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Using the Grid for Astronomical Data Roy Williams, Caltech.

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1 Using the Grid for Astronomical Data Roy Williams, Caltech

2 Palomar-Quest Survey Caltech, NCSA, Yale P48 Telescope CaltechYale NCSA Transient pipeline computing reservation at sunrise for immediate followup of transients Synoptic survey massive resampling (Atlasmaker) for ultrafaint detection TG NCSA and Caltech and Yale run different pipelines on the same data 50 Gbyte/night 5 Tbyte ALERT

3 Wide-area Mosaicking (Hyperatlas) An NVO-Teragrid project C altech High-quality flux-preserving, spatial accuracy Stackable Hyperatlas Edge-free Pyramid weight Mining AND Outreach DPOSS 15º Griffith Observatory "Big Picture"

4 Synoptic Image Stack

5 PQ Pipeline Computing Observation Night 28 columns x 4 filters up to 70 Gbyte real-time next day cleaned frameshyperatlas pages coadd VOEventNet

6 Mosaicking service Logical SIAP NVO Registry Physical SIAP Computing Portal Security Request Sandbox http

7 Transient from PQ from catalog pipeline

8 Event Synthesis Engine Pairitel Palomar 60 Raptor PQ next-day pipelines catalog Palomar-Quest known Variables known asteroids SDSS 2MASS PQ Event Factory remote archives baseline sky eStar VOEventNet VOEventNet: a Rapid-Response Telescope Grid GRB satellites VOEvent database

9 Correlation of mass distribution (SDSS) with CMB (ISW effect) -- statistical significance through ensemble of simulated universes Connolly and Scrantom, U Pittsburgh ISW Effect

10 Analysis of data from AMANDA Antarctic Muon and Neutrino Detector Array Barwick and Silvestri, UC Irvine Amanda analysis

11 Quasar Science An NVO-Teragrid project PennState, CMU, Caltech 60,000 quasar spectra from Sloan Sky Survey Each is 1 cpu-hour: submit to grid queue Fits complex model (173 parameter) –derive black hole mass from line widths clusters globusrun manager NVO data services

12 N-point galaxy correlation An NVO-Teragrid project Pitt, CMU Finding triple correlation in 3D SDSS galaxy catalog (RA/Dec/z) Lots of large parallel jobs kd-tree algorithms

13 TeraGrid

14 TeraGrid Wide Area Network

15 TeraGrid Components Compute hardware –Intel/Linux Clusters, Alpha SMP clusters, POWER4 cluster, … Large-scale storage systems – hundreds of terabytes for secondary storage Very high-speed network backbone – bandwidth for rich interaction and tight coupling Grid middleware – Globus, data management, … Next-generation applications

16 Overview of Distributed TeraGrid Resources HPSS UniTree External Networks Site Resources NCSA/PACI 10.3 TF 240 TB SDSC 4.1 TF 225 TB CaltechArgonne

17 Cluster Supercomputer 100s of nodes purged /scratch parallel file system /home (backed-up) login node job submission and queueing (Condor, PBS,..) user metadata node parallel I/O VO service

18 TeraGrid Allocations Policies Any US researcher can request an allocation –Policies/procedures posted at: –Online proposal submission NVO has an account on Teragrid –(just ask RW)

19 Data storage

20 Logical and Physical names Logical name –application-context eg frame_ fits Physical name –storage-context eg /home/roy/data/frame_ fits eg file:///envoy4/raid3/frames/ /012.fits eg gz

21 Logical and Physical Names Allows –replication of data –movement/optimization of storage –transition to database (lname -> key) –heterogeneous/extensible storage hardware /envoy2/raid2, /pvfs/nvo/, etc etc

22 Physical Name Suggest URI form –protocol://identifier –if you know the protocol, you can interpret the identifier Examples file:// ftp:// srb:// uberftp:// Transition to services

23 Typical types of HPC storage needs TypeTypical size Use Aggregate BW Tolerance for Latency Requirements 11-10TBHome filesyste m A lot of small files, high metadata rates, interactive use. 2 (optional) 100s GB (per CPU) Local scratch space High bandwidth data cache TB Global filesyste m High aggregate bandwidth. Concurrent access to data. Moderate latency tolerated. 4100TB- PB Archival Storage Large storage pools with low cost. Used for long term storage of results.

