1 Science in the Clouds: History, Challenges, and Opportunities Douglas Thain University of Notre Dame GeoClouds Workshop 17 September 2009

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3 The Cooperative Computing Lab We collaborate with people who have large scale computing problems. We build new software and systems to help them achieve meaningful goals. We run a production computing system used by people at ND and elsewhere. We conduct computer science research, informed by real world experience, with an impact upon problems that matter.

Clouds in the Hype Cycle Gartner Hype Cycle Report, 2009

5 What is cloud computing? A cloud provides rapid, metered access to a virtually unlimited set of resources. Two significant impact on users: –End users must have an economic model for the work that they want to accomplish. –Apps must be flexible enough to work with an arbitrary number and kind of resources.

6 Example: Amazon EC2 Sep 2009 (simplified slightly for discussion) Small: 1 core, 1.7GB RAM, 160GB disk –10 cents/hour Large: 2 cores, 7.5GB RAM, 850GB disk –40 cents/hour Extra Large: 4 cores, 15 GB, 1690GB disk –80 cents/hour And the Simple Storage Service: –15 cents per GB-month stored –17 cents per GB transferred (outside of EC2) –1 cent per 1000 write operations –1 cent per read operations

7 Is Cloud Computing New? Not entirely, but a combination of the old ideas of utility computing and distributed computing. –1960 – MULTICS –1980 – The Cambridge Ring –1987 – Condor Distributed Batch System –1989 – –1990s – Clusters, Beowulf, MPI, NOW –1995 – Globus, Grid Computing –2001 –TeraGrid –2004 – Sun Rents CPUs at $1/hour –2006 – Amazon EC2 and S3

8 OpEx of Ownership Clouds Trade CapEx for OpEx Capital Expense of Ownership Cost Time OpEx of Cloud Computing 2X

9 What about grid computing? A vision much like clouds: –A worldwide framework that would make massive scale computing as easy to use as an electrical socket. The more modest realization: –A means for accessing remote computing facilities in their native form, usually for CPU- intensive tasks. The social context: –Large collaborative efforts between computer scientists and computer-savvy fields, particularly physics and astronomy.

10 Clouds vs Grids Grids provide a job execution interface: –Run program P on input A, return the output. –Allows the system to maximize utilization and hide failures, but provides few performance guarantees and inaccurate metering. Clouds provide resource allocation: –Create a VM with 2GB of RAM for 7 days. –Gives predictable performance and accurate metering, but exposes problems to the user. –Can be used to build interactive services. –How do I run 1M jobs on 100 servers?

11 Grid Computing Layer Provides Job Execution Cloud Computing Layer Provides Resource Allocation Submit 1M Jobs Allocate 100 CPUs Dispatch Jobs Manage Load

12 Create a Condor Pool with 100 Nodes Allocate 100 Cores Run 1M Jobs

13 Clouds Solve Some Grid Problems Application compatibility is simplified. –You provide a VM for Linux Performance is reasonably predictable. –10% variations rather than orders of mag. Fewer administrative headaches for the lone user. –A credit card swipe instead of a certificate.

14 But, Problems New and Old: How do I reliably execute 1M jobs? Can I share resources and data with others in the cloud? How do I authenticate others in the cloud? Unfortunately, location still matters. Can we make applications efficiently span multiple cloud providers? Can we join existing centers with clouds? (These are all problems contemplated by grid.)

15 More Open Questions Can I afford to move my data in to the cloud? Can I afford to get it out? Do I trust the cloud to secure my data? How do I go about constructing an economic model for my research? Are there social/technical dangers in putting too many eggs in one basket? Is pay-go the proper model for research? Should universities get out of the data center business?

16 Clusters, clouds, and grids give us access to unlimited CPUs. How do we write programs that can run effectively in large systems?

17 K,V M Key0 Key1 KeyN V V V V V V V V V R R RO2 O1 O0 MapReduce ( S, M, R ) Set S

18 Of course, not all science fits into the Map-Reduce model!

19 Example: Biometrics Research Goal: Design robust face comparison function. F 0.05 F 0.97

20 Similarity Matrix Construction Challenge Workload: 60,000 iris images 1MB each.02s per F 833 CPU-days 600 TB of I/O

21 I have 60,000 iris images acquired in my research lab. I want to reduce each one to a feature space, and then compare all of them to each other. I want to spend my time doing science, not struggling with computers. I have a laptop. I own a few machinesI can buy time from Amazon or TeraGrid. Now What?

