|Title||PGAS for graph analytics: can one sided communications break the scalability barrier ?|
|Project(s)||Meeting Exascale Computing with Source-to-Source Compilers|
|Publication Type||Talk, keynote|
|Year of Publication||2019|
|Location of Talk||EFFECT workshop, Tromsø, Norway|
|Keywords||Convergence, Graph algorithms, PGAS|
As the world is becoming increasingly interconnected, systems are becoming increasingly complex. Therefore, technologies that can analyze connected systems and their dynamic characteristics become indispensable. Consequently, the last decade has seen increasing interest in graph analytics, which allows obtaining insights from such connected data. Parallel graph analytics can reveal the workings of intricate systems and networks at massive scales, which are found in diverse areas such as social networks, economic transactions, and protein interactions. While sequential graph algorithms have been studied for decades, the recent availability of massive datasets has given rise to the need for parallel graph processing, which poses unique challenges.
Benchmarks such as the Graph 500 have shown that graph processing performance is largely unrelated to traditional measurements of performance such as FLOPS or memory bandwidth. Instead, algorithmic communication aggregation and network latencies play a crucial role here.
In this talk we introduce the area of parallel graph analytics with a special focus on news dissemination, along with the technical challenges it presents and discuss how PGAS systems with support for one-sided messaging, such as UPC++, can help in overcoming these challenges.