24 Disk Farms (datawulf) Large files striped over disks Management node for file creation, access, ls, etc etc Homogeneous Disk Farm (= parallel file system) parallel file system metadata node parallel I/O

25 Parallel File System Large files are striped –very fast parallel access Medium files are distributed –Stripes do not all start the same place Small files choke the PFS manager –Either containerize –or use blobs in a database not a file system anymore: pool of 10 8 blobs with lnames

26 Containerizing Shared metadata Easier for bulk movement container file in container

27 Extraction from Container tar container slow extraction (reads whole container) zip container indexed for fast partial extraction 2 Gbyte limit on container size used for fast access 2MASS image service at Caltech

28 Storage Resource Broker (SRB) Single logical namespace while accessing distributed archival storage resources Effectively infinite storage (first to 1TB wins a t-shirt) Data replication Parallel Transfers Interfaces: command-line, API, web/portal.

29 Storage Resource Broker (SRB): Virtual Resources, Replication NCSA SDSC workstation SRB Client (cmdline, or API) hpss-sdscsfs-tape-sdschpss-caltech …

30 Running jobs

31 3 Ways to Submit a Job 1. Directly to PBS Batch Scheduler –Simple, scripts are portable among PBS TeraGrid clusters 2. Globus common batch script syntax –Scripts are portable among other grids using Globus 3. Condor-G –Nice interface atop Globus, monitoring of all jobs submitted via Condor-G –Higher-level tools like DAGMan

32 PBS Batch Submission Single executables to be on a single remote machine –login to a head node, submit to queue Direct, interactive execution –mpirun –np 16./a.out Through a batch job manager –qsub my_script where my_script describes executable location, runtime duration, redirection of stdout/err, mpirun specification… ssh tg-login.[caltech|ncsa|sdsc|uc] –qsub –v "FILE=f544" –qstat or showq –ls *.dat –pbs.out, pbs.err files

33 Remote submission Through globus –globusrun -r [some-teragrid-head- node] -f my_rsl_script where my_rsl_script describes the same details as in the qsub my_script! Through Condor-G –condor_submit my_condor_script where my_condor_script describes the same details as the globus my_rsl_script!

34 globus-job-submit For running of batch/offline jobs –globus-job-submit Submit job same interface as globus-job-run returns immediately –globus-job-status Check job status –globus-job-cancel Cancel job –globus-job-get-output Get job stdout/err –globus-job-clean Cleanup after job

35 Condor-G A Grid-enabled version of Condor that provides robust job management for Globus clients. –Robust replacement for globusrun –Provides extensive fault-tolerance –Can provide scheduling across multiple Globus sites –Brings Condors job management features to Globus jobs

36 Condor DAGMan Manages workflow interdependencies Each task is a Condor description file A DAG file controls the order in which the Condor files are run

37 Data intensive computing with NVO services

38 Two Key Ideas for Fault- Tolerance Transactions No partial completion -- either all or nothing –eg copy to a tmp filename, then mv to correct file name Idempotent Acting as if done only once, even if used multiple times Can run the script repeatedly until finished

39 DPOSS flattening 2650 x 1.1 Gbyte files Cropping borders Quadratic fit and subtract Virtual data SourceTarget

40 Driving the Queues for f in os.listdir(inputDirectory): # if the file exists, with the right size and age, then we keep it ofile = outputDirectory +"/"+ f if os.path.exists(ofile): osize = os.path.getsize(ofile) if osize != : print " -- wrong target size, remaking", osize else: time_tgt = filetime(ofile) time_src = filetime(file) if time_tgt < time_src: print(" -- target too old or nonexistant, making") else: print " -- already have target file " continue cmd = "qsub -v \"FILE=" + f +"\"" print " -- submitting batch job: ", cmd os.system(cmd) Here is the driver that makes and submits jobs

41 PBS script #!/bin/sh #PBS -N dposs #PBS -V #PBS -l nodes=1 #PBS -l walltime=1:00:00 cd /home/roy/dposs-flat/flat./flat \ -infile /pvfs/mydata/source/${FILE}.fits \ -outfile /pvfs/mydata/target/${FILE}.fits \ -chop \ -chop \ -chop \ -chop \ -chop A PBS script. Can do "qsub –v "FILE=f345"