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25 Non-Expert User Using 500 CPUs Try 1: Each F is a batch job. Failure: Dispatch latency >> F runtime. HN CPU FFFF F Try 2: Each row is a batch job. Failure: Too many small ops on FS. HN CPU FFFF F F F F F F F F F F F F F F F F Try 3: Bundle all files into one package. Failure: Everyone loads 1GB at once. HN CPU FFFF F F F F F F F F F F F F F F F F Try 4: User gives up and attempts to solve an easier or smaller problem.

26 Observation In a given field of study, many people repeat the same of work many times, making slight changes to the data and algorithms. In a given field of study, many people repeat the same pattern of work many times, making slight changes to the data and algorithms. If the system knows the overall pattern in advance, then it can do a better job of executing it reliably and efficiently. If the user knows in advance what patterns are allowed, then they have a better idea of how to construct their workloads.

27 Abstractions for Distributed Computing Abstraction: a declarative specification of the computation and data of a workload. A restricted pattern, not meant to be a general purpose programming language. Uses instead of files. Uses data structures instead of files. Provide users with a. Provide users with a bright path. Regular structure makes it tractable to model and predict performance.

28 Working with Abstractions F A1 A2 An AllPairs( A, B, F ) Cloud or Grid A1 A2 Bn Custom Workflow Engine Compact Data Structure

29 All-Pairs Abstraction AllPairs( set A, set B, function F ) returns matrix M where M[i][j] = F( A[i], B[j] ) for all i,j B1 B2 B3 A1A2A3 FFF A1 An B1 Bn F AllPairs(A,B,F) F FF FF F allpairs A B F.exe

30 How Does the Abstraction Help? The custom workflow engine: –Chooses right data transfer strategy. –Chooses the right number of resources. –Chooses blocking of functions into jobs. –Recovers from a larger number of failures. –Predicts overall runtime accurately. All of these tasks are nearly impossible for arbitrary workloads, but are tractable (not trivial) to solve for a specific abstraction.

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32 Choose the Right # of CPUs

33 Resources Consumed

34 All-Pairs in Production Our All-Pairs implementation has provided over 57 CPU-years of computation to the ND biometrics research group over the last year. Largest run so far: 58,396 irises from the Face Recognition Grand Challenge. The largest experiment ever run on publically available data. Competing biometric research relies on samples of images, which can miss important population effects. Reduced computation time from 833 days to 10 days, making it feasible to repeat multiple times for a graduate thesis. (We can go faster yet.)

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36 Are there other abstractions?

37 M[4,2] M[3,2]M[4,3] M[4,4]M[3,4]M[2,4] M[4,0]M[3,0]M[2,0]M[1,0]M[0,0] M[0,1] M[0,2] M[0,3] M[0,4] F x yd F x yd F x yd F x yd F x yd F x yd F F y y x x d d x FF x ydyd Wavefront( matrix M, function F(x,y,d) ) returns matrix M such that M[i,j] = F( M[i-1,j], M[I,j-1], M[i-1,j-1] ) F Wavefront(M,F) M

38 Applications of Wavefront Bioinformatics: –Compute the alignment of two large DNA strings in order to find similarities between species. Existing tools do not scale up to complete DNA strings. Economics: –Simulate the interaction between two competing firms, each of which has an effect on resource consumption and market price. E.g. When will we run out of oil? Applies to any kind of optimization problem solvable with dynamic programming.

39 Problem: Dispatch Latency Even with an infinite number of CPUs, dispatch latency controls the total execution time: O(n) in the best case. However, job dispatch latency in an unloaded grid is about 30 seconds, which may outweigh the runtime of F. Things get worse when queues are long! Solution: Build a lightweight task dispatch system. (Idea from

40 worker work queue F In.txtout.txt put F.exe put in.txt exec F.exe out.txt get out.txt 1000s of workers Dispatched to the cloud wavefront engine queue tasks done

41 Problem: Performance Variation Tasks can be delayed for many reasons: –Heterogeneous hardware. –Interference with disk/network. –Policy based suspension. Any delayed task in Wavefront has a cascading effect on the rest of the workload. Solution - Fast Abort: Keep statistics on task runtimes, and abort those that lie significantly outside the mean. Prefer to assign jobs to machines with a fast history.

