Abstract Since the C++ bindings were deleted in 2008, the Message Passing Interface (MPI) community has recently revived efforts in building high-level modern C++ interfaces. Such interfaces are either built to serve specific scientific application needs (with limited coverage to the underlying MPI functionality), or as an exercise in general-purpose programming model building, with the hope that bespoke interfaces can be broadly adopted to construct a variety of distributed-memory scientific applications. However, with the advent of modern C++-based heterogeneous programming models, GPUs and widespread Machine Learning (ML) usage in contemporary scientific computing, the role of prospective community-standardized high-level C++ interfaces to MPI is evolving. The success of such an interface clearly will depend on providing robust abstractions and features adhering to the generic programming principles that underpin the C++ programming language, without compromising on either performance or portability, the core principles upon which MPI was founded. However, there is a tension between idiomatic C++ handling of types and lifetimes and MPI’s loose interpretation of object lifetimes/ownership and insistence on maintaining global states. Instead of proposing “yet another” high-level C++ interface to MPI, overlooking or providing partial solutions to work around the key issues concerning the dissonance between MPI semantics and idiomatic C++, this paper focuses on the three fundamental aspects of a high-level interface: type system, object lifetimes, and communication buffers, while also identifying inconsistencies in the MPI specification. Presumptive solutions can be unrefined, and we hope the broader MPI and C++ communities will engage with us in productive exchange of ideas and concerns.

The Message-Passing Interface (MPI) and C++ form the backbone of high-performance computing, but MPI only provides $\mathbf{C}$ and Fortran bindings. While this offers great language interoperability, high-level programming languages like C++ make software development quicker and less error-prone.We propose novel $\mathrm{C}_{++}$language bindings that cover all abstraction levels from low-level MPI calls to convenient STL-style bindings, where most parameters are inferred from a small subset of parameters, by bringing named parameters to C++. This enables rapid prototyping and fine-tuning runtime behavior and memory management. A flexible type system and additional safety guarantees help to prevent programming errors.By exploiting C++’s template metaprogramming capabilities, this has (near) zero overhead, as only required code paths are generated at compile time.We demonstrate that our library is a strong foundation for a future distributed standard library using multiple application benchmarks, ranging from text-book sorting algorithms to phylogenetic interference.

The Message-Passing Interface (MPI) and C++ form the backbone of high-performance computing and algorithmic research in the field of distributed-memory computing, but MPI only provides C and Fortran bindings.This provides good language interoperability, but higher-level programming languages make development quicker and less error-prone.We propose novel C++ language bindings designed to cover the whole range of abstraction levels from low-level MPI calls to convenient STL-style bindings, where most parameters are inferred from a small subset of the full parameter set.This allows for both rapid prototyping and fine-tuning of distributed code with predictable runtime behavior and memory management.Using template-metaprogramming, only code paths required for computing missing parameters are generated at compile time, which results in (near) zero-overhead bindings.

Counting triangles in a graph and incident to each vertex is a fundamental and frequently considered task of graph analysis. We consider how to efficiently do this for huge graphs using massively parallel distributed-memory machines. Unsurprisingly, the main issue is to reduce communication between processors. We achieve this by counting locally whenever possible and reducing the amount of information that needs to be sent in order to handle (possible) nonlocal triangles. We also achieve linear memory requirements despite superlinear communication volume by introducing a new asynchronous sparse-all-to-all operation. Furthermore, we dramatically reduce startup overheads by allowing this communication to use indirect routing. Our algorithms scale (at least) up to 32 768 cores and are up to 18 times faster than the previous state of the art.