PPT-GPU-Acceleration of In-Memory Data Analytics 

Evangelia Sitaridi Thesis Defense Columbia University GPUs for Social Media Analytics 2 Search terms Match regexp Bwa zA Z w debate Filter location More GPU Data Analytics UseCases. . Acknowledgement: the lecture materials are based on the materials in NVIDIA teaching center CUDA course materials, including materials from Wisconsin (. Negrut. ), North Carolina Charlotte (. Wikinson. 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, . Instructor Notes. This lecture begins with an example of how a wide-memory bus is utilized in . GPUs. The impact of memory coalescing and memory bank conflicts are also described along with examples on current hardware. By . Ishtiaq. . Hossain. Venkata. Krishna . Nimmagadda. Application of Jacobi Iteration. Cardiac Tissue is considered as a grid of cells.. Each GPU thread takes care of voltage calculation at one cell. This calculation requires Voltage values of neighboring cells. ITK v4 . . w. inter . meeting. Feb 2. nd. 2011. Won-. Ki. . Jeong. , . Harvard University. (. wkjeong@seas.harvard.edu. ). Overview. Introduction. Current status in GPU ITK v4. GPU managers. GPU image. 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. 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. 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. 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. A Tale of Two Cities: GPU Computing and Machine Learning Dr. Xiaowen Chu Department of Computer Science, Hong Kong Baptist University Outline 2 Some Stories of “Two Cities” Evolution of CPUs/ Scheduling Techniques for GPU Architectures with Processing-In-Memory Capabilities Ashutosh Pattnaik Xulong Tang, Adwait Jog, Onur Kay ı ran, Asit Mishra, Mahmut Kandemir , Onur Mutlu with Idle GPU Core Resources. Sina. . Darabi. , Mohammad . Sadrosadati. , . Joël . Lindegger. ,. Negar . Akbarzadeh. , . Mohammad Hosseini, . Jisung. Park, . Juan Gómez-Luna, Hamid . Sarbazi. -Azad, .