OpenSHMEM Specification 1.4

Baker, Matthew; Boehm, Swen; Bouteiller, Aurélien; Chapman, Barbara; Cernohous, Robert; Culhane, James; Curtis, Tony; Dinan, James; Dubman, Mike; Feind, Karl; Venkata, Manjunath Gorentla; Grossman, Max; Hamidouche, Khaled; Hammond, Jeff R.; Itigin, Yossi; Lam, Bryant C.; Knaak, David; Kuehn, Jeffery A; Manser, Jens; Mintz, Tiffany M.; Ozog, David M.; Park, Nicholas S.; Poole, Steve; Poole, Wendy; Pophale, Swaroop; Potluri, Sreeram; Pritchard, Howard; Ravichandrasekaran, Naveen; Raymond, Michael A.; Ross, James A.; Shende, Sameer; Smith, Lauren; Shamis, Pavel

doi:10.2172/1460190

Cited by 5 publications

(5 citation statements)

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“…Compared to other HPC communication interfaces, such as OpenSHMEM [9], MPI provides a weak progress guarantee. For point-to-point communication, MPI guarantees that once a matching pair of send and receive is initiated, one of them will (eventually) complete.…”

Section: Progress and Fairnessmentioning

confidence: 99%

MPI detach — Towards automatic asynchronous local completion

et al. 2022

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Section: Progress and Fairnessmentioning

confidence: 99%

MPI detach — Towards automatic asynchronous local completion

et al. 2022

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“…Minor changes in the latest 2.1 to 2.2 update, e.g., added calls to clSetProgramSpecializationConstant and clSetProgramReleaseCallback, major changes in 1.2 to 2.0 update including shared virtual memory, device queues used to enqueue kernels on a device, added the possibility for kernels enqueing kernels using a device queue 2.2 [9] OpenACC Reduction clause on in a compute construct assumes a copy for each reduction variable, arrays and composite variables are allowed in reduction clauses, local device defined 2.7 [10] Java OpenSHMEM Multithreading support, extended type support, C11 type-generic interfaces for point-to-point synchronization, additional functions and extensions to the existing ones 1.4 [68] Apache Hadoop Support for opportunistic containers, i.e., containers that are scheduled even if there is not enough resources to run them. Opportunistic containers wait for resource availability and since they have low priority, they are preempted if higher priority jobs are scheduled 3.0.3 [69] Apache Spark Built-in avro datasource, support for eager evaluation of DataFrames 2.4 [70] data to and from a GPU.…”

Section: Comparisons Of Existing Parallel Programming Approaches For mentioning

confidence: 99%

Survey of Methodologies, Approaches, and Challenges in Parallel Programming Using High-Performance Computing Systems

Czarnul

Proficz

Drypczewski

2020

Scientific Programming

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This paper provides a review of contemporary methodologies and APIs for parallel programming, with representative technologies selected in terms of target system type (shared memory, distributed, and hybrid), communication patterns (one-sided and two-sided), and programming abstraction level. We analyze representatives in terms of many aspects including programming model, languages, supported platforms, license, optimization goals, ease of programming, debugging, deployment, portability, level of parallelism, constructs enabling parallelism and synchronization, features introduced in recent versions indicating trends, support for hybridity in parallel execution, and disadvantages. Such detailed analysis has led us to the identification of trends in high-performance computing and of the challenges to be addressed in the near future. It can help to shape future versions of programming standards, select technologies best matching programmers’ needs, and avoid potential difficulties while using high-performance computing systems.

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“…Compared to other HPC communication interfaces, such as Open-SHMEM [1], MPI provides a weak progress guarantee. For point-topoint communication, MPI guarantees that once a matching pair of send and receive is initiated, one of them will (eventually) complete.…”

Section: Progress and Fairnessmentioning

confidence: 99%

MPI Detach - Asynchronous Local Completion

Protze

Hermanns

Demiralp

et al. 2020

27th European MPI Users' Group Meeting

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When aiming for large scale parallel computing, waiting time due to network latency, synchronization, and load imbalance are the primary opponents of high parallel efficiency. A common approach to hide latency with computation is the use of non-blocking communication. In the presence of a consistent load imbalance, synchronization cost is just the visible symptom of the load imbalance. Tasking approaches as in OpenMP, TBB, OmpSs, or C++20 coroutines promise to expose a higher degree of concurrency, which can be distributed on available execution units and significantly increase load balance. Available MPI non-blocking functionality does not integrate seamlessly into such tasking parallelization. In this work, we present a slim extension of the MPI interface to allow seamless integration of non-blocking communication with available concepts of asynchronous execution in OpenMP and C++.

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OpenSHMEM Specification 1.4

Cited by 5 publications

References 0 publications

MPI detach — Towards automatic asynchronous local completion

MPI detach — Towards automatic asynchronous local completion

Survey of Methodologies, Approaches, and Challenges in Parallel Programming Using High-Performance Computing Systems

MPI Detach - Asynchronous Local Completion

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