Page 1. Distributed and Parallel Systems. High Performance Computing, Computational Grid, and Numerical Libraries. What is Grid Computing?

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1 QuickTime and a decompressor are needed to see this picture. High omputing, omputational Grid, and Numerical Libraries Distributed systems heterogeneous Distributed and Parallel Systems STI@home ntropia Grid based omputing Beowulf cluster Network of ws lusters w/ special interconnect Massively parallel systems homogeneous ASI Tflops Parallel Dist mem!! "!#$ %&' ()! +, ( - ( -. / ( ( # 3 4 ()! + What is Grid omputing? 6 7 ' 8 DATA AQUISITION ADVAND VISUALIZATION,ANALYSIS The omputational Grid is 97 - :- :- ; '- '- <' :- ; ( = 7? 7 IMAGING INSTRUMNTS OMPUTATIONAL RSOURS LARG-SAL DATABASS omputational Grids and lectric Power Grids <":-! " " :- # # $ % & % ' % & ( An merging Grid A7<B - # - % ) % % + %, &!, % & -. %, # & % $&!/ Page

2 Grids a Grid-Aware Numerical Libraries & $'$ +'& ) Software omponents Feedback Problem Real-time Monitor #:3( 3( ( ;+++%- + +#: 7 ; Whole- Program ompiler Source Application onfigurable Object Program Resource Negotiator Scheduler Negotiation Grid Runtime System ; % -+ ( ; ;++--- < ++ Libraries Binder 3#3D ;++ ++ ; F ; D- ; # ;++ < + Grid-Aware Numerical Libraries & $'$ +'& ) Libraries Software omponents Whole- Program ompiler Feedback Source Application onfigurable Object Program Problem Resource Negotiator Scheduler 3 & $'$ + ) 4 Real-time Monitor Negotiation Binder ) $ & 4 Grid Runtime System ScaLAPAK (:( /? 7 ::6 :# - < < D? "'3D'?( :( # 7#, '= :/'D '3"' ' #''3( '# '9! 6 ScaLAPAK Grid nabled # (:( 7 < G :7 :- G <- 7 - ( A 7B / - / < To Use ScaLAPAK a User Must:? - < / <<:,( ',( ',( '6 :# :? -? : 7 :? 7! 7 -! A H 3 I B % 3; 7 /7 7 ' ' Page

3 GrADS Numerical Library < <- 7 G 7? 7 87? 7 :? 7 < % # GrADS Library Sequence 7 = AB <- < $ $ ) 4 Resource Selector 7? % 7-' ' 7 % / 7 ' - 77 Arrays of Values Generated by Resource Selector 7 $!'?'# % : D,- << : / '' / ( - < ' ' - ScaLAPAK Model Model v T ( n, p) = f t f + vtv + mtm 3 n f = 3p! 7 n = (3 + log p) 4 p! 7 m = n(6 + log p)! 7 t f! t v! t m! 7 : :< # ' 7 : #A7B' ' : Page 3

4 ontract Development Application Launcher 7 7? 7-? : - <7! 7' <? : (? : A H H7 III6 perimental Hardware / Software Grid Resource Selector/ Modeler MacroGrid Testbed Type OS TOR YPHR OPUS luster 8 Dual Pentium III Red Hat Linu.. SMP luster 6 Dual Pentium III Debian Linu..7 SMP luster 8 Pentium II Red Hat Linu..6 Memory MB MB 8 or 6 MB PU speed MHz MHz MHz Network Fast thernet ( Mbit/s) (3om 39B) and switch (BayStack 3T) with 6 ports Gigabit thernet (SK- 9843) and switch (Foundry FastIron II) with 4 ports Myrinet (LANai 4.3) with 6 ports each 7 J ( J 3 :#= (:(K (!(+,(J,( :( :#. (?GAB Independent components being put together and interacting 7-7! 7! : # -! 7<< 8! / ' '! +) 8!! 7 MDS, NWS Model Library writer to supply Optimizer oarse Grid! 7 Model Validation Opus4 Opus3 Opus6 Opus Torc4 Torc6 Torc mem(mb) speed load Speed = 6% of the peak Latency Opus4 Opus3 Opus6 Opus Torc4 Torc6 Torc7 Opus Opus Opus Opus Torc Torc Torc Latency in msec Time (seconds) PDGSV Time Breakdown ScaLAPAK - PDGSV - Using collapsed NWS query from USB 4 machine available, using mainly torc, cypher, msc clusters at UTK [Jan ] 6. other. PDGSV 4. spawn 3. NWS. MDS. Bandwidth Opus4 Opus3 Opus6 Opus Torc4 Torc6 Torc7 Opus Opus Opus Opus Torc Torc Torc Bandwidth in Mb/s This is for a refined grid other PDGSV spawn NWS MDS Matri Size - Nproc Page 4

