As recent heterogeneous systems comprise multi-core CPUs and multiple GPUs, efficient allocation of multiple data-parallel applications has become a primary goal to achieve both maximum total performance and efficiency. However, the efficient orchestration of multiple applications is highly challenging because a detailed runtime status such as expected remaining time and available memory size of each computing device is hidden. To solve these problems, we propose a dynamic data-parallel application allocation framework called ADAMS. Evaluations show that our framework improves the average total execution device time by 1.85× over the round-robin policy in the non-shared-memory system with small data set.
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© 2020 by the authors. Licensee MDPI, Basel, Switzerland.
All Science Journal Classification (ASJC) codes
- Control and Systems Engineering
- Signal Processing
- Hardware and Architecture
- Computer Networks and Communications
- Electrical and Electronic Engineering