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A C++20 Interface for MPI 4.0

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Ali Can Demiralp, Philipp Martin, Niko Sakic, Marcel Krüger, Tim Gerrits

Distributed, Parallel, and Cluster Computing · Submitted 20 Jun 2023

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim The interface utilizes recent language features to ease development of MPI applications.

how it is stated · asserted 1.00 read the record →

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arXiv ID2306.11840 (version 1)
Pith IDQNPBZQ25, Pith's permanent record of this paper and its review
CitationAli Can Demiralp, Marcel Kr\"uger, Niko Sakic, Philipp Martin, Tim Gerrits. "A C++20 Interface for MPI 4.0." arXiv:2306.11840 (2023). https://pith.science/paper/QNPBZQ25
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The compiled record

The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 4,665 input tokens.

What the abstract itself states, as probabilities: claims first 0.04 · states limitation 0.16 · human participants 0.02 · controlled comparison 0.78 · randomized assignment 0.01 · states quantitative result 0.09 · compares with prior results 0.84 · mentions code or data availability 0.04.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; .

We present a modern C++20 interface for MPI 4.0. The interface utilizes recent language features to ease development of MPI applications. An aggregate reflection system enables generation of MPI data types from user-defined classes automatically. Immediate and persistent operations are mapped to futures, which can be chained to describe sequential asynchronous operations and task graphs in a concise way. This work introduces the prominent features of the interface with examples. We further measure its performance overhead with respect to the raw C interface.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 We present a modern C++20 interface for MPI 4.0. contribution 1.00 0.91 0.22 asserted 1.00
s1 The interface utilizes recent language features to ease development of MPI applications. method 0.56 0.57 0.09 asserted 1.00
s2 An aggregate reflection system enables generation of MPI data types from user-defined classes automatically. contribution 0.57 0.82 0.08 asserted 1.00
s3 Immediate and persistent operations are mapped to futures, which can be chained to describe sequential asynchronous operations and task graphs in a concise way. contribution 0.57 0.82 0.11 asserted 1.00
s4 This work introduces the prominent features of the interface with examples. contribution 0.90 0.91 0.08 asserted 0.99
s5 We further measure its performance overhead with respect to the raw C interface. contribution 0.50 0.96 0.10 asserted 1.00

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from observation or experiment 0.97.

What was examined, and what was not

sources read
  • arXiv listing abstract v1
not examined
  • full text
  • figures and tables
  • references and citing sentences
  • relations to other papers
levels run
  • P abstract
not run
  • S sections
  • T body sentences
  • C citations
  • R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

We present a modern C++20 interface for MPI 4.0. The interface utilizes recent language features to ease development of MPI applications. An aggregate reflection system enables generation of MPI data types from user-defined classes automatically. Immediate and persistent operations are mapped to futures, which can be chained to describe sequential asynchronous operations and task graphs in a concise way. This work introduces the prominent features of the interface with examples. We further measure its performance overhead with respect to the raw C interface.

Human review

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What this paper does with the arXiv papers it cites

Each citing sentence is taken from the citing paper's LaTeX source and typed by Pith's classifier. A label appears at probability 0.8 or more (0.95 for support), a contest at 0.7 or more; the quoted words are the citing authors'. How each label was checked · JSON

What it cites

Cites as background 1 paper

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