Evolution of the NVIDIA GPU Architecture

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Conclusions∗ GPUs are massively parallel devices that can be used forgeneral purpose computing, in addition to graphicsprocessing∗ As the cost continues to decrease, these devices becomeoff‐the‐shelf components that can be used to build largersystem.∗ In addition to compute capabilities, Kepler offers thebenefit of additional performance per watt, making a morepower efficient design.∗ When used with other technologies, like OpenCL, GPUs canbe used in heterogeneous platforms.
References∗∗∗∗∗∗∗http://www.nvidia.com/page/corporate_timeline.htmlhttp://www.pcmag.com/encyclopedia_term/0,2542,t=graphics+pipeline&i=43933,00.aspS. L. Alarcon, “CUDA Memories,” unpublished.NVIDIA. (2012 April 16). NVIDIA CUDA C Programming Guide. [Online]. Available:http://developer.download.nvidia.com/compute/DevZone/docs/html/C/doc/CUDA_C_Programming_Guide.pdf.NVIDIA. (2009). NVIDIA’s Next Generation CUDA TM Compute Architecture: Fermi. [Online].Available:http://www.nvidia.com/content/PDF/fermi_white_papers/NVIDIA_Fermi_Compute_Architecture_Whitepaper.pdf.NVIDIA. (2012). NVIDIA’s Next Generation CUDA TM Compute Architecture: Kepler TM GK110.[Online]. Available: http://www.nvidia.com/content/PDF/kepler/NVIDIA‐Kepler‐GK110‐Architecture‐Whitepaper.pdf.NVIDIA. (2012). NVIDIA GeForce GTX 680. [Online]. Available:http://www.geforce.com/Active/en_US/en_US/pdf/GeForce‐GTX‐680‐Whitepaper‐FINAL.pdf
Page 4: Graphics Pipeline
Page 8 and 9: Streaming Multiprocessor Architectu
Page 10 and 11: Architectural Memory Hierarchy
Page 12 and 13: Fermi Improvements∗ Increase the
Page 15 and 16: Kepler SM Design15
Page 17 and 18: Kepler Memory Architecture∗ Share
Page 19 and 20: Kepler Hardware Features∗ Dynamic
Page 21 and 22: Use for Computation∗ Historically
Page 23 and 24: Example - Vector AdditionCfor( int
Page 25: Synchronization and Performance∗

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