PPT-Understanding GPU Memory

Author : conchita-marotz | Published Date : 2016-03-03

Instructor Notes This lecture begins with an example of how a widememory bus is utilized in GPUs The impact of memory coalescing and memory bank conflicts are also

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Understanding GPU Memory: Transcript

Instructor Notes This lecture begins with an example of how a widememory bus is utilized in GPUs The impact of memory coalescing and memory bank conflicts are also described along with examples on current hardware. Avg Access Time 2 Tokens Number of Controllers Average Access Time clock cyles brPage 16br Number of Tokens vs Avg Access Time 9 Controllers Number of Tokens Average Access Time clock cycles brPage 17br brPage 18br Dr A . Sahu. Dept of Comp Sc & . Engg. . . IIT . Guwahati. 1. Outline. Graphics System . GPU Architecture. Memory Model. Vertex Buffer, Texture buffer. GPU Programming Model. DirectX. , OpenGL, . Alan . Gray. EPCC . The University of Edinburgh. Outline. Why do we want/need accelerators such as GPUs?. Architectural reasons for accelerator performance advantages . Latest accelerator Products. NVIDIA and AMD GPUs. Rajat Phull, . Srihari. Cadambi, Nishkam Ravi and Srimat Chakradhar. NEC Laboratories America. Princeton, New Jersey, USA.. www.nec-labs.com. OpenFOAM Overview. OpenFOAM stands for:. ‘. O. pen . F. Patrick Cozzi. University of Pennsylvania. CIS 565 - Fall 2014. Acknowledgements. CPU slides – Varun Sampath, NVIDIA. GPU . slides. Kayvon . Fatahalian. , CMU. Mike Houston, . NVIDIA. CPU and GPU Trends. Lecture 2: more basics. Recap. Can use GPU to solve highly parallelizable problems. Straightforward extension to C++. Separate CUDA code into .cu and .. cuh. files and compile with . nvcc. to create object files (.o files). Multi-Application Execution. Adwait Jog. 1. Onur Kayiran. 2. , Tuba Kesten. 3. , Ashutosh Pattnaik. 3. , . Evgeny Bolotin. 4. , Nil Chatterjee. 4. , Steve Keckler. 4,5. , . Mahmut Kandemir. 3. , Chita Das. Sathish. . Vadhiyar. Parallel Programming. GPU. Graphical Processing Unit. A single GPU consists of large number of cores – hundreds of cores.. Whereas a single CPU can consist of 2, 4, 8 or 12 cores. Evangelia A. Sitaridi, Kenneth A. Ross. Columbia University. DaMoN Workshop. 21st May 2012. . Introduction (1/2). Earlier GPU implementations of data processing operators have resulted in significant speedups. . Installation. CS5100 Advanced . Computer Architecture. Introduction. . of. . Gem5-GPU. It. . merges . 2 popular simulators: gem5 and . gpgpu. -sim. Simulates . CPUs, GPUs, and the interactions between . Add GPUs: Accelerate Science Applications. © NVIDIA 2013. Small Changes, Big Speed-up. Application Code. . GPU. C. PU. Use GPU to Parallelize. Compute-Intensive Functions. Rest of Sequential. CPU Code. Jia. Pan and Dinesh Manocha. University . of North Carolina, Chapel Hill, USA. http://gamma.cs.unc.edu/gplanner. Presenter: . Liangjun. Zhang, Stanford University. Real-time Motion Planning. Dynamic/uncertain/deformable environments. with Application-Transparent Support . for Multiple Page Sizes. Rachata. . Ausavarungniru. n. ,. . Joshua Landgraf, Vance Miller. Saugata. . Ghose. , . Jayneel. Gandhi, Christopher J. . Rossbach. with Idle GPU Core Resources. Sina. . Darabi. , Mohammad . Sadrosadati. , . Joël . Lindegger. ,. Negar . Akbarzadeh. , . Mohammad Hosseini, . Jisung. Park, . Juan Gómez-Luna, Hamid . Sarbazi. -Azad, .

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