An Introduction to Modern GPU Architecture Ashu Rege Director of Developer Technology Agenda • Evolution of GPUs • Computing Revolution • Stream Processing • Architecture details of modern GPUs Evolution of GPUs Evolution of GPUs (1995-1999) • 1995 – NV1 • 1997 – Riva 128 (NV3), DX3 • 1998 – Riva TNT (NV4), DX5 • […]
Introduction to OpenCL Cliff Woolley, NVIDIA Developer Technology Group Welcome to the OpenCL Tutorial! OpenCL Platform Model OpenCL Execution Model Mapping the Execution Model onto the Platform Model Introduction to OpenCL Programming Additional Information and Resources OpenCL is a trademark of Apple, Inc. Design Goals of OpenCL Use all computational resources in the system
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Part 5: OpenCLTM C Language Features Built-in Functions 37 OpenCLTM C Language Derived from ISO C99 No standard C99 headers, function pointers, recursion, variable length arrays, and bit fields Additions to the language for parallelism Work-items and workgroups Vector types Synchronization Address space qualifiers Optimized image access Built-in functions 38 Address
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Contents Exploring and Transforming Data I. Univariate Characterizations 1 I.0StatisticalSummary…………………………………….. 1 I.1Histograms…………………………………………. 3 I.2DensityEstimation …………………………………….. 4 I.3QuantilePlots ……………………………………….. 6 I.4Boxplots…………………………………………… 8 II Bivariate Characterizations 10 II.1Scatterplots ………………………………………… 10 II.2JitteringScatterplots ……………………………………. 12 III. Transforming Data 14 III.1Logarithms………………………………………… 14 III.2PowerTransformations ………………………………….. 16 III.3TransformingRestricted-RangeVariables…………………………. 20 III.4TransformationstoEqualizeSpread……………………………. 21 III.5TransformationsTowardLinearity…………………………….. 23 Note: To access your textbook resources type the following
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Contents Mulitple Regression I. Building Models 1 I.1SimlpeModels……………………………………….. 3 I.2ModelswithFactorsandInteractions……………………………. 15 I.2.1AModelwithOneFactor(ANOVA) ………………………… 15 I.2.2 A Model with One Factor and One Quantitative Variable (ANCOVA) . . . . . . . . . . . 18 I.2.3ModelwithManyFactors(MANOVA)……………………….. 20 I.2.4AModelwithManyFactorsandQuantitativeVariables . . . . . . . . . . . . . . .
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11/18/2020 1 2 Example of the Forward Path of a Convolution Layer Application Case Study – Deep Learning Parallel Implementation of Convolutional Neural Network (CNN) (Part 2) 2 752 1*0+ 1*2 + 1*1+ 1*2 + 1*0 + 1*3 2*1 + 2*1 1*0+ 1*2 + 1*0 + 1*3 1 4 Sequential Code for the Forward Path
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Introduction to Machine Learning Assignment 4 1. [ 40 points] Soft-Margin Classifier In class, we considered a “soft-margin” classifier, which can tolerate some misclassified points. Specifically we considered the classifier given by the linear program: such that min ξi w⃗ , b , ξ⃗ i ∀i, yi〈w⃗,x⃗(i)〉+b≥1−ξi, and ∀i, ξi≥0. As we discussed in class,
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int createChan(int msgSize) { get an empty channel descriptor and initialize it; set return value to the index or address of the descriptor; dispatcher(); } proc sendChan(int chan; byte msg[*]) { find descriptor of channel chan; if (blocked list empty) { # save message acquire buffer and copy msg into it; insert buffer at end
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Message Passing • Different from shared memory programming – no shared memory – can’t use simple semaphores, condition vars – can’t use shared buffers, producer/consumer • Communication based on message passing – Process A on machine 1 sends message to process B on machine 2 (over the network) – How does it get there? [we
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Implementing monitors in a single-processor kernel ————————————————– Monitor Entry: Disable interrupts if (!inMonitor) inMonitor := 1 else { Append(entryQ, currentThread) Sleep } Enable interrupts Monitor Exit: Disable interrupts if (isEmpty(entryQ)) inMonitor := 0 else Append(readyQ, Remove(entryQ)) Enable interrupts Wait: Disable interrupts Append(waitQ, currentThread) if (isEmpty(entryQ)) inMonitor := 0 else Append(readyQ, Remove(entryQ)) Sleep Enable interrupts Signal:
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