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Accelerated bulk memory operations on heterogeneous multi-core systems

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Abstract

A traditional fixed-function graphics accelerator has evolved into a programmable general-purpose graphics processing unit over the past few years, the general-purpose computing on GPU (GPGPU). Recently, revolutionary measures have been taken along this direction: an integrated GPU, i.e., CPUs and GPUs are integrated into the same package or even into the same die. However, considering a system-on-chip, the GPU takes up considerable silicon resources, but when running non-graphical workloads or non-GPGPU applications it is likely that overall system performance will not be affected. This paper presents a novel approach to accelerate conventional operations that are normally performed on CPUs, which are bulk memory operations such as memcpy or memcmp, using an integrated GPU. Offloading bulk memory operations to the GPU has many benefits: (i) The throughput GPU outperforms the CPU in bulk memory operations; (ii) for on-die GPUs with unified cache between the GPU and the CPU, the CPU can utilize the GPU private cache to store the moved data and reduce the CPU cache bottleneck; (iii) additional lightweight hardware can also support asynchronous offloads; and (iv) unlike the prior art using a dedicated hardware copy engine (e.g., DMA), our approach utilizes as much GPU hardware resources as possible. The performance results based on our solution showed that offloaded bulk memory operations outperform CPU up to 4.3 times faster on micro-benchmarks while using fewer resources. Using eight real-world applications and a cycle-based full-system simulation environment, five of eight applications showed about 30% speedup and two applications showed about 20% speedup.

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References

  1. Lee J, Liu Z, Tian X, Woo DH, Shi W, Boumber D, Yan Y, Kwon KA (2012) Acceleration of bulk memory operations in a heterogeneous multicore architecture. In: Proceedings of the 21st International Conference on Parallel Architectures and Compilation Techniques. ACM, pp 423–424

  2. The 50th TOP500 list (2017). https://www.top500.org/lists/2017/11/. Accessed 4 Sept 2018

  3. Benziane SH, Benyettou A (2017) Dorsal hand vein identification based on binary particle swarm optimization. J Inf Process Syst 13(2):268–283

    Google Scholar 

  4. Finogeev AG, Parygin DS, Finogeev AA (2017) The convergence computing model for big sensor data mining and knowledge discovery. Hum Centric Comput Inf Sci 7(1):11

    Article  Google Scholar 

  5. Ghadekar PP, Chopade NB (2016) Content based dynamic texture analysis and synthesis based on SPIHT with GPU. J Inf Process Syst 12(1):46–56

    Google Scholar 

  6. Koo KM, Cha EY (2017) Image recognition performance enhancements using image normalization. Hum Centric Comput Inf Sci 7(1):33

    Article  Google Scholar 

  7. Mohd-Hilmi MN, Al-Laila MH, Malim H, Ahamed NH (2016) Accelerating group fusion for ligand-based virtual screening on multi-core and many-core platforms. J Inf Process Syst 12(4):724–740

    Google Scholar 

  8. Hao F, Min G, Pei Z, Park DS, Yang LT (2017) \( k \)-clique community detection in social networks based on formal concept analysis. IEEE Syst J 11(1):250–259

    Article  Google Scholar 

  9. Hao F, Pei Z, Park DS, Yang LT, Jeong YS, Park JH (2017) Iceberg clique queries in large graphs. Neurocomputing 256:101–110

    Article  Google Scholar 

  10. Song W, Liu L, Tian Y, Sun G, Fong S, Cho K (2017) A 3D localisation method in indoor environments for virtual reality applications. Hum Centric Comput Inf Sci 7(1):39

    Article  Google Scholar 

  11. Memcached—a distributed memory object caching system (2015). http://www.memcached.org/ Accessed 4 Sept 2018

  12. Fung W, Sham I, Yuan G, Aamodt T (2007) Dynamic warp formation and scheduling for efficient GPU control flow. In: Proceedings of the 40th Annual IEEE/ACM International Symposium on Microarchitecture. IEEE Computer Society, pp 407–420

