Future Generation Computer Systems. Self-healing network for scalable fault-tolerant runtime environments

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1 Fuure Generaion Compuer Sysems 26 (2010) Conens liss available a ScienceDirec Fuure Generaion Compuer Sysems journal homepage: Self-healing nework for scalable faul-oleran runime environmens Thara Angskun, Graham Fagg, George Bosilca, Jelena Pješivac-Grbović, Jack Dongarra Deparmen of Compuer Science, The Universiy of Tennessee, 1122 Voluneer Blvd. Knoxville, TN 37996, USA a r i c l e i n f o a b s r a c Aricle hisory: Received 7 December 2006 Received in revised form 17 Sepember 2007 Acceped 29 April 2009 Available online 9 May 2009 Keywords: Faul olerance Rouing proocols Runime environmens Scalabiliy Self-healing The number of processors embedded on high performance compuing plaforms is growing daily o saisfy he user desire for solving larger and more complex problems. Scalable and faul-oleran runime environmens are needed o suppor and adap o he underlying libraries and hardware which require a high degree of scalabiliy in dynamic large-scale environmens. This paper presens a self-healing nework (SHN) for supporing scalable and faul-oleran runime environmens. The SHN is designed o suppor ransmission of messages across muliple nodes while also proecing agains recursive node and process failures. I will auomaically recover iself afer a failure occurs. SHN is implemened on op of a scalable faul-oleran proocol (SFTP). The experimenal resuls show ha boh he laes mulicas and broadcas rouing algorihms used in SHN are faser and more reliable han he original SFTP rouing algorihms Elsevier B.V. All righs reserved. 1. Inroducion Recenly, several high performance compuing plaforms have been insalled wih more han 10,000 CPUs, such as Blue-Gene/L a LLNL, BGW a IBM and Columbia a NASA [1]. However, as he number of componens increases, so does he probabiliy of failure. To saisfy he requiremens of such a dynamic environmen (where he available number of resources is flucuaing), a scalable and faul-oleran framework is needed. Many large-scale applicaions are implemened on op of message passing sysems for which he de faco sandard is he Message Passing Inerface (MPI) [2]. MPI implemenaions require suppor from parallel runime environmens, which are exensions of he operaing sysem services, and provide necessary funcionaliies (such as naming resoluion services) for boh he message passing libraries and applicaions. Alhough here are several exising parallel runime environmens for differen ypes of sysems, hey do no mee some of he major requiremens for MPI implemenaions: scalabiliy, porabiliy and performance. Typically, disribued OS and single sysem image sysems are no porable while he naure of Grid middle-ware has performance problems. The MPICH implemenaion [3] uses a parallel runime environmen called Muli-Purposed Daemon (MPD) [4] o provide scalabiliy and faul olerance hrough a ring opology for some operaions and a ree opology for ohers. Runime environmens of oher MPI implemenaions, such as Harness [5] of FT-MPI [6], Open RTE [7] Corresponding auhor. Tel.: address: angskun@cs.uk.edu (T. Angskun). of Open MPI [8] and LAM of LAM/MPI [9], do no currenly provide scalable or faul-oleran soluions for heir inernal communicaions. The scalabiliy and faul-olerance issues have been addressed in several neworking areas. However, hese approaches canno be used or hey are no efficien in he parallel runime environmens. Srucured peer-o-peer neworking based on disribued hash ables such as CAN [10], Chord [11], Pasry [12] and Tapesry [13] were designed for resource discovery. They are only opimized for unicas messages. Techniques used in sensor or large-scale ad hoc neworking based on gossiping (or he epidemic algorihm) [14,15] mainly focus on informaion aggregaion. A self-healing nework (SHN) ha can be used as a basis for consrucing a higher level, faul-oleran parallel runime environmen is described in his aricle. SHN was designed o suppor ransferring messages across muliple nodes efficienly, while proecing agains recursive node or process failures. SHN auomaically recovers iself o overcome he orphan siuaion (he siuaion where nodes are unreachable because he nework is bisecioned). SHN was buil on op of a scalable and faul-oleran proocol (SFTP) [16] and auomaically recovers iself afer a failure occurs. The SFTP is based on a k-ary sibling ree. The k-ary sibling ree opology is a k-ary ree, where k is he number of fan-ous (k 2), and he nodes on he same level (same deph on he ree) are linked ogeher using a ring opology. The ree is primarily designed o allow scalabiliy for broadcas and mulicas operaions, while he ring is used o provide a well-undersood secondary pah for ransmission when he ree is damaged during failure condiions. The proocol has been formally proven by SPIN [17] o work under boh normal and failure modes X/$ see fron maer 2009 Elsevier B.V. All righs reserved. doi: /j.fuure

