Thought experiment: if we wanted to design a programming system for computing on graphs, where might we begin? What abstractions do we need? What are the fundamental operations …

Jul 31, 2023 · The parallel algorithm model solves the large problem by dividing it into smaller parts and then solving each independent sub-task simultaneously by using its own approach. …

Mar 25, 2021 · To allow developers to succinctly express graph algorithms under such environment, many programming models for parallel graph processing have been proposed. In …

Detailed steps for parallel program design and implementation 1. Preparing Parallelism • Computational task partitioning. Aggregate tasks when needed. • Dependence analysis to …

from system architectures to programming models. In this chapter, we review the challenges of parallel processing of large graphs, representative graph processing

Using these essential components, we propose an abstraction that captures all the significant programming models within graph analytics, such as bulk-synchronous, asynchronous, shared …

The model of a parallel algorithm is developed by considering a strategy for dividing the data and processing method and applying a suitable strategy to reduce interactions. In this chapter, we …

Constructing a Parallel Algorithm • identify portions of work that can be performed concurrently • map concurrent portions of work onto multiple processes running in parallel • distribute a …

Mar 18, 2021 · The proposed model, Adaptive Massively Parallel Computation (AMPC), augments the theoretical capabilities of MapReduce, providing a pathway to solve many graph problems …

• Task dependency graphs can be used to ensure that work is equally spread across all processes at any point (minimum idling and optimal load balance). • Task interaction graphs …