PDF-[READ]-Modern Data Mining Algorithms in C++ and CUDA C: Recent Developments in Feature

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

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[READ]-Modern Data Mining Algorithms in C++ and CUDA C: Recent Developments in Feature: Transcript

The Desired Brand Effect Stand Out in a Saturated Market with a Timeless Brand. 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. © 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. Martin Burtscher. Department of Computer Science. High-End CPUs and GPUs. Xeon X7550 Tesla C2050. Cores 8 (superscalar) 448 (simple). Active threads 2 per core 48 per core. Frequency 2 GHz 1.15 GHz. Principle Component Analysis. Why Dimensionality Reduction?. It becomes more difficult to extract meaningful conclusions from a data set as data dimensionality increases--------D. L. . Donoho. Curse of dimensionality. Håkon Kvale . Stensland. Simula Research Laboratory. PC Graphics Timeline. Challenges. :. Render infinitely complex scenes. And extremely high resolution. In 1/60. th. of one second (60 frames per second). and R Packages. Houtao Deng. houtao_deng@intuit.com. 1. Data Mining with R. 12/13/2011. Agenda. Concept of feature selection. Feature selection methods. The R packages for feature selection. 12/13/2011. Sergei V. Gleyzer. . . Data Science at the LHC Workshop. Nov. . 9. , 2015. Outline. Motivation. What is Feature Selection. Feature Selection. . Methods. Recent work and ideas. Caveats. Nov. 9, 2015. What is CUDA?. Data Parallelism. Host-Device model. Thread execution. Matrix-multiplication . GPU revised!. What is CUDA?. C. ompute . D. evice . U. nified . A. rchitecture. Programming interface to 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. Objects from Satellite Imagery Using Genetic Algorithm By: Eyad A. Alashqar ( 120110378 ) Supervised by: Prof. Nabil M. Hewahi A Thesis Submitted in Partial Fulfillment of the Requirements for the 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 http://www.cs.uic.edu/~. liub. CS583, Bing Liu, UIC. 2. General Information. Instructor: Bing Liu . Email: liub@cs.uic.edu . Tel: (312) 355 1318 . Office: SEO 931 . Lecture . times: . 9:30am-10:45am.

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