5 Time (seconds) ScaLAPAK across 3 lusters 3 OPUS OPUS, YPHR OPUS, TOR, YPHR 3 OPUS, 4 TOR, 6 YPHR 8 OPUS, 4 TOR, 4 YPHR 8 OPUS, TOR, 6 YPHR 6 OPUS, YPHR 8 OPUS, 6 YPHR 8 OPUS OPUS 8 OPUS Matri Size Time (seconds) torcs, 3 mscs QR Timing Breakdown ecution of QR factorization over the grid Application eecution Startup time modeling NWS MDS 3 torcs, 4 mscs 4 torcs, 6 mscs 4 8 Matri Size 4 torcs, 6 mscs 4 torcs, 7 mscs 4 torcs, 7 mscs PDSYVX Timing Breakdown GradSoft & ScaLAPAK Time (s) other_grid_overhead perf_modeling_time nws mds pdsyev_driver_overhead back transformation compute eigenvectors compute eigenvalues tridiagonal reduction PDSYVX - torcs, cyphers Uses torcs only Uses torcs and cyphers 7? 3 3 '( +:7 (:( ' ( :( ( (:( : I ( I G :? "FI / 7 < N-nproc Rescheduling/Redistribution perimental Results Rescheduling/Redistribution perimental Results Time (seconds) Scenario: start application on 4 cyphers machines. After 4 minutes into the run 4 addition processors become available. Want to reorganize the computation to take advantage of the etra computing capability. What s the additional cost? Time (seconds) Application eecution Redistribute time heckpoint read+write Restart time Start time Grid overhead NWS App: 36, 4 cyphers No rescheduling App: 36, 4 cyphers+4 torcs No rescheduling App: 36, 4 cyphers No rescheduling App: 36, 4 App: 36, 4 torcs cyphers+4 torcs App: 36, 4 torcs+4 cyphers, No rescheduling introduced 4 mins. into the run With rescheduling About % better performance even with redistribution and restart. Page

6 Time stimate (sec) Adhoc vs Annealing Scheduling () (9) stimated ecution Time for PDGSV Using Adhoc Scheduler and Annealing Scheduler Given 7 possible hosts; all GrADS 86 machines est_adhoc est_anneal () () (4) (6) (4) () (8) () N (nproc_adhoc) (nproc_anneal) 3 () () 3 (7) (7) Largest Problem Solved /8J' ), J #! % 3.,' 4, : )! <4& % JK + % + % (:( ) - 7. < % :. = 8. + < % D ( :( ompiler analogy 7 General Library Interface In the past: Isolation Motivation for Grid omputing (? : - - <' '- 7 #G7A B +7?! 7-3 :! - < <'7 ' < 77 '7 7? 7 7 Today: ollaboration The Grid! : - < "7 7 H 7/ - < A - B A 3- < G<' 3 B! L Page 6

