We will provide a dataset ‘data.csv’, which can be used to test your program. It is a 2-category, 10- feature dataset with 5×105samples. 1 Random forest – Wikipedia In order to implement a parallel random forest, the following tasks should be accomplished: · Decision tree (without stop-split-early condition) It includes the following components: 1) Calculate […]
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Assignment 1 Final See the page Assignment 1 Introduction for an overview. You should finish the Milestone before beginning this part. Your task here is to write an MPI program for a set of tasks communicating in a ring topology. On page 37 B of the textbook, you can see a picture of processors connected
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Parallel Design Patterns Assessed Coursework, 2023 The deadline for PART TWO of the assessed coursework is Thursday 6th of April at 4pm. You will submit this via the PDP learn pages. About the coursework Part two of the coursework follows on from your initial work on part one. However, these two pieces are marked independently
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Machine Problem 3 Matrix Multiplication The objective of this machine problem is to implement a dense matrix multiplication routine with different number of blocks and threads per block. It is also to understand the impact of data transfer time on performance. A matrix multiplication takes two input matrices M and N and produces an output
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Machine Problem 2 Matrix Addition The objective of this machine problem is to implement matrix addition routines, where each thread produces one or more output matrix elements. It is also to get you familiar with using the CUDA API and the associated setup code. A matrix addition takes two input matrices A and B and
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Machine Problem 1 Device Query The objective of this machine problem is to understand the capabilities of the NVIDIA GPU you will be working with for your machine problems. You will also test your environment and make sure you can build and run your CUDA programs. Write a code that could identify the number and
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Machine Problem 4 Tiled Matrix Multiplication The objective of this machine problem is to get you familiar with using shared memory to write optimized kernel algorithms by implementing a “tiled” version of matrix multiplication. A matrix multiplication takes two input matrices M and N and produces an output matrix P. For simplicity, we will only
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Project 4: Tree reduction and stencil computation in GPU (100 points) Submission guidelines: You will submit 2 files in Canvas. · a PDF report file (filename: Lastname_Firstname_Project_4_Report.pdf) · a ZIP file (filename: Lastname_Firstname_Project_4.zip) containing: · Problem_1/ · gpu_mining_problem1.cu · nonce_kernel.cu (This is provided) · hash_kernel.cu (Write this) · Makefile · support.cu (If you use it
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COMP 4446 5046 Lab02 PyTorch is an open source machine learning library used for applications such as natural language processing and computer vision. It is based on the Torch library. Before we use Pytorch it is neccessary to understand what Pytorch is. Let’s start from the core concepts: Tensor, (Computational) Graph and Automatic Differentiation A
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CUDA MatrixMult Design and implement C and CUDA programs that called MatrixMult.c, MatrixMultNaive.cu, and MatrixMultTiled.cu. The MatrixMult.c, MatrixMultNaive.cu, and MatrixMultTiled.cu programs will use a command line argument that corresponds to the row/column length to multiply two square matrices with random single precision numbers (i.e., floats). They will then output 1) the entire execution time in