42 500x500 Wavefront on ~200 CPUs

43 Wavefront on a 200-CPU Cluster

44 Wavefront on a 32-Core CPU

45 The Genome Assembly Problem AGTCGATCGATCGATAATCGATCCTAGCTAGCTACGA AGTCGATCGATCGAT AGCTAGCTACGA TCGATAATCGATCCTAGCTA Chemical Sequencing Computational Assembly AGTCGATCGATCGAT AGCTAGCTACGA TCGATAATCGATCCTAGCTA Millions of “reads” 100s bytes long.

46 Sample Genomes ReadsDataPairsSequentialTime A. gambiae scaffold101K80MB738K 12 hours A. gambiae complete180K1.4GB12M 6 days S. Bicolor simulated7.9M5.7GB84M 30 days

47 Some-Pairs Abstraction SomePairs( set A, list (i,j), function F(x,y) ) returns list of F( A[i], A[j] ) A1 A2 A3 A1A2A3 F A1 An F SomePairs(A,L,F) FF F (1,2) (2,1) (2,3) (3,3)

48 worker work queue in.txtout.txt put align.exe put in.txt exec F.exe out.txt get out.txt 100s of workers dispatched to Notre Dame, Purdue, and Wisconsin somepairs master queue tasks done F detail of a single worker: Distributed Genome Assembly A1 An F (1,2) (2,1) (2,3) (3,3)

49 Small Genome (101K reads)

50 Medium Genome (180K reads)

51 Large Genome (7.9M)

52 What’s the Upshot? We can do full-scale assemblies as a routine matter on existing conventional machines. Our solution is faster (wall-clock time) than the next faster assembler run on 1024x BG/L. You could almost certainly do better with a dedicated cluster and a fast interconnect, but such systems are not universally available. Our solution opens up research in assembly to labs with “NASCAR” instead of “Formula-One” hardware.

53 What if your application doesn’t fit a regular pattern?

54 Makeflow part1 part2 part3: input.data split.py./split.py input.data out1: part1 mysim.exe./mysim.exe part1 >out1 out2: part2 mysim.exe./mysim.exe part2 >out2 out3: part3 mysim.exe./mysim.exe part3 >out3 result: out1 out2 out3 join.py./join.py out1 out2 out3 > result

55 worker work queue afilebfile put prog put afile exec prog afile > bfile get bfile 100s of workers dispatched to the cloud makeflow master queue tasks done prog detail of a single worker: Makeflow Implementation bfile: afile prog prog afile >bfile Two optimizations: Cache inputs and output. Dispatch tasks to nodes with data.

56 Experience with Makeflow Still in initial deployment, so no big results to show just yet. Easy to test and debug on a desktop machine or a multicore server. The workload says nothing about the distributed system. (This is good.) Graduate students in bioinformatics running codes at production speeds on hundreds of nodes in less than a week.

57 Abstractions as a Social Tool Collaboration with outside groups is how we encounter the most interesting, challenging, and important problems, in computer science. However, often neither side understands which details are essential or non-essential: –Can you deal with files that have upper case letters? –Oh, by the way, we have 10TB of input, is that ok? –(A little bit of an exaggeration.) An abstraction is an excellent chalkboard tool: –Accessible to anyone with a little bit of mathematics. –Makes it easy to see what must be plugged in. –Forces out essential details: data size, execution time.

58 Conclusion Grids, clouds, and clusters provide enormous computing power, but are very challenging to use effectively. An abstraction provides a robust, scalable solution to a narrow category of problems; each requires different kinds of optimizations. Limiting expressive power, results in systems that are usable, predictable, and reliable. Is there a menu of abstractions that would satisfy many consumers of clouds?

59 Acknowledgments Cooperative Computing Lab – Grad Students –Chris Moretti –Hoang Bui –Li Yu –Mike Olson –Michael Albrecht Faculty: –Patrick Flynn –Nitesh Chawla –Kenneth Judd –Scott Emrich NSF Grants CCF , CNS Undergrads –Mike Kelly –Rory Carmichael –Mark Pasquier –Christopher Lyon –Jared Bulosan