42 Hyperatlas Standard naming for atlases and pages TM-5-SIN-20 Page 1589 Standard Scales: scale s means 2 20-s arcseconds per pixel SIN projection TAN projection TM-5 layout HV-4 layout Standard Projections Standard Layout

43 Hyperatlas is a Service All Pages: /getChart?atlas=TM-5-SIN-20 (and no other arguments) E-4'RA---SIN 'DEC--SIN' E-4'RA---SIN'DEC--SIN' E-4'RA---SIN'DEC--SIN' E-4'RA---SIN'DEC--SIN' E-4'RA---SIN'DEC--SIN' E-4'RA---SIN'DEC--SIN' Sky to Page: page=1603&RA=182&Dec=62 --> page, scale, ctype, RA, Dec. x, y E-4 'RA---TAN''DEC--TAN' Best Page: RA=182&Dec=62 --> page, scale, ctype, RA, Dec. x, y E-4 'RA---SIN 'DEC--SIN' Page WCS: page= > page, scale, ctype, RA, Dec E-4 'RA---SIN' 'DEC--SIN' Replicated Implementations baseURL = (services try)(services try)

44 Hyperatlas Service Page to Sky: page=1603&x=200&y=500 --> RA, Dec, nx,n y, nz Relevant pages from sky region: tilesize=4096&ramin=200.0&ramax=202.0&decmin=11.0&decmax= > RA, Dec, nx,n y, nz Implementation baseURL = (services try)(services try) page 1015 ref point RA=200, Dec=10

45 GET services from Python import urllib hyperatlasURL = self.hyperatlasServer + "/getChart?atlas=" + atlas \ + "&RA=" + str(center1) + "&Dec=" + str(center2) stream = urllib.urlopen(hyperatlasURL) # result is a tab-separated line, so use split() to tokenize tokens = stream.readline().split('\t') print "Using page ", tokens[0], " of atlas ", atlas self.scale = float(tokens[1]) self.CTYPE1 = tokens[2] self.CTYPE2 = tokens[3] rval1 = float(tokens[4]) rval2 = float(tokens[5]) This code uses a service to find the best hyperatlas page for a given sky location

46 VOTable parser in Python import urllib import xml.dom.minidom stream = urllib.urlopen(SIAP_URL) doc = xml.dom.minidom.parse(stream) #Make a dictionary for the columns col_ucd_dict = {} for XML_TABLE in doc.getElementsByTagName("TABLE"): for XML_FIELD in XML_TABLE.getElementsByTagName("FIELD"): col_ucd = XML_FIELD.getAttribute("ucd") col_ucd_dict[col_title] = col_counter urlColumn = col_ucd_dict["VOX:Image_AccessReference"] formatColumn = col_ucd_dict["VOX:Image_Format"] raColumn = col_ucd_dict["POS_EQ_RA_MAIN"] deColumn = col_ucd_dict["POS_EQ_DEC_MAIN"] From a SIAP URL, we get the XML, and extract the columns that have the image references, image format, and image RA/Dec

47 VOTable parser in Python import xml.dom.minidom table=[] for XML_TABLE in doc.getElementsByTagName("TABLE"): for XML_DATA in XML_TABLE.getElementsByTagName("DATA"): for XML_TABLEDATA in XML_DATA.getElementsByTagName("TABLEDATA"): for XML_TR in XML_TABLEDATA.getElementsByTagName("TR"): row=[] for XML_TD in XML_TR.getElementsByTagName("TD"): data = "" for child in XML_TD.childNodes: data += row.append(data) table.append(row) Table is a list of rows, and each row is a list of table cells

48 Science Gateways

49 Grid Impediments Learn Globus Learn MPI Learn PBS Port code to Itanium Get certificate Get logged in Wait 3 months for account Write proposal and now do some science....

50 A better way: Graduated Security for Science Gateways Web form - anonymous some science.... Register - logging and reporting more science.... Authenticate X browser or cmd line big-iron computing.... Write proposal - own account power user

51 2MASS Mosaicking portal An NVO-Teragrid project Caltech IPAC

52 Three Types of Science Gateways Web-based Portals –User interacts with community-deployed web interface. –Runs community-deployed codes –Service requests forwarded to grid resources Scripted service call –User writes code to submit and monitor jobs Grid-enabled applications –Application programs on users' machines (eg IRAF) –Also runs program on grid resource

53 Secure Web services for Teragrid Access

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