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An Investigation of Unified Memory Access Performance in CUDA.
... IEEE Conference on High Performance Extreme Computing. IEEE Conference on High Performance Extreme Computing
|November 24, 2015
Summary
Unified Memory Access (UMA) simplifies GPU computing memory management. While UMA offers performance benefits for specific applications, its overheads and reduced flexibility limit broader adoption.
Area of Science:
- Computer Science
- High-Performance Computing
Background:
- CPU-GPU memory management is complex.
- Nvidia's Unified Memory Access (UMA) aims to simplify this.
- UMA claims improved performance.
Purpose of the Study:
- Investigate UMA's performance.
- Evaluate UMA's programming model simplifications.
- Compare UMA to traditional data transfer methods.
Main Methods:
- Experimental evaluation of UMA.
- Analysis of performance metrics.
- Assessment of programming model flexibility.
Main Results:
- UMA enables on-demand data transfer from GPU to CPU and vice-versa.
- UMA outperforms full data transfers for certain parallel applications and small datasets.
- Significant performance overheads and restricted flexibility observed for most applications.
Conclusions:
- UMA offers advantages in specific scenarios but presents considerable overheads.
- The programming model's simplifications may hinder future optimization efforts.
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