Streaming Task Graph Scheduling for Dataflow Architectures
Scalable Parallel Computing Lab, SPCL @ ETH Zurich via YouTube
Future-Proof Your Career: AI Manager Masterclass
Learn AI, Data Science & Business — Earn Certificates That Get You Hired
Overview
AI, Data Science & Cloud Certificates from Google, IBM & Meta — 40% Off
One plan covers every Professional Certificate on Coursera. 40% off Coursera Plus Annual.
Unlock All Certificates
Explore a cutting-edge approach to scheduling task graphs on dataflow architectures in this 29-minute conference talk from #HPDC 2023. Delve into the concept of canonical task graphs and their application in streaming task graph scheduling for dataflow devices. Learn about steady-state analysis techniques and how they inform the partitioning of task graphs into temporally multiplexed components of spatially executed tasks. Discover the potential benefits of this innovative scheduling method, including increased speedup and improved device utilization compared to traditional approaches. Gain insights into the challenges and opportunities presented by dataflow accelerators in the field of high-performance computing. Follow the presentation's logical progression from introduction to conclusions, covering key topics such as spatial block partitioning, task scheduling, and real-world results on synthetic and realistic workloads.
Syllabus
- Introduction
- Canonical Task Graphs
- Steady State Analysis
- Spatial Block Partitioning and Task Scheduling
- Results
- Conclusions
Taught by
Scalable Parallel Computing Lab, SPCL @ ETH Zurich