  13. Intel streaming SIMD extensions technology (2017). https://www.intel.com/content/www/us/en/support/articles/000005779/processors.html. Accessed 4 Sept 2018

  14. Nvidia CUDA (2007). https://developer.nvidia.com/cuda-zone. Accessed 4 Sept 2018

  15. Intel advanced vector extensions 512 (AVX-512) (2015). https://www.intel.com/content/www/us/en/architecture-andtechnology/avx-512-overview.html. Accessed 4 Sept 2018

  16. Gschwind M (2006) Chip multiprocessing and the cell broadband engine. In: Proceedings of the 3rd Conference on Computing Frontiers, CF ’06. ACM, New York, NY, USA, pp 1–8

  17. Jiang X, Solihin Y, Zhao L, Iyer R (2009) Architecture support for improving bulk memory copying and initialization performance. In: Proceedings of the 2009 18th International Conference on Parallel Architectures and Compilation Techniques. IEEE Computer Society, Washington, DC, USA, pp 169–180

  18. Seshadri V, Mutlu O (2017) Simple operations in memory to reduce data movement. In: Hurson AR, Milutinovic V (ed) Advances in computers, vol 106. Elsevier, New York, pp 107–166

    Google Scholar 

  19. Zhao L, Bhuyan LN, Iyer R, Makineni S, Newell D (2007) Hardware support for accelerating data movement in server platform. IEEE Trans Comput 56:740–753

    Article  MathSciNet  Google Scholar 

  20. Woo DH, Lee HHS (2010) Compass: a programmable data prefetcher using idle GPU shaders. In: Hoe JC, Adve VS (eds) ASPLOS. ACM, New York, pp 297–310

    Google Scholar 

  21. Abts D, Bataineh A, Scott S, Faanes G, Schwarzmeier J, Lundberg E, Johnson T, Bye M, Schwoerer G (2007) The Cray BlackWidow: a highly scalable vector multiprocessor. In: Proceedings of the 2007 ACM/IEEE Conference on Supercomputing, SC ’07. ACM, New York, NY, USA, pp 17:1–17:12

  22. Ahn J, Hong S, Yoo S, Mutlu O, Choi K (2016) A scalable processing-in-memory accelerator for parallel graph processing. ACM SIGARCH Comput Archit News 43(3):105–117

    Article  Google Scholar 

  23. Hsieh K, Ebrahimi E, Kim G, Chatterjee N, O’Connor M, Vijaykumar N, Mutlu O, Keckler SW (2016) Transparent offloading and mapping (tom): enabling programmer-transparent near-data processing in GPU systems. ACM SIGARCH Comput Archit News 44(3):204–216

    Article  Google Scholar 

  24. Pattnaik A, Tang X, Jog A, Kayiran O, Mishra AK, Kandemir MT, Mutlu O, Das CR (2016) Scheduling techniques for GPU architectures with processing-in-memory capabilities. In: Proceeedings of the 2016 International Conference on Parallel Architecture and Compilation Techniques (PACT). IEEE, pp 31–44

  25. Seshadri V, Lee D, Mullins T, Hassan H, Boroumand A, Kim J, Kozuch MA, Mutlu O, Gibbons PB, Mowry TC (2017) Ambit: in-memory accelerator for bulk bitwise operations using commodity dram technology. In: Proceedings of the 50th Annual IEEE/ACM International Symposium on Microarchitecture. ACM, pp 273–287

  26. Vaidyanathan K, Chai L, Huang W, Panda DK (2007) Efficient asynchronous memory copy operations on multi-core systems and I/OAT. In: Proceedings of the 2007 IEEE International Conference on Cluster Computing, CLUSTER ’07. IEEE Computer Society, Washington, DC, USA, pp 159–168

  27. Kernighan BW, Dennis M (1988) The C programming language. Prentice-Hall, Upper Saddle River

    Google Scholar 

  28. 7th generation Intel core and Celeron desktop processor families with Intel H110 and Intel Q170 chipsets: platform brief (2017). https://www.intel.com/content/dam/www/public/us/en/documents/platformbriefs/7th-generation-core-processor-deskop-iot-platform-brief.pdf. Accessed 4 Sept 2018

