PDF-[PDF]-Hands-On GPU-Accelerated Computer Vision with OpenCV and CUDA: Effective techniques

Author : janoahjamaahl | Published Date : 2023-03-16

The Desired Brand Effect Stand Out in a Saturated Market with a Timeless Brand

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[PDF]-Hands-On GPU-Accelerated Computer Vision with OpenCV and CUDA: Effective techniques: Transcript

The Desired Brand Effect Stand Out in a Saturated Market with a Timeless Brand. 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. Annie . Yang and Martin Burtscher*. Department of Computer Science. Highlights. MPC compression algorithm. Brand-new . lossless . compression algorithm for single- and double-precision floating-point data. © Dan Negrut, . 2012. UW-Madison. Dan Negrut. Simulation-Based Engineering Lab. Wisconsin Applied Computing Center. Department of Mechanical Engineering. Department of . Electrical and Computer Engineering. Håkon Kvale . Stensland. iAD-lab, Department for Informatics. Basic 3D Graphics Pipeline. Application. Scene Management. Geometry. Rasterization. Pixel Processing. ROP/FBI/Display. Frame. Buffer. Memory. 1. Content. What is . OpenCV. ?. What is face detection and . haar. cascade classifiers?. How to make face detection in Java using . OpenCV. Live Demo. Problems in face detection process. How to improve face detection. 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 . Leiming Yu, Fanny Nina-Paravecino, David Kaeli, Qianqian Fang. 1. Outline. Monte Carlo . eXtreme. GPU Computing. MCX. in OpenCL. Conclusion. 2. Monte Carlo . eXtreme. Estimates the 3D light (. fluence. 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 . Scientific Computing and Visualization. Boston . University. GPU Programming. GPU – graphics processing unit. Originally designed as a graphics processor. Nvidia's. GeForce 256 (1999) – first GPU. Agenda. Text book / resources. Eclipse . Nsight. , NVIDIA Visual Profiler. Available libraries. Questions. Certificate dispersal. (Optional) Multiple GPUs: Where’s Pixel-Waldo?. Text Book / Resources. 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. Cliff Woolley NVIDIADeveloper Technology GroupGPUCPUGPGPU Revolutionizes ComputingLatency Processor Throughput processorLow Latency or High ThroughputCPUOptimized for low-latency access to cached dat The Desired Brand Effect Stand Out in a Saturated Market with a Timeless Brand

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