Quantifying the NUMA Behavior of Partitioned GPGPU Applications

Author:

Matz Alexander1,Fröning Holger1

Affiliation:

1. Heidelberg University, Heidelberg, Germany

Publisher

ACM Press

Reference25 articles.

1. V. Adhinarayanan and W. Feng. 2016. An automated framework for characterizing and subsetting GPGPU workloads. In 2016 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS). 307--317.

2. Akhil Arunkumar, Evgeny Bolotin, Benjamin Cho, Ugljesa Milic, Eiman Ebrahimi, Oreste Villa, Aamer Jaleel, Carole-Jean Wu, and David Nellans. 2017. MCM-GPU: Multi-Chip-Module GPUs for Continued Performance Scalability. In Proceedings of the 44th Annual International Symposium on Computer Architecture (ISCA '17). ACM, New York, NY, USA, 320--332.

3. S. Che, M. Boyer, J. Meng, D. Tarjan, J. W. Sheaffer, S. Lee, and K. Skadron. 2009. Rodinia: A benchmark suite for heterogeneous computing. In 2009 IEEE International Symposium on Workload Characterization (IISWC). 44--54.

4. Anthony Danalis, Gabriel Marin, Collin McCurdy, Jeremy S. Meredith, Philip C. Roth, Kyle Spafford, Vinod Tipparaju, and Jeffrey S. Vetter. 2010. The Scalable Heterogeneous Computing (SHOC) Benchmark Suite. In Proceedings of the 3rd Workshop on General-Purpose Computation on Graphics Processing Units (GPGPU-3). ACM, New York, NY, USA, 63--74.

5. M. Doggett. 2012. Texture Caches. IEEE Micro 32, 3 (May 2012), 136--141.

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