2 480 T. Angskun e al. / Fuure Generaion Compuer Sysems 26 (2010) (a) Node 4 dies. (b) Nodes 4 and 5 die. Fig. 1. SHN afer recovery High possibiliy of orphan (a) Excepional case. (b) Rearranged nodes. Fig. 2. Orphan prevenion. The srucure of his paper is as follows. The nex secion inroduces he self-healing nework and is recovery algorihm. Secion 3 presens he rouing algorihm along wih some experimenal resuls, while Secion 4 analyzes and discusses he reliabiliy of self-healing nework, followed by conclusions and fuure work in Secion Self-healing nework (SHN) 2.1. Overview The self-healing nework (SHN) is designed o suppor generic runime environmens of MPI implemenaions. Currenly, he inegraion of SHN in a faul-oleran implemenaion of message passing inerface called FT-MPI, as well as in he modular MPI implemenaion called Open MPI, is in progress. The nework is designed o suppor various operaions needed by scalable and faul-oleran MPI runime environmens. The examples of hose operaions and he deails on heir usage are described below. Disribued direcory service Direcory service is a sorage ha mainains informaion used during execuion of an MPI job such as conac informaion of each process, coordinaor of recovery algorihm in FT-MPI, ec. SHN enables us o use he nework as a disribued direcory service by mapping necessary informaion o he logical node ID. Scalable and faul-oleran informaion managemen (updae, query) can be done wih unicas messages of SFTP rouing (similar o resource discovery in he srucured peero-peer neworking). Sandard I/O redirecion Alhough he MPI sandard does no define how an inpu and an oupu can be reaed, mos of he MPI implemenaion redirecs he sandard oupu and he sandard error o he user erminal (if no run under he bach scheduling). This operaion can be done using he k-ary ree as a main roue o forward he sandard oupu/error and using he ring in case of failures. Monioring framework A monioring framework provides informaion such as processes, nodes, messages for ool and applicaion developmen. Examples of hose ools are parallel debuggers, runime faul deecors, runime verificaion and load balancers, ec. To build a scalable and faul-oleran monioring framework, all of he communicaion underneah he framework can use muliple ypes of message ransmissions (unicas, mulicas and broadcas) provided by SHN. In general, SHN provides he capabiliy o send unicas, mulicas and broadcas messages from any nodes while proecing he effecive message delivery agains node and process failures SHN recovery There are some siuaions where nodes do no die bu become unreachable due o nework bisecioning. This siuaion can be prevened by self-recovery. When a node deecs ha a neighbor disappears, i will send a unicas message o esablish he connecion wih he nex neighbor in he ring in he direcion of he dead node. This procedure will be coninued unil he connecion wih one of he nodes in he ring can be esablished or unil he node idenifies iself as he las remaining node in he ring. If wo nodes ry o esablish a connecion a he same ime, he connecion which is iniiaed by he higher ID will be dropped. Fig. 1(a) illusraes an example where logical node 4 dies. All neighbors of node 4 will begin o recover he logical opology by re-esablishing heir connecions in he appropriae direcion. If node 5 also dies, he same recovery procedure will occur as shown in Fig. 1(b). There is an excepion when he number of nodes on he las level (highes deph) of he ree is a mos equal o k (k is he fan-ou as shown in Fig. 2(a)). In his case, he conac informaion of he nodes on he las level should be propagaed o he grandparen in order o avoid he nework bisecion if he paren disappears. Alernaively, if here are a leas wo nodes in he las level, hese nodes can be rearranged o reduce he possibiliy of orphan nodes as shown in he Fig. 2(b). However, he iniializaion phase of his opology is more complex. The simples soluion o preven his problem is o change he fan-ou for a paricular number of nodes such ha he number of nodes in he las level of he ree is always more han ha of fan-ou as shown in Fig. 3. The fan-ous beween he lower bound and he upper bound, excep hose excepional cases in he Fig. 3, are safe from he high possibiliy of an orphan problem. The experimens have been conduced on an AMD Ahlon TM 64 Processor GHz machine wih 1 GB of main memory, running on Linux kernel Rouing algorihm in SHN The SHN rouing algorihm is based on he SFTP rouing algorihm [16]. The iniial sysem proocol, unicas message proocol,

