IAP09 CUDA@MIT 6.963 - Lecture 01: Logistics (Nicolas Pinto, MIT)

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See http://sites.google.com/site/cudaiap2009 and http://pinto.scripts.mit.edu/Classes/CUDAIAP2009

Educação Tecnologia

6.963
IT /
A@M
CUD
9
IAP0

Supercomputing on your desktop:
Programming the next generation of cheap
and massively parallel hardware using CUDA

Lecture 01
Nicolas Pinto (MIT)

Kick - Off session

Still doing your
computations the old way?

Fresh New
Technology
Available NOW!

09)
IAP
(
63
6.9

Course Goals
• Learn how to program massively parallel
processors and achieve
–high performance
–functionality and maintainability
–scalability across future generations
• Acquire technical knowledge required to
achieve the above goals
–principles and patterns of parallel programming
–processor architecture features and constraints
–programming API, tools and techniques
6.963
d for
apte
© David Kirk/NVIDIA and Wen-mei W. Hwu, 2007

ad
ECE 498AL1, University of Illinois, Urbana-Champaign

Class logistics
Teaching Staff (MIT)

GPU Computing with
CUDA David Luebke (NVIDIA)
CUDA Demos
Marc Adams (NVIDIA)

Class logistics
Teaching Staff (MIT)

GPU Computing with
CUDA David Luebke (NVIDIA)
CUDA Demos
Marc Adams (NVIDIA)

High-Throughput
Scientiﬁc Computing
Hanspeter Pﬁster (Harvard)

af f
St
ing
ach
Te

Faculty: Prof. Steven G. Johnson

af f
St
ing
ach
Te

TAs: Justin Riley and Nicolas Poilvert

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St
ing
ach
Te

Instructor: Nicolas Pinto
Contact: pinto@mit.edu

ule
ed
ch
S
Lectures: M/W/F 10-12 (#32-155)
HandsOn: M/W/F 2-5 (#32-141)

Project Hours: T/R 2-5 (#3-370)

ule
ed
ch
S
/ CUDA Basics
/
/ CUDA Advanced
/
/ Theory
/
/ Case Studies
/
/ Projects
/

are
rdw
Ha

$70,000+
from NVIDIA, Rowland/Harvard and MIT
(OEIT, DiCarlo Lab, Graphics CSAIL, EECS)

ect
oj
Pr
he
T Project Presentations
@the_end_of_the_course
MIT
6.963

DO
TO
1) Discussion Group
2) Team Project
3) Assignments
4) Enjoy!
Contact: pinto@mit.edu

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IAP09 CUDA@MIT 6.963 - Lecture 01: Logistics (Nicolas Pinto, MIT)

1. 6.963 IT / A@M CUD 9 IAP0 Supercomputing on your desktop: Programming the next generation of cheap and massively parallel hardware using CUDA Lecture 01 Nicolas Pinto (MIT) Kick - Off session

2. Solve Tomorrow’s Problems, Today!

3. Need More Throughput?

4. Still doing your computations the old way?

5. Tired Of Waiting For Your Computations?

6. HPC has changed. Did You?

7. Fresh New Technology Available NOW! 09) IAP ( 63 6.9

9. Guaranteed

10.

11.

12. Course Goals • Learn how to program massively parallel processors and achieve –high performance –functionality and maintainability –scalability across future generations • Acquire technical knowledge required to achieve the above goals –principles and patterns of parallel programming –processor architecture features and constraints –programming API, tools and techniques 6.963 d for apte © David Kirk/NVIDIA and Wen-mei W. Hwu, 2007 ad ECE 498AL1, University of Illinois, Urbana-Champaign

13. Today yey!!

14.

15. Class logistics Teaching Staff (MIT)

16. Class logistics Teaching Staff (MIT) GPU Computing with CUDA David Luebke (NVIDIA) CUDA Demos Marc Adams (NVIDIA)

17. Class logistics Teaching Staff (MIT) GPU Computing with CUDA David Luebke (NVIDIA) CUDA Demos Marc Adams (NVIDIA) High-Throughput Scientiﬁc Computing Hanspeter Pﬁster (Harvard)

18. Some Logistics...

19. af f St ing ach Te Faculty: Prof. Steven G. Johnson

20. af f St ing ach Te TAs: Justin Riley and Nicolas Poilvert

21. af f St ing ach Te Instructor: Nicolas Pinto Contact: pinto@mit.edu

22. ule ed ch S Lectures: M/W/F 10-12 (#32-155) HandsOn: M/W/F 2-5 (#32-141) Project Hours: T/R 2-5 (#3-370)

23. ule ed ch S / CUDA Basics / / CUDA Advanced / / Theory / / Case Studies / / Projects /

24. ces ur eso R

25. ces ur eso R

26. are rdw Ha 30+ GPUs

27. are rdw Ha 19 MacBook Pro

28. are rdw Ha $70,000+ from NVIDIA, Rowland/Harvard and MIT (OEIT, DiCarlo Lab, Graphics CSAIL, EECS)

29. The “Project”

30.

31. (s) ect oj Pr he T

32. ct oje Pr he T

33. ect oj Pr he T Project Presentations @the_end_of_the_course MIT 6.963

34. ion tit pe om C

35. onal Pers ifts ter G mpu rco Supe

36. DO TO 1) Discussion Group 2) Team Project 3) Assignments 4) Enjoy! Contact: pinto@mit.edu

37.

38. ME CO

IAP09 CUDA@MIT 6.963 - Lecture 01: Logistics (Nicolas Pinto, MIT)

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