QUIZ 03 QUIZ 03 Say it if you know it CVSS Temporal Exploit Code Maturity Exploit code maturity answers the question, “Is this exploit being used in the wild?” Many exploits are only theoretical in nature, and never actually get exploited by adversaries. Others get exploited, but code to operationalize those exploits never gets widely distributed, […]
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GPU Programming – Exercise 2: Reductions 1 Introduction This exercise is about how to perform reductions, in particular to perform dot products of really large vectors. Operations such as these are very common, but represent a challenge for many-core systems, such as GPUs. As long as sub-tasks can be performed in isolation, it is usually
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Parallel Memory Models CMPSC 450 Taxonomy of Parallel Computing Paradigms • SIMD – Single Instruction Multiple Data – A single instruction pipeline applied to multiple compute elements. Ex: Vector Processors, GPU Processing, MMX, SSE, AVX instruction sets. • MIMD – Multiple Instruction Multiple Data – Multiple instruction pipelines are working on multiple data streams concurrently.
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CUDA CMPSC 450 What is CUDA • Compute Unified Device Architecture • An extension of the C programming language created by nVidia. • Enables GPUs to execute programs written in C in an integrated host (CPU) + device (GPU) app C program • Execute “kernels” as a SIMT program • A dedicated hardware solution CMPSC
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Prefix Sums CMPSC 450 Definition: The all-prefix-sums operation takes a binary associative operator , and an ordered set of n elements and returns the ordered set [a0, a1, …, an−1], [a0,(a0 a1), …,(a0 a1 … an−1)]. CMPSC 450 Serial example • Make binary-associative operator ‘+’ b[0] = a[0]; for (i = 1; i < n;
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CPSC 425: Computer Vision Assignment 6: Deep Learning Attribution: This assignment is developed based on the example here. Preface This assignment consists of three parts: In the first part, you will implement various PyTorch deep learning layers using Numpy; in part two, you will experiment with different hyper-parameters on a image classification task and find
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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License • The OpenCL programming language can run on NVIDIA GPUs, AMD GPUs, Intel CPUs, Intel GPUs, mobile devices, and (supposedly) FPGAs (Field-Programmable Gate Arrays). • But, OpenCL is at its best on compute devices with large amounts of data parallelism, which usually implies
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In [ ]: import torch import torchvision import torchvision.transforms as transforms import torch.nn as nn import torch.optim as optim import numpy as np import matplotlib.pyplot as plt from torch.utils.data import random_split, Dataset Load data with pytorch In [ ]: # Set up config variables config = { ‘data_path’: ‘./data’, # directory path of dataset, } In [ ]: # Import MNIST
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10-601 Introduction to Machine Learning Machine Learning Department School of Computer Science Carnegie Mellon University Logistic Regression + Feature Engineering + Regularization Matt Gormley Lecture 11 Mar. 3, 2021 1 Reminders • Homework 3: KNN, Perceptron, Lin.Reg. – Out: Mon, Feb. 22 – Due: Mon, Mar. 01 at 11:59pm – IMPORTANT: you may only use
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Assignment_4_2021_WIN_4MLS In [ ]: import torch import torchvision import torchvision.transforms as transforms import torch.nn as nn import torch.optim as optim import numpy as np import matplotlib.pyplot as plt from torch.utils.data import random_split, Dataset Load data with pytorch¶ In [ ]: # Set up config variables config = { ‘data_path’: ‘./data’, # directory path of dataset, } In [ ]: # Import
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