

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Enabling communication concurrency through flexible MPI endpoints

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INTERNATIONAL JOURNAL OF HIGH PERFORMANCE COMPUTING APPLICATIONS 2014年第4期28卷 390-405页

作者： Dinan, James Grant, Ryan E. Balaji, Pavan Goodell, David Miller, Douglas Snir, Marc Thakur, Rajeev Intel Corp Hudson MA 01749 USA Sandia Natl Labs Scalable Software Syst Grp Albuquerque NM 87185 USA Argonne Natl Lab Lemont IL USA Cisco Syst Inc San Jose CA USA IBM Corp Rochester MN USA Argonne Natl Lab Math & Comp Sci Div Lemont IL USA

MPI defines a one-to-one relationship between MPI processes and ranks. This model captures many use cases effectively;however, it also limits communication concurrency and interoperability between MPI and programming models that utilize threads. This paper describes the MPI endpoints extension, which relaxes the longstanding one-to-one relationship between MPI processes and ranks. Using endpoints, an MPI implementation can map separate communication contexts to threads, allowing them to drive communication independently. Endpoints also enable threads to be addressable in MPI operations, enhancing interoperability between MPI and other programming models. These characteristics are illustrated through several examples and an empirical study that contrasts current multithreaded communication performance with the need for high degrees of communication concurrency to achieve peak communication performance.

关键词： MPI endpoints hybrid parallel programming interoperability communication concurrency

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A High Performance parallel and Heterogeneous Approach to Narrowband Beamforming

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IEEE TRANSACTIONS ON parallel AND DISTRIBUTED SYSTEMS 2016年第8期27卷 2196-2207页

作者： Sarofeen, Christian Gillett, Philip Naval Surface Warfare Ctr Computat Anal & Design Carderock Div West Bethesda MD 20817 USA Naval Surface Warfare Ctr Hydroacoust & Propulsor Dev Carderock Div West Bethesda MD 20817 USA

This paper describes a high performing, hybrid parallel, and heterogeneous algorithmic approach to narrowband Delay-Sum Beamforming (DSB) in the frequency domain using a Just-In-Time Asynchronous Data Method (JIT-ADM) parallel pattern. JIT-ADM is a novel asynchronous parallel programming pattern that unifies various levels of asynchronous concurrency available with distributed heterogeneous computing. The computational performance of this DSB algorithm was analyzed on a 50 node Cray XC30 with a single 10-core Intel Xeon E5-2670 v2 and NVIDIA Tesla K20X general purpose Graphics Processing Unit (GPU) on each node. The algorithm exhibits well behaved weak scalability with 92.7 percent parallel efficiency at 50 nodes compared to maximum performance observed. It is also shown that the algorithm efficiently utilizes a large portion of the available hardware. During beamforming the GPU is utilized at 51.8 percent of its maximum double precision floating point throughput whereas a comparable Central Processing Unit (CPU) version utilizes 60.0 percent of its maximum expected floating point throughput. Across the weak scalability study, utilizing GPUs for processing, a 2-5x performance gain is achieved compared to using CPUs. A brief derivation and validation of the implemented DSB is also presented.

关键词： Beamforming delay-sum beamforming distributed computing heterogeneous computing hybrid parallel programming

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PERFORMANCE EVALUATION OF programming MODELS FOR SMP-BASED CLUSTERS

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JOURNAL OF THE CHINESE INSTITUTE OF ENGINEERS 2008年第7期31卷 1181-1188页

作者： Lee, Myungho Park, Neungsoo Ro, Won W. Li, Kuan-Ching Providence Univ Dept Comp Sci & Informat Engn Taichung Taiwan Myong Ji Univ Dept Comp Software Yongin Kyung Ki Do South Korea Konkuk Univ Dept Comp Sci & Engn Seoul South Korea Yonsei Univ Dept Elect Engn Seoul 120749 South Korea

Recently, computing Clusters based oil shared-memory multiprocessors (SNIP's) is becoming popular for high performance computing (HPC) applications. With the recent prevalence of CPU's, which are small-scale SMP's themselves, multi-core CPU's SMP Clusters will become increasingly popular in the near future. SNIP clusters have characteristics of both SMP's and MPP's. Therefore, developing parallel programs which can efficiently exploits characteristics of both SMP and MPP in SMP Clusters is a challenging task. Standard parallel programming Models Such as MPI. OpenMP, or hybrid (a combination of the two former models) are commonly used for SNIP Clusters. Depending oil the characteristics of applications, however, some programming models are better than others. To identify and select a Suitable programming model for an application oil SMP Clusters needs a quantity of analysis of the application behavior and its performance. In this paper, We conduct experimental studies to evaluate the benefits and limits of MPI and OpenMP oil three SNIP-based systems using standard HPC applications parallelized using MPI, OpenMP, and hybrid model. The performance results and final analysis may lead to in optimal programming model for the applications.

关键词： high performance computing MPI openMP hybrid parallel programming cluster of SMP'S

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Hints to improve automatic load balancing with LeWI for hybrid applications

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JOURNAL OF parallel AND DISTRIBUTED COMPUTING 2014年第9期74卷 2781-2794页

作者： Garcia, Marta Labarta, Jesus Corbalan, Julita Barcelona Supercomp Ctr Barcelona 08034 Spain Univ Politecn Cataluna Dept Comp Architecture Barcelona Spain

The DLB (Dynamic Load Balancing) library and LeWl (LEnd When Idle) algorithm provide a runtime solution to deal with the load imbalance of parallel applications independently of the source of imbalance. DLB relies on the usage of hybrid programming models and exploits the malleability of the second level of parallelism to redistribute computation power across processes. When executing real applications with LeWl, although application's performance is significantly improved, we have observed in some cases efficiency values between 60% and 70%, far from our theoretical limit. This work is a deep analysis of the sources of efficiency loss correlated with application characteristics, parallelization schemes and programming models. We have based our analysis in fine grain monitoring tools and metrics and validated our conclusions by reproducing them in synthetic experiments. As a result, this work teaches us some lessons that can be seen as hints to programmers to help LeWI make an efficient use of computational resources and obtain the maximum performance. (C) 2014 Elsevier Inc. All rights reserved.

关键词： Application performance analysis Load balancing hybrid parallel programming

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