CHEN, Xinyu - HKUST Class Schedule & Quota

Class Schedule & Quota
2025-26 Spring 2025-26 Winter 2025-26 Fall 2024-25 Summer
2025-26 Spring
Common Core Course
The class information on this page will normally be updated every 15 to 20 minutes during class enrollment period.
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This service was first adapted from a student contribution in 2011-12 Fall. The efforts of all concerned are duly acknowledged.

AIAA AMAT BSBE CMAA CNCC CNGF DLED DSAA EOAS FTEC FUNH INFH INTR IOTA IPEN LANG MICS MSSM PDEV PLED ROAS SEEN SMMG SOCH SYSH UCMP UCUG UFUG UGOD

COURSE INFO

VECTOR	[0-1-0:0]
DESCRIPTION	Seminar topics presented by students, faculty and guest speakers. Students are expected to attend regularly and demonstrate proficiency in presentation in accordance with the program requirements. Graded P or F.

FUNH 6800J - Function Hub Seminar (0 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
T03 (6067)	We 06:00PM - 06:50PM	Rm 101, W1	CHEN, Sihan CHEN, Xinyu XU, Jiang	120 Quota/Enrol/Avail Y1 MPhil and MICS RPG students: 120/0/120	0	120	0	This section is for PhD(MICS), MPhil (MICS) and Year 1 MPhil only.

COURSE INFO

VECTOR	[3-0-0:3]
DESCRIPTION	This course introduces the fundamentals of GPU-accelerated computing, focusing on how GPUs enable high-performance solutions for complex tasks in fields like AI, data analytics, and scientific simulations. Students will explore GPU architectures, learn parallel programming models such as CUDA and ROCM, and develop skills in memory management, performance optimization, and thread synchronization. Through hands-on projects, participants will gain practical experience in leveraging GPU parallelism to solve real-world computational challenges, preparing them to apply these techniques in industries.

VECTOR

[3-0-0:3]

DESCRIPTION

This course introduces the fundamentals of GPU-accelerated computing, focusing on how GPUs enable high-performance solutions for complex tasks in fields like AI, data analytics, and scientific simulations. Students will explore GPU architectures, learn parallel programming models such as CUDA and ROCM, and develop skills in memory management, performance optimization, and thread synchronization. Through hands-on projects, participants will gain practical experience in leveraging GPU parallelism to solve real-world computational challenges, preparing them to apply these techniques in industries.

MICS 6001V - Accelerated Computing with GPUs (3 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
L01 (6098)	Th 09:00AM - 11:50AM	Rm 101, W2	CHEN, Xinyu	20	0	20	0