Abstract
In this paper, we present Amethyst, a generalized on-the-fly de/re-compression framework for GPUs. Our developed framework allows to execute every computational function on every (un)compressed input format and to output the result in an arbitrary (un)compressed format. Thus, our on-the-fly de/re-compression framework approach automatically conducts a decompression on the input side and compression on the output side. To show the feasibility and applicability of our framework, we developed a prototype for integer data types using different integer compression formats. We use this prototype to present exhaustive evaluation results using a great variety of data-intensive operations ranging from simple additions up to compaction and compute-intensive matrix multiplications to validate the efficiency of Amethyst.
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References
Amethyst Github. https://github.com/yogi-tud/Amethyst
Chaudhuri, S., Shim, K.: Including group-by in query optimization. In: VLDB, vol. 94, pp. 12–15 (1994)
CUB: Main Page. https://nvlabs.github.io/cub/index.html
Damme, P., Ungethüm, A., Hildebrandt, J., Habich, D., Lehner, W.: From a comprehensive experimental survey to a cost-based selection strategy for lightweight integer compression algorithms. ACM Trans. Database Syst. 44(3), 9:1–9:46 (2019)
Damme, P., Ungethüm, A., Pietrzyk, J., Krause, A., Habich, D., Lehner, W.: Morphstore: analytical query engine with a holistic compression-enabled processing model. Proc. VLDB Endow. 13(11), 2396–2410 (2020)
Fett, J., Kober, U., Schwarz, C., Habich, D., Lehner, W.: Accelerating parallel operation for compacting selected elements on GPUs. In: Cano, J., Trinder, P. (eds.) Euro-Par 2022. LNCS, vol. 13440, pp. 186–200. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-12597-3_12
Graefe, G., Shapiro, L.D.: Data compression and database performance. University of Colorado, Boulder, Department of Computer Science (1990)
Lemire, D., Boytsov, L.: Decoding billions of integers per second through vectorization. Softw. Pract. Experience 45(1), 1–29 (2015)
Munshi, A., Gaster, B., Mattson, T.G., Ginsburg, D.: OpenCL Programming Guide. Pearson Education (2011)
NVIDIA Blog: Write Flexible Kernels with Grid-Stride Loops. https://docs.nvidia.com/cuda/pdf/CUDA_C_Best_Practices_Guide.pdf
Sanders, J., Kandrot, E.: CUDA by Example: An Introduction to General-Purpose GPU Programming. Addison-Wesley Professional (2010)
Shanbhag, A., Madden, S., Yu, X.: A study of the fundamental performance characteristics of GPUs and CPUs for database analytics. In: SIGMOD, pp. 1617–1632 (2020)
Shanbhag, A., Yogatama, B.W., Yu, X., Madden, S.: Tile-based lightweight integer compression in GPU. In: SIGMOD, pp. 1390–1403 (2022)
Acknowledgements
This work was partly funded by (1) the European Union’s Horizon 2020 research and innovation program under grant agreement No 957407 (DAPHNE) and (2) SoftwareCampus program of the German Federal Ministry of Education and Research.
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Fett, J., Habich, D., Lehner, W. (2024). Amethyst - A Generalized on-the-Fly De/Re-compression Framework to Accelerate Data-Intensive Integer Operations on GPUs. In: Tekli, J., Gamper, J., Chbeir, R., Manolopoulos, Y. (eds) Advances in Databases and Information Systems. ADBIS 2024. Lecture Notes in Computer Science, vol 14918. Springer, Cham. https://doi.org/10.1007/978-3-031-70626-4_8
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