Advanced Parallel Computing (240199)
Classes(Fall, 2020) - Class overview (PDF)
Online class
Instructor: Duksu Kim (bluekds at koreatech.ac.kr / #435, 2nd Eng. Building)
Office hour : Mon. 14:00~16:00
Prerequisite
(Required) C Programming
(Strongly Recommended) Multi-core Programming (Undergraduate level)
(Recommended) System Programming, Data structure
(Required) PC or Laptop with a multi-core CPU / (Recommended) PC or Laptop with a NVIDIA GPU
We will rent a development kit (e.g., Jetson kit) for CUDA if you need
However, you need to prepare a monitor and a keyboard/mouse yourself to use that.
Textbooks
Setup CUDA Dev. environments
Windows Dev. environments [Kor]
Linux(Ubuntu) Dev. environments on Jetson Kit [Kor]
Trouble shooting
Q. My laptop has a Nvidia GPU, but CUDA does not work properly
A. Check the GPU system on your laptop whether a hybrid GPU system (e.g., Intel HD graphics + Nvidia GPU)
In this case, disabling the intel GPU on the device manager of you OS may fix the problem
Lecture Notes and Videos
Lecture 1. Parallel Processing Overview (9/2)
What is and Why Parallel Computing
Parallel Program Performance
Parallel Program Design
Parallel Processing Hardware
Heterogeneous Computing
Contents
Lecture 2-3. OpenMP Overview (9/9, 9/16)
OpenMP introduction
Parallel construct
Work-sharing construct
Scope of Variables
Synchronization construct & Locks
Nested parallelism
Contents
Lecture 4. CUDA Overview I (9/23)
Introduction to GPGPU
Hello CUDA
Basic Workflow of CUDA
CUDA Thread Hierarchy
Organizing Threads
Contents
Lecture 5. CUDA Overview II (9/30)
CUDA Execution Model
CUDA Memory Model & Performance
Using Shared Memory
Maximizing Memory Throughput
Contents
Lecture 6. CUDA Overview III (10/7)
Synchronization
CUDA Stream & Concurrent Execution
CUDA Event
Multi-GPUs and Heterogeneous Computing
Contents
Lecture slides
Paper Seminar (10/21)
Fast Filtering of LiDAR Point Cloud in Urban Areas Based on Scan Line Segmentation and GPU Acceleration [paper]
Energy-efficient excution of data-parallel application on heterogeneous mobile platforms [paper]
Safety view management for augmented reality based on MapReduce strategy on multi-core processors [paper]
Real-Time Face Detection and Tracking Utilising OpenMP and ROS [paper]
[Project] Proposal presentation (10/28)
Paper Seminar (11/04)
M-DTM: Migration-based dynamic thermal management for heterogeneous mobile multi-core processors [paper]
Parallel Processing for Data Deduplication [paper]
P Sobe et al, PARS-Mitteilungen, 2015
Presented by In-Chul Hwang (Slides) (Video)
Robust Dynamic Resource Allocation via Probabilistic Task Pruning in Heterogeneous Computing Systems [paper]
Parallel K Nearest Neighbor Matching for 3D Reconstruction [paper]
Paper Seminar (11/11)
Parallel Scheduled Sampling [paper]
Duckworth et al., arXiv:1906.04331, Jun 2019
Presented by Jin-Hwan Kim (Slides) (Video)
Neural Network Implementation using CUDA and OpenMP [paper]
Jang et al., Digital Image Computing: Techniques and Applications, 2008
Presented by Jae-Min Sa (Slides) (Video)
SandTrap: Trackiing information flows on demand with parallel permissions [paper]
Razeen et al, MobiSys, 2018
Presented by Euihyeok Lee (Slides) (Video)
[Project] Midterm presentation (11/18)
Paper Seminar (11/25)
CPU and GPU Parallel Processing for Mobile Augmented Reality [paper]
Baek et al., International Congress on Image and Signal Processing (CISP), 2013
Presented by Juhwan Lee (Slides) (Video)
Light Field Depth Estimation on Off-the-Shelf Mobile GPU [paper]
Ivan et al, CVPR Workshops, 2018
Presented by Ye-Chan Choi (Slides) (Video)
DeepSense: A GPU-based Deep Convolutional Neural Network Framework on Commodity Mobile Devices [paper]
Huynh et al., Workshop on Wearable Systems and Applications, 2016
Presented by Sang-Won Hwang (Slides) (Video)
12/02 - No class
[Project] Final presentation (12/09, 12/16)
Paper Seminar (Pending)
Performance and Scalability of GPU-based Convolutional Neural Networks [paper]
Strigl et al., IEEE Euromicro Conference on Parallel, Distributed and Network-based Processing, 2010
Presented by Joon-Ho Park (Slides) (Video)
Flexible, High Performance Convolutional Neural Networks for Image Classification [paper]
Curesan et al., Twenty-Second International Joint Conference on Artificial Intelligence, 2011
Presented by Joon-Ho Park (Slides) (Video)