PPT-Texture Memory -in CUDA Perspective

Author : alida-meadow | Published Date : 2018-10-28

TEXTURE MEMORY IN IN CUDA PERSPECTIVE VINAY MANCHIRAJU TEXTURE MEMORY Read only memory used by programs in CUDA Used in General Purpose Computing for Accuracy

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Texture Memory -in CUDA Perspective: Transcript

TEXTURE MEMORY IN IN CUDA PERSPECTIVE VINAY MANCHIRAJU TEXTURE MEMORY Read only memory used by programs in CUDA Used in General Purpose Computing for Accuracy and Efficiency. 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 Basically a child CUDA Kernel can be called from within a parent CUDA kernel and then optionally synchronize on the completion of that child CUDA Kernel The parent CUDA kernel can consume the output produced from the child CUDA Kernel all withou t heterogeneous programming. Katia Oleinik. koleinik@bu.edu. Scientific Computing and Visualization. Boston . University. Architecture. NVIDIA Tesla M2070: . Core clock: 1.15GHz . Single instruction . 448 CUDA cores . . 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. GPGPU Programming in CUDA. Supada . Laosooksathit. NVIDIA Hardware Architecture. Host. memory. Recall. 5 steps for CUDA Programming. Initialize device. Allocate. device memory. Copy. data to device memory. Lecture . 7: Lab 3 Recitation. Today. Miscellaneous CUDA syntax. Recap on CUDA and buffers. Shared memory for an N-body simulation. Flocking simulations. Integrators. CUDA Kernels. Launching the kernel:. NVIDIA Corporation. Tesla GPU Computing. A Revolution in High Performance Computing. Agenda. CUDA Review. Architecture. Programming Model. Memory Model. CUDA C. CUDA General Optimizations. Fermi. Next Generation Architecture. Proposed Work. This . work aims . to enable efficient dynamic memory management on NVIDIA GPUs by utilizing a sub-allocator between CUDA and the programmer. This work enables Many-Task Computing applications, which need to dynamically allocate parameters for each task, to run efficiently on GPUs.. on . Ubuntu. Cuda. download site. . https://developer.nvidia.com/cuda-downloads. $ . sudo. . dpkg. -. i. cuda-repo-ubuntu1404_7.5-18_amd64.deb . $ . sudo. apt-get update . $ . sudo. apt-get install . introduce the use of multiple CUDA streams to overlap memory transfers with kernel computations.. Also introduced is paged-locked memory. 2. Page-locked host memory. (also called pinned host memory). Blindfold activity. Was it easy or hard to guess what type of fabric it was?. Why? What clues did you have?. Texture. Where is texture found?. In the thickness and appearance of fabric.. What is it?. Programming, Part 3. -- Streaming, Library and Tuning. CSCE 790: Parallel Programming Models for Multicore and . Manycore. Processors. Department of Computer Science and Engineering. Yonghong. Yan. Ajaya. . Neupane. , . Zhiyun. Qian . and . Nael. Abu-. Ghazaleh. University of California, Riverside. Rendered Insecure: . GPU Side Channel Attacks . are Practical. 1. G. raphics . P. rocessing . May 8-Stephen Jones GTC 2018CUDANEW FEATURESAND BEYOND22CUDA ECOSYSTEM 2018CUDADOWNLOADS IN 20173500000CUDA REGISTERED DEVELOPERS800000GTC ATTENDEES80003CUDA DEVELOPMENT ECOSYSTEMFrom Ease of Use to S

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