2015/07/23 by Jesun Firoz, Jesun Sahariar Firoz, Thejaka Amila Kanewala +8 · 1 voice
Computer Science · #Advanced Graph Neural Networks #D.1.3 #Data Structures and Algorithms (cs.DS) #Distributed #Distributed and Parallel Computing Systems #FOS: Computer and information sciences #Graph Theory and Algorithms #Parallel #Performance (cs.PF) #Software Engineering (cs.SE) #and Cluster Computing (cs.DC) #cs.DC #cs.DS #cs.PF #cs.SE
paper · pdf · doi:10.48550/arxiv.1507.06702
openalex publication_date 2015/07/23 · arxiv published 2015/07/23 · arxiv updated 2015/07/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The increasing complexity of the software/hardware stack of modern supercomputers results in explosion of parameters. The performance analysis becomes a truly experimental science, even more challenging in the presence of massive irregularity and data dependency. We analyze how the existing body of research handles the experimental aspect in the context of distributed graph algorithms (DGAs). We distinguish algorithm-level contributions, often prioritized by authors, from runtime-level concerns that are harder to place. We show that the runtime is such an integral part of DGAs that experimental results are difficult to interpret and extrapolate without understanding the properties of the runtime used. We argue that in order to gain understanding about the impact of runtimes, more information needs to be gathered. To begin this process, we provide an initial set of recommendations for describing DGA results based on our analysis of the current state of the field.