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Parallel
Programming & Architectures |
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Overview: Parallel
processing is about accelerating heavy computations, for instance, in
artificial intelligence, machine learning, big data processing, scientific simulations,
signal processing, and autonomous robotics. Parallel platforms range from
large scale systems to tiny wearable devices. This course serves as an
introduction to the world of parallel computing and covers algorithm
development, programming techniques, and architectures. |
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Example
Applications: |
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Big
Data, Heavy
Simulations
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Large Scale
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Machine
Learning, Signal
Processing, Autonomous
Robotics
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Desktop, Embedded
Systems
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IoT, Wearable
Devices
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Ultra
Low Power
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Prerequisite: C Programming |
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Syllabus: 1.
Motivation and demanding
applications 2.
Types of parallelism: ILP, DLP,
TLP 3.
Components of parallel
architectures 4.
Process scheduling 5.
Shared-memory multi-threading 6.
Pthread library 7.
Busy waiting, mutex,
semaphore 8.
SIMD hardware and programming 9.
GPGPU architecture 10.
CUDA programming model 11.
Data-parallel algorithms 12.
Algorithm optimization techniques 13.
Other frameworks such as OpenCL |
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References: 1.
Peter Pacheco, An
Introduction to Parallel Programming, 2nd Edition (2020) or 1st Edition
(2011). 2.
Nicholas Wilt, The
CUDA Handbook, 2nd Edition (2020) or 1st Edition (2013). |
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Grading: Exam (12),
Programming Exercises (8) |
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Instructor: Matin Hashemi |
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