PPT-Orchestrating Multiple Data-Parallel Kernels on Multiple De

Author : faustina-dinatale | Published Date : 2017-12-27

Janghaeng Lee Mehrzad Samadi and Scott Mahlke October 2015 University of Michigan Ann Arbor University of Michigan Electrical Engineering and Computer

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Orchestrating Multiple Data-Parallel Kernels on Multiple De: Transcript


Janghaeng Lee Mehrzad Samadi and Scott Mahlke October 2015 University of Michigan Ann Arbor University of Michigan Electrical Engineering and Computer Science Financial Modeling. Solar . Radiative. Kernel. s. And Applications. Zhonghai Jin. Constantine . Loukachine. Bruce . Wielicki. Xu. Liu. SSAI, Inc. / NASA . Langley research . Center. July 6-9, 2010. Objective:. . Introduce the reflected solar spectral kernels, their spectral characteristics, and the potential applications to CLARREO . Stephen Mayhew. Hyung Sul Kim. 1. Outline. What are features?. How are they defined in NLP tasks in general?. How they are defined specifically for relation extraction. ? (Kernel methods). 2. What are features?. Popcorn project. Problem. What brand of microwave popcorn do you get your moneys worth?. Variables. Same size bags of popcorn were popped, Each bag was popped 2 minutes 10 seconds. All bags were butter lovers. with Multiple Labels. Lei Tang. , . Jianhui. Chen and . Jieping. Ye. Kernel-based Methods. Kernel-based methods . Support Vector Machine (SVM). Kernel Linear Discriminate Analysis (KLDA). Demonstrate success in various domains. April 2016. Agenda. Overview. Kernel architecture. Producing kernels. Using kernels. Introduction to Kernels. 2. Introduction to Kernels. 3. What is a SPICE “Kernel”. “Kernel” means file. “Kernel” means a file . Ross McIlroy, Chris Hawblitzel, Galen Hunt. Microsoft Research. Helios: Heterogeneous Multiprocessing with Satellite Kernels. 1. Problem: HW now heterogeneous. Heterogeneity . ignored. by operating systems. Specialization of Kernelization. Daniel Lokshtanov. We. Kernels. ∃. ¬. . Kernels. Why?. What’s Wrong with Kernels. (from a practitioners point of view). Only handles . NP-hard. problems.. Don’t combine well with . Pipeline . Parallelism from . Multiple . Dependent Kernels for . GPUs. Gwangsun Kim, . Jiyun Jeong, John Kim. Mark Stephenson. GPU Background. CTA. Kernel grid. CTA (Cooperative Thread Array). or Thread block. Machine Learning. March 25, 2010. Last Time. Basics of the Support Vector Machines. Review: Max . Margin. How can we pick which is best?. Maximize the size of the margin.. 3. Are these really . “equally valid”?. Machine Learning. March 25, 2010. Last Time. Recap of . the Support Vector Machines. Kernel Methods. Points that are . not. linearly separable in 2 dimension, might be linearly separable in 3. . Kernel Methods. :. Native & Convenient Heterogeneous. Computing for D. Outline. Introduction. Compiler. Libraries. Using . DCompute. (present and future). Future directions. State of Hardware. X86 . –. all compilers. Dr Susan Cartwright. Dept of Physics and Astronomy. University of Sheffield. Parallel Universes. Are you unique?. Could there be another “you” differing only in what you had for breakfast this morning?. Ke Wang. Sparse Correspondence Problems. Dense Correspondence Problems. Stereo. Motion. Motion vs. Stereo: Differences. Motion: . Uses velocity: consecutive frames must be close to get good approximate time derivative. Table of Contents. Overview of Evolutionary Computation. Programming Strategies. Related Architecture. Efficiency Comparison. Implementations. Conclusion. Origins. Based on the study of evolution by Charles Darwin.

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