  29. Magnusson P, Christensson M, Eskilson J, Forsgren D, Hallberg G, Hogberg J, Larsson F, Moestedt A, Werner B (2002) Simics: a full system simulation platform. Computer 35(2):50–58

    Article  Google Scholar 

  30. Neelakantam N, Blundell C, Devietti J, Martin MM, Zilles C (2008) FeS2: A full-system execution-driven simulator for x86. In: Proceedings of the Architectural Support for Programming Languages and Operating Systems. ASPLOS 2018

  31. Martin MMK, Sorin DJ, Beckmann BM, Marty MR, Xu M, Alameldeen AR, Moore KE, Hill MD, Wood DA (2005) Multifacet’s general execution-driven multiprocessor simulator (gems) toolset. SIGARCH Comput Archit News 33:92–99

    Article  Google Scholar 

  32. Yourst MT (2007) PTLsim: a cycle accurate full system x86-64 microarchitectural simulator. In: IEEE International Symposium on Performance Analysis of Systems and Software. ISPASS 2007

  33. Meng J, Skadron K (2009) Avoiding cache thrashing due to private data placement in last-level cache for manycore scaling. In: Proceedings of the 2009 IEEE International Conference on Computer Design, ICCD’09. IEEE Press, Piscataway, NJ, USA, pp 282–288

  34. Blackburn SM, Garner R, Hoffmann C, Khang AM, McKinley KS, Bentzur R, Diwan A, Feinberg D, Frampton D, Guyer SZ, Hirzel M, Hosking A, Jump M, Lee H, Moss JEB, Phansalkar A, Stefanović D, VanDrunen T, von Dincklage D, Wiedermann B (2006) The dacapo benchmarks: java benchmarking development and analysis. SIGPLAN Not 41:169–190

    Article  Google Scholar 

  35. DaCapo benchmark suite. http://dacapobench.org/. Accessed 4 Sept 2018

  36. Pybench. http://svn.python.org/. Accessed 4 Sept 2018

  37. ClamAV open source antivirus engine. http://www.clamav.net/. Accessed 4 Sept 2018

  38. Koziol J (2003) Intrusion detection with Snort, 1st edn. Sams, Indianapolis

    Google Scholar 

  39. Gzip. http://www.gzip.org/. Accessed 4 Sept 2018

  40. Sphinx text search server. http://sphinxsearch.com/. Accessed 4 Sept 2018

  41. ClamAV test files. https://packages.ubuntu.com/xenial-updates/utils/clamav-testfiles. Accessed 4 Sept 2018

  42. MIT Lincoln Laboratory 1998/1999 DARPA off-line intrusion detection (1999). https://www.ll.mit.edu/rd/datasets. Accessed 4 Sept 2018

  43. TREC-9 filtering track collections (2007). http://trec.nist.gov/data/t9_filtering.html. Accessed 4 Sept 2018

  44. Large text compression benchmark (2009). http://cs.fit.edu/~mmahoney/compression/text.html. Accessed 4 Sept 2018

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Acknowledgements

This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2016R1D1A3B03933370).

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Authors and Affiliations

  1. Department of Big Data Engineering, Daegu Catholic University, Gyeongsan-si, Republic of Korea

    JongHyuk Lee

  2. Department of Computer Science, University of Houston, Houston, USA

    Weidong Shi

  3. School of Information Technology Engineering, Daegu Catholic University, Gyeongsan-si, Republic of Korea

    JoonMin Gil

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  1. JongHyuk Lee
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  2. Weidong Shi
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Corresponding author

Correspondence to JoonMin Gil.

Additional information

This paper is an extended version of a conference paper that appeared as [1].

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Lee, J., Shi, W. & Gil, J. Accelerated bulk memory operations on heterogeneous multi-core systems. J Supercomput 74, 6898–6922 (2018). https://doi.org/10.1007/s11227-018-2589-x

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