If you are interested in Hadoop or Mapreduce, I would like to recommend participating or submitting your paper to the First International Workshop on Theory and Practice of Mapreduce (MAPRED’2010) (held in correspondance with the 2nd IEEE International Conference on Cloud Computing Technology and Science).

(I just joined the workshop as a program committee member)

Best regards,

Amund Tveit (co-founder of Atbrox)

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Underneath are statistics about which 20 papers (of about 80 papers) were most read in our 3 previous postings about mapreduce and hadoop algorithms (the postings have been read approximately 5000 times). The list is ordered by decreasing reading frequency, i.e. most popular at spot 1.

1. MapReduce-Based Pattern Finding Algorithm Applied in Motif Detection for Prescription Compatibility Network
   authors: Yang Liu, Xiaohong Jiang, Huajun Chen , Jun Ma and Xiangyu Zhang – Zhejiang University

2. Data-intensive text processing with Mapreduce
   authors: Jimmy Lin and Chris Dyer – University of Maryland

3. Large-Scale Behavioral Targeting
   authors: Ye Chen (eBay), Dmitry Pavlov (Yandex Labs) and John F. Canny (University of California, Berkeley)

4. Improving Ad Relevance in Sponsored Search
   authors: Dustin Hillard, Stefan Schroedl, Eren Manavoglu, Hema Raghavan and Chris Leggetter (Yahoo Labs)

5. Experiences on Processing Spatial Data with MapReduce
   authors: Ariel Cary, Zhengguo Sun, Vagelis Hristidis and Naphtali Rishe – Florida International University

6. Extracting user profiles from large scale data
   authors: Michal Shmueli-Scheuer, Haggai Roitman, David Carmel, Yosi Mass and David Konopnicki – IBM Research, Haifa

7. Predicting the Click-Through Rate for Rare/New Ads
   authors: Kushal Dave and Vasudeva Varma – IIIT Hyderabad

8. Parallel K-Means Clustering Based on MapReduce
   authors: Weizhong Zhao, Huifang Ma and Qing He – Chinese Academy of Sciences

9. Storage and Retrieval of Large RDF Graph Using Hadoop and MapReduce
   authors: Mohammad Farhan Husain, Pankil Doshi, Latifur Khan and Bhavani Thuraisingham – University of Texas at Dallas

10. Map-Reduce Meets Wider Varieties of Applications
    authors: Shimin Chen and Steven W. Schlosser – Intel Research

11. LogMaster: Mining Event Correlations in Logs of Large-scale Cluster Systems
    authors: Wei Zhou, Jianfeng Zhan, Dan Meng (Chinese Academy of Sciences), Dongyan Xu (Purdue University) and Zhihong Zhang (China Mobile Research)

12. Efficient Clustering of Web-Derived Data Sets
    authors: Luıs Sarmento, Eugenio Oliveira (University of Porto), Alexander P. Kehlenbeck (Google), Lyle Ungar (University of Pennsylvania)

13. A novel approach to multiple sequence alignment using hadoop data grids
    authors: G. Sudha Sadasivam and G. Baktavatchalam – PSG College of Technology

14. Web-Scale Distributional Similarity and Entity Set Expansion
    authors: Patrick Pantel, Eric Crestan, Ana-Maria Popescu, Vishnu Vyas (Yahoo Labs) and Arkady Borkovsky (Yandex Labs)

15. Grammar based statistical MT on Hadoop
    authors: Ashish Venugopal and Andreas Zollmann (Carnegie Mellon University)

16. Distributed Algorithms for Topic Models
    authors: David Newman, Arthur Asuncion, Padhraic Smyth and Max Welling – University of California, Irvine

17. Parallel algorithms for mining large-scale rich-media data
    authors: Edward Y. Chang, Hongjie Bai and Kaihua Zhu – Google Research

18. Learning Influence Probabilities In Social Networks
    authors: Amit Goyal, Laks V. S. Lakshmanan (University of British Columbia) and Francesco Bonchi (Yahoo! Research)

19. MrsRF: an efficient MapReduce algorithm for analyzing large collections of evolutionary trees
    authors: Suzanne J Matthews and Tiffani L Williams – Texas A&M University

20. User-Based Collaborative-Filtering Recommendation Algorithms on Hadoop
    authors: Zhi-Dan Zhao and Ming-sheng Shang

    

    Best regards,

    Amund Tveit (Atbrox co-founder)

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Atbrox is startup company providing technology and services for Search and Mapreduce/Hadoop. Our background is from Google, IBM and research.

