This paper introduces a new approach to building distributed-memory graph analytics
systems that exploits heterogeneity in processor types (CPU and GPU), partitioning policies …

Graph analytics systems must analyze graphs with billions of vertices and edges which
require several terabytes of storage. Distributed-memory clusters are often used for …

Distributed graph analytics systems for CPUs, like D-Galois and Gemini, and for GPUs, like
D-IrGL and Lux, use a bulk-synchronous parallel (BSP) programming and execution model …

Distributed-memory clusters are used for in-memory processing of very large graphs with
billions of nodes and edges. This requires partitioning the graph among the machines in the …

The trend towards processor heterogeneity and distributed-memory has significantly
increased the complexity of parallel programming. In addition, the mix of applications that …

Efficient graph partitioning is vital for high-performance graph-parallel systems. Traditional
graph partitioning methods attempt to both minimize communication cost and guarantee …

Asynchronous programming models (APM) are gaining more and more traction, allowing
applications to expose the available concurrency to a runtime system tasked with …

Graph neural networks (GNN) are compute intensive; thus, they are attractive for
acceleration on distributed platforms. We present DeepGalois, an efficient GNN framework …

The HPX asynchronous many-task runtime system has been using TCP and MPI as its
communication backends (parcelports). We developed a new HPX parcelport using a new …

There is a growing interest in Asynchronous Many-Task (AMT) runtimes as an efficient way
to map irregular and dynamic parallel applications onto heterogeneous computing …