![]() ![]() The course adds material on instruction level parallelism, including pipelining and vectorization.Supercomputing and scientific/numerical applications will be deemphasized.The course will not cover GPU programming, GPUs, or machine learning frameworks, such as TensorFlow, Keras, and PyTorch.Examples will be drawn from machine learning and data science as much as possible.The new syllabus changes the focus of the course: This course replaces Parallel Programming as it was taught from 2013–2021. Data-parallel distributed computing: dask, spark.Synchronization and concurrency control.Shared-memory parallelism and programming with threads.Machine learning in Python: scikit-learn.Data science in Python: dataframes, numpy, scipy.Computer Systems Fundamentals (EN 601.333 or the equivalent).Data Structures (EN 601.226 or the equivalent).Intermediate Programming (EN 601.120 or the equivalent).The course is suitable for second-year undergraduate CS majors and graduate students from other science and engineering disciplines that have prior programming experience. The course will not cover GPU deep-learning frameworks nor CUDA. The course will cover modern data-parallel programming frameworks, including Dask, Spark, Hadoop!, and Ray. Concepts from computer architecture and operating systems will be developed in support of parallelism, including Moore’s law, the memory hierarchy, caching, processes/threads, and concurrency control. It explores parallelism at all levels, including instruction level parallelism (pipelining and vectorization), shared-memory multicore, and distributed computing. It focuses mostly on the Python programming ecosystem but will use C/C++ to accelerate Python and Java to explore shared-memory threading. This course studies parallelism in data science, drawing examples from data analytics, statistical programming, and machine learning. Note to students trying to enrollĪll course enrollment prior to the first day of classes 2022 will be conducted through SIS.įor students eligible to enroll through SIS, requests for permission to enroll based onĮxceptional circumstances or requests to be promoted from the wait list will not be granted.Įmails requesting such permissions will not receive replies. The material on this page mirrors that information. Parallel Computing for Data Science Fall 2022 (EN 601.420/620) ![]()
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