PPT-Mosaic: A GPU Memory Manager

with ApplicationTransparent Support for Multiple Page Sizes Rachata Ausavarungniru n Joshua Landgraf Vance Miller Saugata Ghose Jayneel Gandhi Christopher J Rossbach. . 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, . 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. 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. 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 Sitaridi, . Thesis Defense. Columbia University . GPUs for Social Media Analytics. 2. Search terms: . Match . regexp. : “/\B#\w*[a-. zA. -Z] \w*/ . . debate. Filter location. More GPU Data Analytics Use-Cases. 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. Hui. Li. Geoffrey Fox. Research Goal. provide . a uniform . MapReduce programming . model that works . on HPC . Clusters or . Virtual Clusters cores . on traditional Intel architecture chip, cores on . CS 179: GPU Programming Lecture 7 Last Week Memory optimizations using different GPU caches Atomic operations Synchronization with __ syncthreads () Week 3 Advanced GPU-accelerable algorithms “Reductions” to parallelize problems that don’t seem intuitively parallelizable CS 179: GPU Programming Lecture 7 Week 3 Goals: Advanced GPU- accelerable algorithms CUDA libraries and tools This Lecture GPU- accelerable algorithms: Reduction Prefix sum Stream compaction Sorting (quicksort) Martin Burtscher. Department of Computer Science. High-end CPU-GPU Comparison. . Xeon 8180M. . Titan V. Cores 28 5120 (+ 640). Active threads 2 per core 32 per core. Frequency 2.5 (3.8) GHz 1.2 (1.45) GHz. Refers to the gluing of small pieces of only one type of material onto a surface to create/form a composition. The variations may be in size and color of the material. The small pieces of material that are glued onto a surface to create mosaic work are called .