1. December 13, 20041 G raphical A symmetric P rocessing Prototype Presentation December 13, 2004

2. 2 Team Organization

3. December 13, 20043 http://www.apple.com/education/science/profiles/vatech/

4. December 13, 20044 http://www.apple.com/education/science/profiles/vatech/

5. December 13, 20045 http://www.apple.com/education/science/profiles/vatech/

6. December 13, 20046 Problem Statement Computationally intensive environments underutilize Graphical Processing Units.

7. December 13, 20047 Background Information Discussed since 1996, but never implemented. GPU Performance –Multiplied at a rate of 2.8 times per year since 1993 –Expected to increase at this rate for another 5 years The more performance increases the more helpful our product becomes http://www.computer.org/computer/homepage/1003/entertainment

8. December 13, 20048 Solution: G.A.P. Create a usable, extendable, and maintainable API to leverage the unused computing power of graphics processors that will result in increased performance of scientific, database, and other processor- intensive applications.

9. December 13, 20049 Solution: G.A.P. Utilizing existing hardware: –Improve computing power –Improve computing time –Improve computing responsiveness

10. December 13, 200410 Solution Implementation By creating a: –SDK to utilize the GPU –Selling that SDK to NVIDIA

11. December 13, 200411 What is an SDK? S.D.K. – Software Development Kit A set of programs that allows software developers to create products to run on a particular platform or to work with an API. Include: Manual, Examples, Libraries Other examples, both free and commercial: –Java, OS/2, AW, Windows, DirectX

12. December 13, 200412 Phase 1 Product Goals Demonstrate amount of power in current GPUs –Also: Ability to utilize power Secure funding to continue development Secure interested parties – universities and research labs Take first steps towards NVIDIA partnership

13. December 13, 200413 Phase 1 Product Objectives Leverage the GPU for additional power Improve throughput on workstation machines Ease programming difficulty for utilizing the GPU Maintain current program compatibility Preserve system stability

14. December 13, 200414 Product Risks & Mitigations Vendor Support –NVIDIA sets aside $1billion to use on Acquisitions R&D Writing the Software –Time intensive product “Build first and optimize later”

15. December 13, 200415 Product Functional Diagram

16. December 13, 200416 Product Dataflow Diagram

17. December 13, 200417 Product Dataflow Diagram

18. December 13, 200418 Product Dataflow Diagram

19. December 13, 200419 Product Dataflow Diagram

20. December 13, 200420 Dataflow Diagram for Product

21. December 13, 200421 Prototype

22. December 13, 200422 Navier-Stokes Equations used to refer to the incompressible form of the momentum equation. a full and general set of differential equations governing the motion of a fluid http://www.navier-stokes.net/nsdef.htm

23. December 13, 200423 Navier-Stokes Equations Simulation of Fluid Like Behavior –Example of applications used within Computational Intensive Environments –Multiple Old Dominion PHD candidate’s thesis topics focus on Navier Stokes Will serve as a basis application to prove efficiency of GPU over CPU –Shows an average 60% gain in efficiency

24. December 13, 200424 Prototype Functional Diagram

25. December 13, 200425 Dataflow Diagram for Prototype

26. December 13, 200426 Dataflow Diagram for Prototype

27. December 13, 200427 Demonstration Two versions of an executable –CPU vs GPU Navier Stokes on a vector field with four jets –Demonstration will consist of firing the jets for different lengths of time and observing performance –Observe CPU alone –Observe GPU alone –Observe Simultaneously

28. December 13, 200428 On the CPU

29. December 13, 200429 With GAP on the GPU

30. December 13, 200430 Risks Main research issues include quality of floating point –The numbers are ‘single precision’ not double. Works best when ‘batched,’ which requires a relatively ‘parallel’ system –Already a multithreading issue. Solutions both in programmer practice and compiler design exist.

31. December 13, 200431 Risks Mitigated (Prototype) Floating Point Quality: –Distributed the field thickly enough that floating point was accurate. Batching: –Used “Stream” operator that ensured a command size was sufficient before it flushed the results.

32. December 13, 200432 Risk Mitigation (Product) Floating Point –NVIDIA says cards will include double precision upon demand –NVIDIA partnership will expedite. Batching –The Context system has an internal, self optimizing queue, with the “flush” instruction for programmer flexibility.

33. December 13, 200433 Testing and Evaluation 20 Frames to 1 “real world second” –Translates:.75-1.75 speed on GPU –Faster than a “real world second”!.025-.25 speed on CPU

34. December 13, 200434 Suitability What does this prove? –Gives magnitude of performance increase –Efficiency gain with no new hardware –“Real world” problem solved –Standard interface any program could use

35. December 13, 200435 Degree of Completeness Similarities Prototype General access functions “Context” based input Demonstrated performance gain Utilizes GPU for as much work as possible Release General access functions “Context” based input Demonstrated performance gain Utilizes GPU for as much work as possible

36. December 13, 200436 Degree of Completeness Differences Prototype Specific to GF5 platform Limited GAP Commands “All or Nothing” GPU use Release General platform Wide array of GAP commands Dynamic GPU use based on capabilities

37. December 13, 200437 Budget Reports

38. December 13, 200438 Phase I Funding Phase I SBIR –Completed at the end of Phase 0

39. December 13, 200439 Phase I Budget Staff

40. December 13, 200440 Phase I Budget

41. December 13, 200441 Major Milestones Phase I Organize Project Group Produce Project Descriptive Paper Develop Contracts Produce Budget White Paper Produce Project User Manual Develop Prototype Produce SBIR Phase II Proposal Produce Project Website

42. December 13, 200442 Phase I Schedule

43. December 13, 200443 Phase II Funding Phase II SBIR –Completed at the end of Phase I

44. December 13, 200444 Phase II Budget Staff *4 programmers needed

45. December 13, 200445 Patent Acquisition

46. December 13, 200446 Phase II Budget * Purchased in Phase 1

47. December 13, 200447 Major Milestones Phase II Production Marketing Legal Negotiation Final Preproduction Alterations

48. December 13, 200448 Phase II Schedule

49. December 13, 200449 Phase III We plan to sell the product to NVIDIA at the end of Phase II Doing so would mitigate all responsibilities and risk factors that may arise on the market –While we increase the companies profit by over $6.5 million

50. December 13, 200450 Profit Margin/Break Even Immediate Profit $70 million average profit for acquisitions –If we obtain 1/10(average) –We would still make a $6.5 million gain http://nvidia.com/object/IO_20010612_6602.html http://nvidia.com/object/IO_8086.html

51. December 13, 200451 Profit Margin/Break Even Phase 1 Budget Phase 2 Budget Total GAP Acquisition by NVIDIA $7,000,000 NET PROFIT$6,592,000

52. December 13, 200452 Conclusion Through our prototype we have achieved “proof of concept” The overall efficiency gain obtained within computationally intensive environments proves a need for GAP

53. December 13, 200453 G raphical A symmetric P rocessing