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19-475-0502 PARALLEL COMPUTING FOR DATA SCIENCE
Core/Elective: Elective Semester: 5 Credits: 4
Course Description

This course is to discuss exclusively on parallel data structures, algorithms, software tools, and applications in data science. It includes examples not only from the classic "n observations, p variables" matrix format but also from time series, network graph models, and numerous other structures common in data science. With the main focus on GPU based computation, the examples illustrate the range of issues encountered in parallel programming.

Course Objectives

To gain a working knowledge of parallel programming with data sets
To develop programming skills required for parallel computing
To know advanced datastructures required for efficient data processing

Course Content

Module I
Parallel computing: languages and models for parallelism - Sequential vs parallel: concurrent, parallel, distributed - parallel hardware architecture - modifications to the von Neumann Model -Evolution of GPU - GPGPU - introduction to data parallelism - CUDA program structure - vector addition kernel - device global memory and data transfer

Module II
CUDA thread organization - mapping threads to multi-dimensional data - assigning resources to blocks - synchronization and transparent scalability - thread scheduling and latency tolerance -Memory access efficiency - CUDA device memory types - performance considerations - global memory bandwidth - instruction mix and thread granularity -floating point considerations

Module III
Parallel programming patterns: convolution - prefix sum - sparse matrix and vector multiplication -application case studies - strategies for solving problems using parallel programming

Module IV
Parallel Patterns: merge sort, sequential and parallel approaches, co-rank function implementation, basic parallel merge kernel – Graph search: sequential BFS, parallel BFS, optimizations

Module V
CUDA dynamic parallelism: example for dynamic parallelism, memory data visibility, configurations and memory management, synchronization, streams and events

REFERNCES

1. David B. Kirk, Wen-mei W Hwu; Programming Massively Parallel Processors, 3 ed, Morgan Kaufmann, 2016
2. Peter Pacheco, An Introduction to Parallel Programming, Morgan Kaufmann, 2011
3. Norman Matloff; Parallel Computing for Data Science, 1 ed, CRC Press, 2015


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