Abstract
This paper envisions a future in which high performance and energy-modest parallel computing on low-end edge devices were achieved through cross-device functionality abstraction to make them interactive to cloud machines. Rather, there has been little exploration of the overall optimization into kernel processing can deliver for increasingly popular but heavy burden on low-end edge devices. Our idea here is to extend the capability of functionality abstraction across edge clients and cloud servers to identify the computation-intensive code regions automatically and execute the instantiation on the server at runtime. This paper is an attempt to explore this vision, ponder on the principle, and take the first steps towards addressing some of the challenges with 
. As a kernel-level solution, 
enables edge devices to abstract not only application layer but also system layer functionalities, as if they were to instantiate the abstracted function inside the same 
kernel programming. Experimental results demonstrate that 
makes cross-kernel functionality abstraction efficient for low-end edge devices and benefits them significant performance optimization than the default scheme unless in a constraint of low transmission bandwidth.
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References
Adreno GPU. https://developer.qualcomm.com/software/adreno-gpu-sdk/gpu
Binder-for-linux. https://github.com/hungys/binder-for-linux
CUDA Samples. https://docs.nvidia.com/cuda/cuda-samples/
Qualcomm Snapdragon Processor. https://www.qualcomm.com/snapdragon
UNI-T UT658 USB Tester. http://www.uni-trend.com
Aoki, R., et al.: Hybrid OpenCL: enhancing OpenCL for distributed processing. In: ISPA, pp. 149–154. IEEE (2011)
Bui, D.H., et al.: Rethinking energy-performance trade-off in mobile web page loading. In: MobiCom, pp. 14–26. ACM (2015)
Chun, B.G., et al.: Clonecloud: elastic execution between mobile device and cloud. In: EuroSys, pp. 301–314. ACM (2011)
Cuervo, E., et al.: Maui: making smartphones last longer with code offload. In: MobiSys, pp. 49–62. ACM (2010)
Cuervo, E., et al.: Kahawai: high-quality mobile gaming using GPU offload. In: MobiSys, pp. 121–135. ACM (2015)
Culler, D.E., et al.: Parallel programming in split-C. In: Proceedings of the Supercomputing 1993, pp. 262–273. IEEE (1993)
Fung, W.W., Aamodt, T.M.: Thread block compaction for efficient SIMT control flow. In: HPCA, pp. 25–36. IEEE (2011)
Georgiev, P., et al.: Accelerating mobile audio sensing algorithms through on-chip GPU offloading. In: MobiSys, pp. 306–318. ACM (2017)
Jäskeläinen, P.O., et al.: OpenCL-based design methodology for application-specific processors. In: SAMOS, pp. 223–230. IEEE (2010)
Nvidia, C.: Programming guide (2010)
Oh, S., et al.: Mobile plus: multi-device mobile platform for cross-device functionality sharing. In: MobiSys, pp. 332–344. ACM (2017)
Satyanarayanan, M., et al.: The case for VM-based cloudlets in mobile computing. IEEE Pervasive Comput. 8(4) (2009)
Shi, W., et al.: Edge computing: vision and challenges. IEEE Internet Things J. 3(5), 637–646 (2016)
Stallman, R.: Using and porting the GNU compiler collection. In: MIT Artificial Intelligence Laboratory. Citeseer (2001)
Stone, J.E., et al.: OpenCL: a parallel programming standard for heterogeneous computing systems. CiSE 12(3), 66–73 (2010)
Wang, W., et al.: Enabling cross-ISA offloading for COTS binaries. In: MobiSys, pp. 319–331. ACM (2017)
Wu, C., et al.: Butterfly: mobile collaborative rendering over GPU workload migration. In: INFOCOM 2017, pp. 1–9. IEEE (2017)
Acknowledgement
We thank the anonymous reviewers for their valuable and insightful comments. This work is supported by Tsinghua University Initiative Scientific Research Program under Grants No. 20161080066.
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Wu, C., Zhang, Y., Zhou, Y., Li, Q. (2018). Accelerating Low-End Edge Computing with Cross-Kernel Functionality Abstraction. In: Vaidya, J., Li, J. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2018. Lecture Notes in Computer Science(), vol 11334. Springer, Cham. https://doi.org/10.1007/978-3-030-05051-1_38
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DOI: https://doi.org/10.1007/978-3-030-05051-1_38
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