Computer Science > Distributed, Parallel, and Cluster Computing
[Submitted on 4 Feb 2024 (v1), last revised 21 May 2024 (this version, v2)]
Title:Exploring the Design Space for Message-Driven Systems for Dynamic Graph Processing using CCA
View PDF HTML (experimental)Abstract:Computer systems that have been successfully deployed for dense regular workloads fall short of achieving scalability and efficiency when applied to irregular and dynamic graph applications. Conventional computing systems rely heavily on static, regular, numeric intensive computations while High Performance Computing systems executing parallel graph applications exhibit little locality, spatial or temporal, and are fine-grained and memory intensive. With the strong interest in AI which depend on these very different use cases combined with the end of Moore's Law at nanoscale, dramatic alternatives in architecture and underlying execution models are required. This paper identifies an innovative non-von Neumann architecture, Continuum Computer Architecture (CCA), that redefines the nature of computing structures to yield powerful innovations in computational methods to deliver a new generation of highly parallel hardware architecture. CCA reflects a genus of highly parallel architectures that while varying in specific quantities (e.g., memory blocks), share a multiple of attributes not found in typical von Neumann machines. Among these are memory-centric components, message-driven asynchronous flow control, and lightweight out-of-order execution across a global name space. Together these innovative non-von Neumann architectural properties guided by a new original execution model will deliver the new future path for extending beyond the von Neumann model. This paper documents a series of interrelated experiments that together establish future directions for next generation non-von Neumann architectures, especially for graph processing.
Submission history
From: Bibrak Qamar Chandio [view email][v1] Sun, 4 Feb 2024 18:05:02 UTC (978 KB)
[v2] Tue, 21 May 2024 20:16:41 UTC (1,015 KB)
References & Citations
Bibliographic and Citation Tools
Bibliographic Explorer (What is the Explorer?)
Connected Papers (What is Connected Papers?)
Litmaps (What is Litmaps?)
scite Smart Citations (What are Smart Citations?)
Code, Data and Media Associated with this Article
alphaXiv (What is alphaXiv?)
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub (What is DagsHub?)
Gotit.pub (What is GotitPub?)
Hugging Face (What is Huggingface?)
Papers with Code (What is Papers with Code?)
ScienceCast (What is ScienceCast?)
Demos
Recommenders and Search Tools
Influence Flower (What are Influence Flowers?)
CORE Recommender (What is CORE?)
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.