Abstract
In analytical queries, a number of important operators like JOIN and GROUP BY are suitable for parallelization, and GPU is an ideal accelerator considering its power of parallel computing. However, when data size increases to hundreds of gigabytes, one GPU card becomes insufficient due to the small capacity of global memory and the slow data transfer between host and device. A straightforward solution is to equip more GPUs linked with high-bandwidth connectors, but the cost will be highly increased. We utilize unified memory (UM) produced by NVIDIA CUDA (Compute Unified Device Architecture) to make it possible to accelerate large-scale queries on just one GPU, but we notice that the transfer performance between host and UM, which happens before kernel execution, is often significantly slower than the theoretical bandwidth. An important reason is that, in single-GPU environment, data processing systems usually invoke only one or a static number of threads for data copy, leading to an inefficient transfer which slows down the overall performance heavily. In this paper, we present D-Cubicle, a runtime module to accelerate data transfer between host-managed memory and unified memory. D-Cubicle boosts the actual transfer speed dynamically through a self-adaptive approach. In our experiments, taking data transfer into account, D-Cubicle processes 200 GB of data on a single GPU with 32 GB of global memory, achieving 1.43x averagely and 2.09x maximally the performance of the baseline system.
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Acknowledgements
This work was supported by the National Natural Science Foundation of China (Grant Nos. 61732014 and 62141214) and the National Key Research and Development Program of China (2018YFB1003400).
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Jialun Wang is a PhD candidate in the School of Data Science and Engineering, East China Normal University, China. He received his bachelor’s degree in computer science from Sichuan University, China. His research interests include parallel and distributed systems, CPU-GPU heterogeneous systems, and in-memory data processing.
Wenhao Pang is currently working toward the master’s degree in the School of Data Science and Engineering, East China Normal University, China. He received his bachelor’s degree from Donghua University, China. His research interests include parallel and distributed systems, and heterogeneous computing.
Chuliang Weng is a full professor on computer science at East China Normal University (ECNU), China. Before joining ECNU, he worked at Huawei Central Research Institute as a principal researcher and technical director. Before joining Huawei, he was an associate professor in the Department of Computer Science and Engineering at Shanghai Jiao Tong University, China. He was also a visiting research scientist in the Department of Computer Science at Columbia University, USA. His interests center on building fast and secure systems. His research includes parallel and distributed systems, system virtualization and cloud, storage systems, operating systems, and system security.
Aoying Zhou, Professor at School of Data Science and Engineering (DaSE), East China Normal University (ECNU), China. He is CCF Fellow, Vice President of Shanghai Computer Society, and Associate Editor-in-Chief of Chinese Journal of Computer. His research interests include database, data management, digital transformation, and data-driven applications such as Financial Technology (FinTech), Education Technology (EduTech), and Logistics Technology (LogTech).
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Wang, J., Pang, W., Weng, C. et al. D-Cubicle: boosting data transfer dynamically for large-scale analytical queries in single-GPU systems. Front. Comput. Sci. 17, 174610 (2023). https://doi.org/10.1007/s11704-022-2160-z
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DOI: https://doi.org/10.1007/s11704-022-2160-z