Update 2010-July-13: Can remove towards from the title of this posting today, Amazon just launched cluster compute instances with 10GB network bandwidth between nodes (and presents a run that enters top 500 list at 146th place, I estimate the run to cost ~$20k).

The Top 500 list is for supercomputers what Fortune 500 is for companies. About 80% of the list are supercomputers built by either Hewlett Packard or IBM, other major supercomputing vendors on the list include Dell, Sun (Oracle), Cray and SGI. Parallel linpack benchmark result is used as the ranking function for the list position (a derived list – green 500 – also includes power-efficiency in the ranking).

Trends towards Cloud Supercomputing
To our knowledge the entire top 500 list is currently based on physical supercomputer installations and no cloud computing configurations (i.e. virtual configurations lasting long enough to calculate the linpack benchmark), that will probably change within in a few years. There are however trends towards cloud-based supercomputing already (in particular within consumer internet services and pharmaceutical computations), here are some concrete examples:

1. Zynga (online casual games, e.g. Farmville and Mafia Wars)
   Zynga uses 12000 Amazon EC2 nodes (ref: Manager of Cloud Operations at Zynga)

2. Animoto (online video production service)
   Animoto scaled from 40 to 4000 EC2 nodes in 3 days (ref: CTO, Animoto)

3. Myspace (social network)
   Myspace simulated 1 million simultaneous users using 800 large EC2 nodes (3200 cores) (ref: highscalability.com)

4. New York Times
   New York Times used hundreds of EC2 nodes to process their archives in 36 hours (ref: The New York Times Archives + Amazon Web Services = TimesMachine)

5. Reddit (news service)
   Reddit uses 218 EC2 nodes (ref: I run reddit’s servers)

Examples with (rough) estimates

1. Justin.tv (video service)
   In october 2009 Justin.tv users watched 50 million hours of video, and they cost (reported earlier) was about 1 penny per user-video-hour, a very rough estimate would be monthly costs of 50M/0.01 = 500k$, i.e. 12*500k$ = 6M$ anually. Assuming that half their costs are computational, this would be about 3M$/(24*365*0.085) ~ 4029 EC2 nodes 24×7 through the year, but since they are a video site bandwidth is probably a significant fraction of the cost, so cutting the rough estimate in half to around 2000 EC2 nodes.
   (ref: Watching TV Together, Miles Apart and Justin.tv wins funding, opens platform)

2. Newsweek
   Newsweek saves up to $500.000 per year by moving to the cloud, assuming they cut their spending in half by using the cloud that would correspond to $500.000/(24h/day*365d/y*0.085$/h) ~ 670 EC2 nodes 24×7 through the year (probably a little less due to storage and bandwidth costs)
   (ref: Newsweek.com Explores Amazon Cloud Computing)

3. Recovery.gov
   Recory.gov saves up to $420.000 per year by moving to the cloud, assuming they cut their spending in half by using the cloud that would correspond to $420.000/(24h/day*365d/y*0.085$/h) ~ 560 EC2 nodes 24×7 through the year (probably a little less due to storage and bandwidth costs). (ref: Feds embrace cloud computing; move Recovery.gov to Amazon EC2)

Other examples of Cloud Supercomputing

1. Pharmaceutical companies Eli Lilly, Johnson & Johnson and Genentech
   Offloading computations to the cloud (ref: Biotech HPC in the Cloud and The new computing pioneers)

2. Pathwork Diagnostics
   Using EC2 for cancer diagnostics (ref: Of Unknown Origin: Diagnosing Cancer in the Cloud)

Best regards,

Amund Tveit

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