3 T. Angskun e al. / Fuure Generaion Compuer Sysems 26 (2010) Fig. 3. Safe fan-ou. and broadcas from a specific roo proocol are he same as he SFTP proocol. The new mulicas and broadcas rouing algorihms from any nodes in he nework, which are exensions of he SFTP rouing algorihm, have been added. Boh can be used before (including some node failures) and afer recovery of he logical opology. The SHN rouing algorihms can be described as follows Mulicas messages in SHN The mulicas from any nodes in SHN is he capabiliy o send messages o several desinaions (1 o m, where m < n). Unlike he IP mulicas, mulicas group managemen (group creaion and erminaion) is no required. The mulicas group members are embedded in he message header. Mulicas messages in SFTP are delivered by a sender o he firs desinaion in he desinaion liss. Then, he firs desinaion will forward he message o he nex desinaion and so on. If an inermediae node is one of he nodes in he desinaion lis, i will remove iself from he lis. The order of nodes in he desinaion lis is a descending order sored by he number of hops from a sender o hose desinaions (i.e., he larges number of hops firs). This rouing algorihm works fine if he desinaion nodes are consecuive or if hey are locaed in he same area of he ree. The new mulicas rouing algorihm in SHN is an enhancemen of he SFTP mulicas rouing algorihm. The mulicas message can be spli a an inermediae node, if he shores pahs o hose desinaion nodes are no in he same direcion from he inermediae node poin of view. However, if here are more han one shores pah o a desinaion, he inermediae node will choose he nex hop ha can go along wih oher desinaions. Fig. 4(a) shows an example of node 2 sending a mulicas message o nodes 7, 8 and 9 wih he new rouing algorihm. Fig. 5. Mulicas resul (wih 1 failure node). In case of failure, if a node deecs ha he nex hop for he mulicas messages has died, i auomaically reroues he mulicas messages using an alernae nex hop as shown in Fig. 4(b). When a node receives a mulicas message, i will firs deermine he header and choose he nex hop for each mulicas desinaion according o he shores pah o hem. The node will recreae he header corresponding o he direcion of each nex hop. Messages ha conain he larges number of hops will be forwarded firs, in order o increase he nework hroughpu (by allowing a large number of messages simulaneously ino he nework). Fig. 5 confirms ha he new mulicas rouing algorihm is faser han he original algorihm used in he SFTP. The experimenal resuls were obained from an average number of seps for sending mulicas messages o wo desinaions wih a dead node (fan-ou = 2). The wo desinaion nodes (D) were obained ( from combinaions of all possible nodes (N), i.e., source node D and he dead node were randomly seleced Broadcas messages in SHN N D ), where a Broadcas from any node rouing proocol is an enhancemen of broadcas rouing in SFTP. In SFTP, he broadcas is done by sending messages o a roo of he ree, which will hen forward he messages o he res of he ree. Only he ree opology of SFTP is used o preven a broadcas sorm and duplicae messages. The ring is used only in he case of failure. The firs obvious improvemen of his rouing proocol is o allow a node beween a source and a roo of he ree o send messages o heir children afer hey send he messages o heir paren (called up down), as shown in Fig. 6(a) wih node 4 as he roo. The second improvemen is using a logical spanning ree from he source as shown in Fig. 6(b). When a (a) Normal circumsances. (b) Failure circumsances. Fig. 4. Mulicas message ransmission.

4 482 T. Angskun e al. / Fuure Generaion Compuer Sysems 26 (2010) (a) Up down. (b) Spanning ree from source. Fig. 6. Broadcas message ransmission. Fig. 8. Bahub curve. Fig. 7. Broadcas resul. node receives a broadcas message, i will calculae he nex hops using spanning rees from he source node. There are wo seps involved in he compuaion of he nex hop. The firs sep is o creae a spanning ree using a source node as he roo node of he ree. The spanning-ree creaion algorihm is based on a modified version of he breah firs search wih a graph coloring algorihm. The second sep is o calculae he nex hop. The nex hop is chosen from children of each node according o he spanning ree ha has he highes cos among is children. The cos is compued from he number of seps used o send a message o all nodes in he children s subrees. In case of failure, a broadcas message is encapsulaed ino a mulicas message, and hen he message is sen from he paren of he failure node o is children in he spanning ree. Fig. 7 indicaes ha he up down algorihm is marginally faser han he original SFTP, while he new spanning-ree broadcas rouing algorihm is significanly faser han he SFTP broadcas rouing algorihm due o increased parallelism. The experimenal resuls were obained from an average number of seps for sending a broadcas message from every node (fan-ou = 2). 