ChessBrain: a Linux-Based Distributed Computing Experiment
On May 27, 2003, 646 machines worked together to play a single game of chess. This was the first time such a feat had been accomplished, and it was made possible by the power of Linux, open-source software and hundreds of contributors from over 37 different countries.
ChessBrain (chessbrain.net) is a distributed computation project that uses the idle processing power of distributed machines to solve computationally intensive problems. ChessBrain is a system focused on playing chess, but the underlying system can be adapted for other games as well as for non-game-related applications.
Imagine playing a game against an opponent, except every time he moves, you grab the phone and start calling friends for help. You call Sue, describe the current position and ask her to call you back when she has an answer. Then you call Ryan to ask whether you should worry about a pending attack; again, you ask for a call back when he has an answer. After calling 20 other friends, you sit back and wait for replies. This is similar to how ChessBrain plays chess.
ChessBrain consists of a Linux-based server application, the SuperNode, and client software known as PeerNodes. The SuperNode connects to an on-line game server, which allows visiting members to play against one another, challenge ChessBrain to a game or watch ChessBrain play against its current opponent. While ChessBrain plays, it examines positions, dispatches hundreds of potential moves to remote PeerNodes for analysis, collects feedback from the PeerNodes, processes that information and makes its best move. ChessBrain exists as an ever-changing pool of networked machines. Philosophically and scientifically, it's a beautiful thing.
I started ChessBrain as a distributed computing experiment in the summer of 2001. By the end of that year, I had a working prototype and needed a place to host the server. My longtime friend, Walter Howard, the webmaster of HackerWhacker (hackerwhacker.com), offered to host the server on his personal T1 line.
On June 9, 2002, ChessBrain appeared on Slashdot, and the positive exposure resulted in hundreds of new PeerNode operators. Gavin Roy, one of the new members, owns the bteg network (www.bteg.net) and offered to host a SuperNode server free of charge. On June 27, I met Gavin for dinner and handed this near stranger a SuperNode server on a Pentium III machine. ChessBrain gained another server, I gained another friend, and Gavin has become an important supporter of the ChessBrain Project. I transitioned the SuperNode over to Gavin's site, and Walt continued to host the original SuperNode as a secondary backup and experimental server.
During the months that followed, we gained an amazing amount of exposure. Few seemed to mind that ChessBrain couldn't actually play chess. The first eight months of 2002 were spent working on the SuperNode server and porting the PeerNode client to Microsoft Windows and Apple's Mac OS X.
Once the server and clients worked well, the focus was on getting ChessBrain actually to play. The wbec-ridderkerk (www.wbec-ridderkerk.nl) site in the Netherlands lists nearly 200 freely available chess-playing programs. I reviewed a few, looking for one with relatively clean code and the ability to compile under several operating systems. I found an ideal program in Beowulf, written by Colin Frayn, who was then a PhD candidate at Cambridge University in England. We exchanged several e-mails and Colin joined the project. We collaborated entirely on-line using e-mail and instant messaging (IM) and began making necessary modifications. Colin adapted his chess program for distributed computing, and I modified the SuperNode and PeerNode clients to use his engine. The time difference between London and Los Angeles proved ideal. I would IM Colin at my 3AM and again during the day. By my late afternoon, Colin would head for bed, and I would work through his night. Before crashing, I would leave feedback for Colin. This round-the-clock cycle continued for months.
Colin adapted his original Beowulf chess engine to become two chess-playing components, BeoServer and BeoClient. He developed the pair to support distributed chess play within the ChessBrain framework. On December 22, 2002, ChessBrain played its first game of distributed chess. By January 2003, the ChessBrain community had provided 62 machines and was testing regular builds.
Realizing the promise of Apache® Hadoop® requires the effective deployment of compute, memory, storage and networking to achieve optimal results. With its flexibility and multitude of options, it is easy to over or under provision the server infrastructure, resulting in poor performance and high TCO. Join us for an in depth, technical discussion with industry experts from leading Hadoop and server companies who will provide insights into the key considerations for designing and deploying an optimal Hadoop cluster.
Sponsored by AMD
If you already use virtualized infrastructure, you are well on your way to leveraging the power of the cloud. Virtualization offers the promise of limitless resources, but how do you manage that scalability when your DevOps team doesn’t scale? In today’s hypercompetitive markets, fast results can make a difference between leading the pack vs. obsolescence. Organizations need more benefits from cloud computing than just raw resources. They need agility, flexibility, convenience, ROI, and control.
Stackato private Platform-as-a-Service technology from ActiveState extends your private cloud infrastructure by creating a private PaaS to provide on-demand availability, flexibility, control, and ultimately, faster time-to-market for your enterprise.
Sponsored by ActiveState
| Containers—Not Virtual Machines—Are the Future Cloud | Jun 17, 2013 |
| Lock-Free Multi-Producer Multi-Consumer Queue on Ring Buffer | Jun 12, 2013 |
| Weechat, Irssi's Little Brother | Jun 11, 2013 |
| One Tail Just Isn't Enough | Jun 07, 2013 |
| Introduction to MapReduce with Hadoop on Linux | Jun 05, 2013 |
| Android's Limits | Jun 04, 2013 |
- Containers—Not Virtual Machines—Are the Future Cloud
- Lock-Free Multi-Producer Multi-Consumer Queue on Ring Buffer
- Linux Systems Administrator
- Introduction to MapReduce with Hadoop on Linux
- Senior Perl Developer
- Technical Support Rep
- UX Designer
- Weechat, Irssi's Little Brother
- One Tail Just Isn't Enough
- Android's Limits
- http://www.pldhs.com/
18 min 47 sec ago - Free is costly
1 hour 34 min ago - Bought photoshop CS5 for developing a website :(
1 hour 50 min ago - Reply to comment | Linux Journal
2 hours 38 min ago - Reply to comment | Linux Journal
2 hours 38 min ago - Replica Watches
5 hours 3 min ago - Reply to comment | Linux Journal
9 hours 14 min ago - on the path to understanding
9 hours 18 min ago - As a fisher,we know that a
1 day 4 hours ago - All I Say Is Worth Share!
1 day 5 hours ago
Featured Jobs
| Linux Systems Administrator | Houston and Austin, Texas | Host Gator |
| Senior Perl Developer | Austin, Texas | Host Gator |
| Technical Support Rep | Houston and Austin, Texas | Host Gator |
| UX Designer | Austin, Texas | Host Gator |
| Web & UI Developer (JavaScript & j Query) | Austin, Texas | Host Gator |
Free Webinar: Hadoop
How to Build an Optimal Hadoop Cluster to Store and Maintain Unlimited Amounts of Data Using Microservers
Realizing the promise of Apache® Hadoop® requires the effective deployment of compute, memory, storage and networking to achieve optimal results. With its flexibility and multitude of options, it is easy to over or under provision the server infrastructure, resulting in poor performance and high TCO. Join us for an in depth, technical discussion with industry experts from leading Hadoop and server companies who will provide insights into the key considerations for designing and deploying an optimal Hadoop cluster.
Some of key questions to be discussed are:
- What is the “typical” Hadoop cluster and what should be installed on the different machine types?
- Why should you consider the typical workload patterns when making your hardware decisions?
- Are all microservers created equal for Hadoop deployments?
- How do I plan for expansion if I require more compute, memory, storage or networking?





Comments
Four years
Reading that article today is like watching some old star trek series :D
Gameserver
Nice Script :P
I hope it will bring the Thing further.
Gameserver