Finding maximum-weight independent sets in graphs is an important NP-hard optimization problem. Given a vertex-weighted graph \(G\), the task is to find a subset of pairwise non-adjacent vertices of \(G\) with maximum weight. Most recently published practical exact algorithms and heuristics for this problem use a variety of data-reduction rules to compute (near- )optimal solutions. Applying these rules results in an equivalent instance of reduced size. An optimal solution to the reduced instance can be easily used to construct an optimal solution for the original input.

The suffix array contains the lexicographical order of all suffixes of a text. It is one of the most well-studied text indices with applications in bioinformatics, compression, and pattern matching. The main bottleneck of distributed-memory suffix array construction algorithms is their memory requirements. Even careful implementations require 30×-60× the input size as working memory. We present a scalable and lightweight distributed-memory adaptation of the difference cover (DCX) suffix array construction algorithm. Our approach relies on novel bucketing and random chunk redistribution techniques which reduce our memory requirement to 20×-26× the input size for medium-sized inputs and to 14×-15× for large-sized inputs. Regarding running time, we achieve speedups of up to 5× over current state-of-the-art distributed suffix array construction algorithms.

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.

String sorting is an important part of tasks such as building index data structures. Unfortunately, current string sorting algorithms do not scale to massively parallel distributed-memory machines since they either have latency (at least) proportional to the number of processors $p$ or communicate the data a large number of times (at least logarithmic). We present practical and efficient algorithms for distributed-memory string sorting that scale to large $p$. Similar to state-of-the-art sorters for atomic objects, the algorithms have latency of about $p^{1/k}$ when allowing the data to be communicated $k$ times. Experiments indicate good scaling behavior on a wide range of inputs on up to 49152 cores. Overall, we achieve speedups of up to 5 over the current state-of-the-art distributed string sorting algorithms.

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.

String sorting is an important part of tasks such as building index data structures. Unfortunately, current string sorting algorithms do not scale to massively parallel distributed-memory machines since they either have latency (at least) proportional to the number of processors p or communicate the data a large number of times (at least logarithmic). We present practical and efficient algorithms for distributed-memory string sorting that scale to large p. Similar to state-of-the-art sorters for atomic objects, the algorithms have latency of about p1/k when allowing the data to be communicated k times. Experiments show good scaling behavior on a wide range of inputs on up to 49 152 cores.We achieve speedups of up to 5 over the current state-of-the-art distributed string sorting algorithms.

We develop and extensively evaluate highly scalable distributed-memory algorithms for computing minimum spanning trees (MSTs). At the heart of our solutions is a scalable variant of Borůvka’s algorithm. For partitioned graphs with many local edges we improve this with an effective form of contracting local parts of the graph during a preprocessing step. We also adapt the filtering concept of the best practical sequential algorithm to develop a massively parallel Filter-Borůvka algorithm that is very useful for graphs with poor locality and high average degree. Our experiments indicate that our algorithms scale well up to at least 65 536 cores and are up to 800 times faster than previous distributed MST algorithms.

There has been surprisingly little work on algorithms for sorting strings on distributed-memory parallel machines. We develop efficient algorithms for this problem based on the multi-way merging principle. These algorithms inspect only characters that are needed to determine the sorting order. Moreover, communication volume is reduced by also communicating (roughly) only those characters and by communicating repetitions of the same prefixes only once. Experiments on up to 1280 cores reveal that these algorithm are often more than five times faster than previous algorithms.