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The abstraction of a shared memory space over separate CPU and GPU memory domains has eased the burden of portability for many HPC codebases. However, users pay for ease of use provided by system-managed memory with a moderate-to-high performance overhead. NVIDIA Unified Virtual Memory (UVM) is currently the primary real-world implementation of such abstraction and offers a functionally equivalent testbed for in-depth performance study for both UVM and future Linux Heterogeneous Memory Management (HMM) compatible systems. The continued advocacy for UVM and HMM motivates improvement of the underlying system. We focus on UVM-based systems and investigate the root causes of UVM overhead, a non-trivial task due to complex interactions of multiple hardware and software constituents and the desired cost granularity. In our prior work, we delved deeply into UVM system architecture and showed internal behaviors of page fault servicing in batches. We provided quantitative evaluation of batch handling for various applications under different scenarios, including prefetching and oversubscription. We revealed that the driver workload depends on the interactions among application access patterns, GPU hardware constraints, and host OS components. Host OS components have significant overhead present across implementations, warranting close attention. This extension furthers our prior study in three aspects: fine-grain cost analysis and breakdown, extension to multiple GPUs, and investigation of platforms with different GPU-GPU interconnects. We take a top-down approach to quantitative batch analysis and uncover how constituent component costs accumulate and overlap, governed by synchronous and asynchronous operations. Our multi-GPU analysis shows reduced cost of GPU-GPU batch workloads compared to CPU-GPU workloads. We further demonstrate that while specialized interconnects, NVLink, can improve batch cost, their benefits are limited by host OS software overhead and GPU oversubscription. This study serves as a proxy for future shared memory systems, such as those that interface with HMM, and the development of interconnects.
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BeaCloud: A Generic Architecture for Sustainable Smart City using Bluetooth Beacons
In recent years, Bluetooth beacons have been widely used in numerous application domains, including smart cities, assistive technologies, and intelligent transportation management. Researchers or developers associated with these domains frequently require diverse systems to implement or test their prototype related innovations. They need to deploy beacons for the specific environment every time; such customized systems typically cannot often be reused. Hence, the cost of implementation increases, and multiple systems generate redundant data. In this paper, we propose BeaCloud - an architecture which provides a common platform of multiple beacon-based systems. BeaCloud enables inter-system communication and allows easy and secure access to data for the system administrators. The proposed architecture presents a cost-effective model that offers reduced cost and hardware. Also, BeaCloud reduces the data redundancy by up to 40%. To demonstrate the feasibility of BeaCloud, we implemented a testbed of three testing sites and evaluated the system's performance.
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- Award ID(s):
- 1952090
- PAR ID:
- 10282332
- Date Published:
- Journal Name:
- IEEE International Conference on High Performance Computing and Communications (HPCC)
- Page Range / eLocation ID:
- 1150 to 1157
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/HPCC-SmartCity-DSS50907.2020.00149
