1 Scaling Up Data Intensive Science to Campus Grids Douglas Thain Clemson University 25 Septmber 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.

4 What is a Campus Grid? A campus grid is an aggregation of all available computing power found in an institution: –Idle cycles from desktop machines. –Unused cycles from dedicated clusters. Examples of campus grids: –700 CPUs at the University of Notre Dame – ,000 CPUs at Clemson University –20,000 CPUs at Purdue University

5 Provides robust batch queueing on a complex distributed system. Resource owners control consumption: –“Only run jobs on this machine at night.” –“Prefer biology jobs over physics jobs.” End users express needs: –“Only run this job where RAM>2GB” –“Prefer to run on machines

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

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

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

14 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?

15 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.

16 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.

17 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.

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

19 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

20 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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22 Choose the Right # of CPUs

23 Resources Consumed

24 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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27 Are there other abstractions?

28 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

29 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

30 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)

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

32 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

33 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.

34 Experience with Makeflow Reusing a good old idea in a new way. 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. Student from Clemson got complex biometrics workload running in a few weeks.

35 Putting it All Together Web Portal Data Repository Campus Grid F Y Z X Abstraction

36 BXGrid Schema fileid = size = 300K type = jpg sum = abc123… replicaid=423 state=ok replicaid=105 state=ok replicaid=293 state=creating replicaid=102 state=deletingTypeSubjectEyeColorFileIDIrisS100RightBlue10486 IrisS100LeftBlue10487 IrisS203RightBrown24304 IrisS203LeftBrown24305 Scientific Metadata General Metadata Immutable Replicas

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40 Results from Campus Grid

41 Biocompute

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43 Parallel BLAST Makeflow

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45 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.

46 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. Portal + Repository + Abstraction + Grid = New Science Capabilities = New Science Capabilities

47 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 and CNS and CNS Undergrads –Mike Kelly –Rory Carmichael –Mark Pasquier –Christopher Lyon –Jared Bulosan –Kameron Srimoungach –Rachel Witty –Ryan Jansen –Joey Rich