We propose a GPU fine-grained load-balancing abstraction that decouples load balancing from work processing and aims to support both static and dynamic schedules with a programmable interface to implement new load-balancing schedules. Prior to our work, the only way to unleash the GPU’s potential on irregular problems has been to workload- balance through application-specific, tightly coupled load- balancing techniques. With our open-source framework for load-balancing, we hope to improve programmers’ productivity when developing irregular-parallel algorithms on the GPU, and also improve the overall performance characteristics for such applications by allowing a quick path to experimentation with a variety of existing load-balancing techniques. Consequently, we also hope that by separating the concerns of load-balancing from work processing within our abstraction, managing and extending existing code to future architectures becomes easier.
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Mazed and Confused: A Dataset of Cybersickness, Working Memory, Mental Load, Physical Load, and Attention During a Real Walking Task in VR
- Award ID(s):
- 2211785
- PAR ID:
- 10596666
- Publisher / Repository:
- IEEE
- Date Published:
- ISBN:
- 979-8-3315-1647-5
- Page Range / eLocation ID:
- 1048 to 1057
- Format(s):
- Medium: X
- Location:
- Bellevue, WA, USA
- Sponsoring Org:
- National Science Foundation
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