Orchestrated Co-scheduling, Resource Partitioning, and Power Capping on CPU-GPU Heterogeneous Systems via Machine Learning.
Issa SabaEishi ArimaDai LiuMartin SchulzPublished in: ARCS (2022)
Keyphrases
- heterogeneous systems
- grid environment
- machine learning
- data transfer
- parallel computing
- multithreading
- graphics processing units
- resource allocation
- scheduling algorithm
- resource constraints
- parallel architectures
- computing systems
- data access
- grid computing
- processing units
- parallel machines
- data flow
- data transmission
- distributed architecture
- scheduling problem
- parallel computers
- real time
- application developers
- power consumption
- peer to peer
- parallel programming
- file system
- parallel processing
- massively parallel
- security issues
- data management
- mobile robot
- distributed computing
- agent technology
- data partitioning
- distributed environment
- web services