Architecture of Computing Systems – ARCS 2020 by Unknown
Author:Unknown
Language: eng
Format: epub
ISBN: 9783030527945
Publisher: Springer International Publishing
6 Conclusion
We presented X-CEL, a measurement-based method to estimate the potential of near-memory acceleration. It helps to perform an early yet robust estimation whether the development effort of a near-memory accelerator is worthwhile. The two-stage method is based on measurements of an easy-to-integrate near-memory core (near-memory, but no accelerator) variant, which is closer to the target design than the existing baseline implementation (neither near-memory, nor hardware-accelerated). We showcased X-CEL with a (near-memory) graph copy problem in a tile-based MPSoC with a set of distributed graph algorithm kernels. An in-depth analysis revealed that the second stage estimate is within 5 % of the actual speedup in 70 % of the configurations. Moreover, it has 36 % higher accuracy than the original estimate.
Future work could refine the estimation model, as well as extend the framework to more case studies.
All in all, we envision X-CEL to become an x-cel-lent tool in the hand of developers to make sophisticated predictions on the near-memory acceleration potential and thereby avoid unnecessary development effort.
References
1.
Ahn, J., Hong, S., Yoo, S., Mutlu, O., Choi, K.: A scalable processing-in-memory accelerator for parallel graph processing. In: Proceedings of the 42nd Annual International Symposium on Computer Architecture, Portland, OR, USA, 13–17 June 2015, pp. 105–117 (2015). https://doi.org/10.1145/2749469.2750386
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