7 ? 3,D! = ", Motivation for NetSolve NetSolve Network nabled Server 3 / 7- +-, : 7-9 ' 7 7'7' ' '9 "! '7 = G :7 A B NetSolve: The Big Picture NetSolve: The Big Picture Schedule Schedule Database Database AGNT(s) AGNT(s) Matlab Mathematica IBP Depot Matlab Mathematica A, B IBP Depot, Fortran, Fortran Java, cel Java, cel S A S S3 S4 S A S S3 S4 No knowledge of the grid required, RP like. No knowledge of the grid required, RP like. NetSolve: The Big Picture NetSolve: The Big Picture Schedule Schedule Matlab Mathematica Handle back IBP Depot AGNT(s) Database Matlab Mathematica Request S! IBP Depot AGNT(s) Database, Fortran, Fortran Java, cel Java, cel S A S S3 S4 S Answer () A A, B OP, handle Op(, A, B) S S3 S4 No knowledge of the grid required, RP like. No knowledge of the grid required, RP like. Page 7

8 Basic Usage Scenarios NetSolve Agent Agent 7 7 G- 7 '(:( ' '(:(':"!' (M!"'(:(! < A: B/ : / - A, B,? <- -< / 'A B # 7 '' 3#3D'9 3 3 # NetSolve Agent Agent NetSolve G; : : #3:( 3- <7- ' - < :7 8+ / A! B 3 D,# 7 3 G (:# #7'D' 7' - < 3 7< ;7<' 7<'< '9 NetSolve NetSolve # 7 / ; (N,'O < ( 7 A = netsolve( matmul, B, ); Possible parallelisms hidden. Page 8

9 Java This is a linear solver for dense matrices from the LAPAK Library. MATRIX DOUBL A Double precision VTOR DOUBL b Right VTOR DOUBL Generating New Services in NetSolve (? 7 - # NetSolve Parser/ ompiler New Service Added! Server Service Service Service Service New Service Task Farming - Multiple Requests To Single Problem ( ; "9:;( ;: D ; & < "9 A B ( - ' Data Persistence Data Persistence (cont d) 3 ( 8 "?( - -! + < / / command(a, sequence(a, B, B) ) result input A, command(a, intermediate output ) result D intermediate output D, input command3(d, ) Server Server netsl_begin_sequence( ); netsl( command, netsl( command, A, A, B, B, ); ); netsl( command, netsl( command, A, A,,, D); D); netsl( command3, netsl( command3, D, D,,, F); F); netsl_end_sequence(, D); result F Server University of Tennessee Deployment: Scalable Intracampus Research Grid SInRG NPAI Alpha Project - Mell: 3-D Monte-arlo Simulation of Neuro-Transmitter Release in Between ells USD (F. Berman, H. asanova, M. llisman), Salk Institute (T. Bartol), MU (J. Stiles), UTK (Dongarra, M. Miller, R. Wolski) Study how neurotransmitters diffuse and activate receptors in synapses blue unbounded, red singly bounded, green doubly bounded closed, yellow doubly bounded open The Knoville ampus has two DS-3 commodity Internet connections and one DS-3 Internet/Abilene connection. An O-3 ATM link routes IP traffic between the Knoville campus, National Transportation Research enter, and Oak Ridge National Laboratory. UT participates in several national networking initiatives including Internet (I), Abilene, the federal Net Generation Internet (NGI) initiative, Southern Universities Research Association (SURA) Regional Information Infrastructure (RII), and Southern rossroads (SoX). D - ;' "' ' "'" " ' - ' -< The UT campus consists of a meshed ATM O- being migrated over to switched Gigabit by early. Page 9

10 Web Interface Web Server NetSolve IPARS-enabled Servers #:( G '! ( " 7<'-' J? - #"/ D - < :'-'7< : + ' 8 D #:( - # #:(#; 'D! ( 3' 7' ' 7 Netsolve and SIRun SIRun torso defibrillator application hris Johnson, U of Utah Things Not Touched On onclusions 7F. # = -- = ( 7 33-<!< 3-< 3-< #,< : - D! +7? 3 ( ( (? 7 7 "/ (? = "/ 7 F 7< - / (?- 7 ( ( / 7? 7 - < ( '- / O- - < # 8 ' ' Page