The DOI matches its Crossref record; 40 of the 40 references it cites were found in a registry or an index.
Practical Near-Data Processing for In-Memory Analytics Frameworks
Abstract
The end of Dennard scaling has made all systemsenergy-constrained. For data-intensive applications with limitedtemporal locality, the major energy bottleneck is data movementbetween processor chips and main memory modules. For such workloads, the best way to optimize energy is to place processing near the datain main memory. Advances in 3D integrationprovide an opportunity to implement near-data processing (NDP) withoutthe technology problems that similar efforts had in the past. This paper develops the hardware and software of an NDP architecturefor in-memory analytics frameworks, including MapReduce, graphprocessing, and deep neural networks. We develop simple but scalablehardware support for coherence, communication, and synchronization, anda runtime system that is sufficient to support analytics frameworks withcomplex data patterns while hiding all thedetails of the NDP hardware. Our NDP architecture provides up to 16x performance and energy advantageover conventional approaches, and 2.5x over recently-proposed NDP systems. We also investigate the balance between processing and memory throughput, as well as the scalability and physical and logical organization of the memory system. Finally, we show that it is critical to optimize software frameworksfor spatial locality as it leads to 2.9x efficiency improvements for NDP.
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Cites
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Cited by
Lee, S., Kang, S.-H., Lee, J., Kim, H., Lee, E., Seo, S., Yoon, H., Lee, S., Lim, K., Shin, H., et al. (2021) · 2021 ACM/IEEE 48th Annual International Symposium on Computer Architecture (ISCA) · doi:10.1109/isca52012.2021.00013 · Cited by 269
Data-Centric Computing Frontiers: A Survey On Processing-In-Memory
Siegl, P., Buchty, R., & Berekovic, M. (2016) · Proceedings of the Second International Symposium on Memory Systems · doi:10.1145/2989081.2989087 · Cited by 55
Metadata
- Authors
- Gao, M., Ayers, G., & Kozyrakis, C.
- Venue
- 2015 International Conference on Parallel Architecture and Compilation (PACT)
- Publisher
- IEEE
- Published
- 2015-10
- Pages
- 113-124
- OpenAlex
- W2545376626
- Cited by
- 266
- References
- 40
- FWCI
- 35.19
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Gao, M., Ayers, G., & Kozyrakis, C. (2015). Practical Near-Data Processing for In-Memory Analytics Frameworks. 2015 International Conference on Parallel Architecture and Compilation (PACT). https://doi.org/10.1109/pact.2015.22
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