Cross Teaching Parallelism and Ray Tracing: A Project based Approach to Teaching Applied Parallel Computing

Transcription

1 and Ray Tracing: A Project based Approach to Teaching Applied Parallel Computing Chris Lupo Computer Science Cal Poly Session 0311 GTC 2012 Slide 1

2 The Meta Data Cal Poly is medium sized, public polytechnic university Primarily undergraduate, with a relatively small Masters program Learn by Doing I am not a Graphics guy Slide 2

3 Outline Background The Learning Objectives The Classroom Experience Results and Learning Outcomes Challenges and Next Steps Conclusions Slide 3

4 Background This experience is about: Two classes combined Applied Parallel Computing Advanced Rendering Cross teaching Two faculty Students teaching students Measuring learning objectives Slide 4

5 Background Summer 2010, Cal Poly became one of the first NVIDIA CUDA Teaching Centers Equipment TA funding Now over 500 universities around the world teaching centers Slide 5

6 Background Curriculum based roughly on material from UIUC. Emphasize performance Emphasize architectural understanding Excellent online resources from NVIDIA and developer forums. At least three good books Plenty of compute. Cal Poly has ~35000 GPU cores GeForce GTX 470/480 general use workstations Quadro 5000 graphics workstations Tesla C2050 compute server Slide 6

7 CUDA Objectives A Systems Approach Thread Model Memory Model Atomics / Critical Sections Control Flow Optimization / Occupancy / Tuning Debug Debug Debug Slide 7

8 The Classroom Experience Two senior level technical electives Applied Parallel Computing Advanced Rendering Combined for weeks 3 5 of 10 week term ~35 students in each course Students worked in groups of 2 or 3, each group has a student from each course Slide 8

9 The Classroom Experience Students in Parallel course exposed to CUDA with Matrix Multiply prior to combining Straightforward to conceptualize Students have prior experience with algorithm Performance impact of parallelization easily seen Forms basis for many other scientific applications Three Parts Sequential CPU OpenMP CPU CUDA GPU Slide 9

10 The Classroom Experience How Matrix Multiply matches with Learning Objectives: Thread model: Multiple block and thread dimensions Memory model: Global and shared, plus registers Occupancy and performance: thread and block optimization Debug Debug Debug Parallelism Strategies Tiling Grid and block size adjustments Correctness! Slide 10

11 The Classroom Experience Slide 11

12 The Classroom Experience Students in Rendering course build basic CPU ray tracer prior to combining Fundamental part of global illumination Become comfortable with the mathematics Develop significant code base for use in remainder of course Slide 12

13 The Classroom Experience Second project is parallel ray tracer Render the bunny 70K triangles 36K spheres anti aliasing Software engineering experience Performance analysis experience Slide 13

14 The Classroom Experience Average team speedup over CPU is ~200 X Best (measurable) speedup is greater than 600 X Slide 14

15 Learning Results Pre and Post surveys given to assess learning objectives: Positive correllations for parallel computing: Able to analyze applications that benefit from parallelism Identify parallel programming paradigms and systems used Identify GPU computing hardware and programming models Able to conduct basic parallel computation with CUDA model Analyze and measure performance of parallel computing systems Determine impact of latency and resource contention on throughput Slide 15

16 Learning Results Pre and Post surveys given to assess learning objectives: Positive correllations for rendering: Understand and apply basic ray tracing algorithms Identify global illumination algorithms Slide 16

17 Learning Results Pre and Post surveys given to assess learning objectives: Non positive correllations: Able to develop a software project in C++ Interested in topics related to rendering and computer graphics Slide 17

18 The New Classroom Experience Rasterization + Gaussian Blur Anti Aliasing Triangle space Image space 9x9 Gaussian blur Speedup ~80X vs. CPU Example: each illuminated point is another rasterized bunny Slide 18

19 Drunken Army Slide 19

20 MandelBunny Slide 20

21 Tilt Shift Bunny Slide 21

22 Cell Wall Bunny Slide 22

23 Challenges Completely new lab infrastructure GPUs, power supplies, OS distro, display drivers, libraries, software tools Course scheduling: lab time, large lecture room Rapidly changing technology: driver updates, tool updates, SDK changes Book had out of date material in week 3 Language difficulties: C++ desired language, but CUDA integration was seriously limited One year later it's quite good Quarter system (10 weeks) leaves no margin Start programming Day 1 Very hard to recover from lateness/errors Slide 23

24 Tips 8 P's Plan, Prepare, Patience, Practice, Practice,... Know your code before trying to demo Encourage excellence Bribery helps, give out prizes for fastest code Slide 24

25 Conclusions Cross teaching was effective for us Basic CUDA can be taught/learned in a short amount of time Ray tracing is a compelling application of parallel computing Though certainly not the only one Slide 25

26 Personal Observations Student engagement was amazing Final projects were awesome Several students claimed they had best experience of college career Fractals, compression, OpenCL, computation finance, game engines, media compression, fluid simulation, N body motion etc. Undergraduate research and publication Interdisciplinary applications My most rewarding teaching experience to date Slide 26

27 Slide 27

28 Extra Slide Projects Final Project Student/Team choice o More advanced ray tracers Monte carlo, reflection/refraction, etc. o Game simulation o Financial modeling o Audio compression (flac) o WebGL o Data Mining (Wikipedia) o OpenCL Slide 28