Understanding the GPU Microarchitecture to Achieve Bare-Metal Performance Tuning
暂无分享,去创建一个
Mingyu Chen | Jiajia Li | Xiuxia Zhang | Guangming Tan | Shuangbai Xue | Ke-ren Zhou | Guangming Tan | Mingyu Chen | Jiajia Li | Xiuxia Zhang | K. Zhou | Shuangbai Xue
[1] Paulo Centoducatte,et al. Extending the ArchC language for automatic generation of assemblers , 2005, 17th International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD'05).
[2] Xinxin Mei,et al. Benchmarking the Memory Hierarchy of Modern GPUs , 2014, NPC.
[3] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[4] Christian S. Collberg,et al. Reverse interpretation + mutation analysis = automatic retargeting , 1997, PLDI '97.
[5] Tanja Lange,et al. Usable assembly language for GPUs: a success story , 2012, IACR Cryptol. ePrint Arch..
[6] Jack Dongarra,et al. An Improved MAGMA GEMM for Fermi GPUs , 2010 .
[7] Ninghui Sun,et al. Fast implementation of DGEMM on Fermi GPU , 2011, 2011 International Conference for High Performance Computing, Networking, Storage and Analysis (SC).
[8] Andrew Zisserman,et al. Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.
[9] André Seznec,et al. Performance upper bound analysis and optimization of SGEMM on Fermi and Kepler GPUs , 2013, Proceedings of the 2013 IEEE/ACM International Symposium on Code Generation and Optimization (CGO).
[10] 杰克·希莱尔·肖凯特,et al. Methods and apparatus for source operand collector caching , 2012 .
[11] Andreas Moshovos,et al. Demystifying GPU microarchitecture through microbenchmarking , 2010, 2010 IEEE International Symposium on Performance Analysis of Systems & Software (ISPASS).
[12] Qian Wang,et al. AUGEM: Automatically generate high performance Dense Linear Algebra kernels on x86 CPUs , 2013, 2013 SC - International Conference for High Performance Computing, Networking, Storage and Analysis (SC).
[13] Xiang Zhang,et al. OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks , 2013, ICLR.
[14] James Demmel,et al. Benchmarking GPUs to tune dense linear algebra , 2008, 2008 SC - International Conference for High Performance Computing, Networking, Storage and Analysis.
[15] Jingyue Wu,et al. gpucc: An open-source GPGPU compiler , 2016, 2016 IEEE/ACM International Symposium on Code Generation and Optimization (CGO).
[16] Dawson R. Engler,et al. Reverse-Engineering Instruction Encodings , 2001, USENIX Annual Technical Conference, General Track.