GPU, a Gnutella Processing Unit an extensible P2P framework for distributed computing

1. GPU, a Gnutella Processing Unit an extensible P2P framework for distributed computing (joint work involving source code of at least 25 people) GPU logo created by Mark Grady and David A. Lucas

2. How many of you did not shutdown the computer and are now here in this room? • Assume we are 15 people running a screensaver without performing real work. The talk lasts one hour. • Opportunity loss for one hour: Speed: 15 * 0.8 GFlops = 12 GFlops Comp: 12GFlops * 1h = 43‘200 billion of floating point operations • Costs for one hour: Power consumption: 15 * 300 W = 4500 W during one hour = 4.5 kWh Money: 4.5kWh à 0.20 CHF = 0.9 CHF Oil needed: 0.36 liter (Gasoline: 12.3 kWh/kg) CO2 emissions: 0.81 kg CO2 (Gasoline: 2.27 kg CO2 / liter)

3. During one year (15 people)… • Opportunity loss for one year: Speed: 15 * 0.8 GFlops = 12 GFlops Comp: 12GFlops * 1y = 378 432 000 billion of floating point ops • Costs for one year: Power consumption: 15 * 300 W = 4500 W during one year = 39.42 MWh Money: 39.42 MWh => 7 884 CHF (525.6 CHF per head) Oil needed: 3153.6 liter (Gasoline: 12.3 kWh/kg) CO2 emissions: 7 t CO2 (Gasoline: 2.27 kg CO2 / liter) We signed the Kyoto Protocol…

4. Let‘s jump into the matter This video was computed in a distributed fashion by 10 computers running for about one day. The GPU program distributed jobs, Terragen computed the frames. Hacker Red created terrain, sky and camera path. Terragen artists: Nico, Red, paulatreides, nikoala, nanobit

5. Distributed Computing • Distributed computing is a science which solves a large problem by giving small parts of the problem to many computers to solve and then combining the solutions for the parts into a solution for the problem. • Recent distributed computing projects have been designed to use the computers of hundreds of thousands of volunteers all over the world, via the Internet, to look for extra-terrestrial radio signals, to look for prime numbers so large that they have more than ten million digits, and to find more effective drugs to fight cancer and the AIDS virus. • These projects run when the computer is idle • These projects are so large, and require so much computing power to solve, that they would be impossible for any one computer or person to solve in a reasonable amount of time. (from distributedcomputing.info)

6. Distributed Computing vs. Supercomputers Lot of computational power available through Distributed Computing. However, supercomputers support intra-processor algorithms. Distributed computing projects keep running the same dumb task on all computers.

7. Client/Server Peer to peer Client Node Client Client Node Node Node Server Client Client Node Client Client Node Node Timeline 1998 Seti@Home (Anderson et al.) Gnutella, J. Frankel, T. Pepper 2000 Many other projects follow: Folding@home, Chessbrain.net… File sharing systems: Kazaa eMule.. P2P research frame works: Sun JXTA, Triana, GPU 2002 Grid Computing Globus Toolkit BOINC project provides framework to unify several projects. 2004 Centralized approach Peer to Peer approach

8. A A A A Node Principle of Gnutella • Flooding incoming outgoing Several ideas to limit geometrical growth of packets: List of already seen packets, Time To live stamp, QueryHit routing, Ultrapeers system keeps the network more tree-like. Gnutella is implemented in GPU thanks to Kamil Pogorzelski.

9. P2P networks are random graphs… Node is computer. Length of edges is distance in milliseconds between two nodes.

10. …random graphs with given edge length do not necessarily fit into plane…

11. …random graphs are fractal in their nature. • Fractal dimension of the Gnutella network: • D = 7.79 (difficult to imagine). • Interesting property of fractals: patterns repeat, patterns similar at different scale lengths.

12. Framework Architecture Network Architecture Suitable Tasks for the framework Extensions Distributed Search Engine Terragen Landscape Generator Long term goals

13. GPU architecture on a single computer

14. Example Main GPU application Frontends visualize results of computations. Frontends monitor network performance. Delphi GL port and effects by Tom Nuydens and Chris Rorden Plugins perform computation.

15. Network Architecture GPUs advertise their IP number on a public list, GPUs know each other through autoping and autopongs, GPUs know IP address of entry gates.

16. GPU Network in practice(December 2004) • Around 10 computers available at any time of the day, in average • 3 FTP servers, one for collecting generated images, one to distribute updated binaries and one to distribute videos. • Web on sourceforge.net • CVS on sourceforge.net • Documentation on sourceforge.net

17. Special features: Chat System • Allows developers and users to meet, to exchange ideas and bug reports • Debugging on the fly: if the network is running correctly, you should definitively not see the same sentence repeated five times (as it happened before).

18. Suitable Tasks for the framework • The star topology is a subset of the random graph. Any centralized approach can run with some overhead on the P2P network (Rene Tegel, applaunch.dll) • No overhead for: • Monte Carlo Methods • Evolutionary Algorithms • Randomized Algorithms • Distributed Databases (to same extent)

19. GPU Extension I • Distributed Search Engine by Rene Tegel Each GPU can run crawlers on websites. Links are visited randomly. Visited pages are indexed in a local database. Each GPU can query the databases of other GPUs. Status for this extension: experimental.

20. GPU Extension II • Terragen Landscape Generator (PlanetSide Software and Rene Tegel)

21. Terragen™ • Terragen is a software written by PlanetSide, a UK company. It is not open source but free for personal use. • GPUs download terrain description and camera path from the FTP server, and decide to render a particular frame randomly. The computed frame is uploaded back to the FTP server. • By merging frames together with a codec, we are able to generate videos (merging on one computer only) • Status for this extension: production • Typical centralized extension • Download already produced videos here.

22. Special features: Autoupdate system Releasing through Sourceforge takes about three quarter hour. Quick way to deliver fixes and to keep the cluster updated: Download new files from FTP server (tea.ch)

23. Long term goals • At present, system scales up to 40-60 computers. Change this to scale up to 500 000 computers as any good P2P network does. • Try to extend the framework to get a so that it supports agents (agent based model) • Try to implement an example of evolutionary algorithm (e.g. Core Wars) • Try to implement a project with public appealing, like Near Earth Objects Hazard Monitoring (ORSA)

24. Long term goals II • GPU Core • Keep it under GPL license. • Rewrite it to be less ugly and more object-oriented. • Abandon Gnutella and go for a connection layer with Distributed Hash Tables. • Connection layer should be generalized to support any sort of communication (chats, computations, file-sharing) • Native Linux implementation (not only through wine emulator, although already stable and fast) • Not only x86 architecture

25. Special features: CVS support • Goal: keep source code of developers synchronized. • Done through CVS of Sourceforge, bash Unix shell, Cygwin or TortoiseCVS • Red files are not in sync with repository.

26. GPU Cluster Pictures

27. Thank you for your attention! Home of the project is http://gpu.sourceforge.net More videos here… And thanks to the GPU Team and