Graph neural networks (GNNs) are an emerging research field. This specialized deep
neural network architecture is capable of processing graph structured data and bridges the …

SAT solving in large distributed environments has previously led to some famous results and
to impressive speedups for selected inputs. However, in terms of general-purpose SAT …

With the growing reliance of modern supercomputers on accelerator-based architecture
such a graphics processing units (GPUs), the development and optimization of electronic …

We present new sequential and parallel sorting algorithms that now represent the fastest
known techniques for a wide range of input sizes, input distributions, data types, and …

Partitioning graphs into blocks of roughly equal size such that few edges run between blocks
is a frequently needed operation in processing graphs. Recently, size, variety, and structural …

The single-source shortest-path (SSSP) problem is a notoriously hard problem in the
parallel context. In practice, the Δ-step** algorithm of Meyer and Sanders has been widely …

Data structures for efficient sampling from a set of weighted items are an important building
block of many applications. However, few parallel solutions are known. We close many of …

Gradient aggregation has long been identified as a major bottleneck in today's large-scale
distributed machine learning training systems. One promising solution to mitigate such …

Research on algorithms has drastically increased in recent years. Various sub-disciplines of
computer science investigate algorithms according to different objectives and standards …

In this work, we address an online job scheduling problem in a large distributed computing
environment. Each job has a priority and a demand of resources, takes an unknown amount …