4. Reliabiliy analysis of SHN The reliabiliy of SHN has been analyzed using he discreeeven simulaion echnique [18]. The reliabiliy is defined as he abiliy o mainain an operaion over a period of ime, i.e., he reliabiliy R() = Pr (he nework is operaional in [0, ]). SHN is operaional if i can successfully deliver messages from any source o he alive desinaion(s), even when some nodes in he rouing pah die. The cumulaive disribuion funcion (cdf), F() can be defined as F() = 0 f ()d where f () is he probabiliy densiy funcion (pdf) ha is associaed wih he lifeime of he nework. There are several characerisics ha are commonly used in reliabiliy analysis. These characerisics can be deermined from he pdf and cdf, e.g., reliabiliy funcion, hazard funcion and mean ime beween failures. The reliabiliy funcion (or survival funcion), R(), is he probabiliy ha SHN survives up o ime. I can be defined as R() = 1 F(). The simulaion assumes ha here is no failure a he iniial ime, i.e., = 0, R(0) = 1. The Hazard funcion, h() is he failure rae of he nework. The h() is defined by h() = f () R(). The failure rae in pracice has a bahub shape [19]. The hazard funcion of SHN is also assumed o change as he bahub curve, which consiss of hree phases: decreasing failure rae (burn in), consan failure rae and increasing failure rae (wearing ou) as shown in Fig. 8. The mean ime beween failure (MTBF) is defined o be he average (or expeced) lifeime of he nework. The MTBF is defined by MTBF = R()d. 0 The probabiliy densiy funcion of SHN is assumed o follow he Weibull disribuion [20]. This disribuion has he capabiliy o model he bahub curve. The pdf of he Weibull disribuion is given by [ ( ) ] β f () = βα β β 1 exp α where α is he scale parameer and β is he shape parameer. The associae funcions of he Weibull disribuion can be summarized

5 T. Angskun e al. / Fuure Generaion Compuer Sysems 26 (2010) (a) β effec. (b) α effec. Fig. 9. Effecs of shape (β) and scale (α). Table 1 Associae characerisic funcions of disribuions. Characerisics General Weibull CDF, F() 0 f ()d 1 e ( α ) β Reliabiliy funcion, R() 1 F() e ( α ) β f () Hazard funcion, h() βα β β 1 R() ( ) MTBF R()d αγ β in Table 1. The Γ denoes a gamma funcion where Γ (n) is defined as Γ (n) = e x x n 1 dx. 0 If n is an ineger hen Γ (n) = (n 1)!. In he mos general form, he 3-parameer form [21] of he Weibull includes an addiional waiing ime parameer µ (someimes called a shif or locaion parameer). The formulas for he 3-parameer Weibull can be easily obained from hese formulas by subsiuing occurrences of by ( µ). The Weibull lifeime disribuion assumes ha he hazard funcion is ime dependen. The hazard funcion is dependen on he value of β as shown in Fig. 9(a). If β < 1, he hazard funcion is he decreasing funcion (infan moraliy or burn in), i.e., he older i is, he less likely i fails (he firs phase of he bahub curve). If β = 1, he age has no effec. I is he second phase of he bahub curve. If β > 1, he hazard funcion is he increasing funcion (wearing ou), i.e., he older i is, he more likely i is o fail. I is he hird phase of he bahub curve. If 1 < β < 2, he hazard funcion is concave (increasing a a decreasing rae). On he oher hand, he hazard funcion is convex (increasing a an increasing rae), if β > 2. Fig. 9(b) shows he effecs of he characerisic life (α) on he failure rae, which affecs he spread (scale) of he disribuion. The simulaion assumes ha MTBF of he nework is hree years (26,280 h). Several β and is corresponding α parameers have been esed as shown in Table 2. If β equals o 1, he hazard funcion is ime independen, i.e., he nework is equally likely o fail a any momen during is lifeime, regardless of how old i is. The failure rae is known o be a consan ( 1 ). This is a special case α where Weibull becomes he exponenial disribuion [22]. Fig. 10 illusraes he effec of β and is corresponding α parameers (as shown in Table 2) in he Weibull lifeime disribuion o he percen average of success of mulicas operaions. I shows ha he new mulicas rouing used in SHN is more reliable han he original SFTP rouing for every value of he β parameer. Fig. 11 Table 2 Weibull parameers (MTBF=26,280). β α h() R() , , e ( , e ( , , 111, e ( , , e ( , , e ( Fig. 10. Weibull on mulicas (MTBF=26,280). ) ,140 ) 26,280 29, , , illusraes he effec of β and is corresponding α parameers (as shown in Table 2) in he Weibull lifeime disribuion o he percen average of success of broadcas operaions. I shows ha he spanning-ree (from source) broadcas rouing algorihm used in SHN is he mos reliable rouing algorihm when compared wih he up down and he original SFTP rouing for all values of he β parameer. 5. Conclusions and fuure work The self-healing nework (SHN) for parallel runime environmens was designed and developed o suppor runime environmens of MPI implemenaions. SHN is implemened on op of a scalable faul-oleran proocol (SFTP). Simulaed performance resuls indicae ha he new broadcas and mulicas rouing algorihms of SHN are faser and more reliable han he original SFTP rouing algorihms. ) 1.5 ) 2.0 ) 2.5

6 484 T. Angskun e al. / Fuure Generaion Compuer Sysems 26 (2010) (a) SFTP. (b) Up down. (c) Spanning ree. Fig. 11. Weibull on broadcas (MTBF=26,280). There are several improvemens ha we plan for he near fuure. Making he proocol aware of he underlying nework opology (in boh he LAN and WAN environmens) will grealy improve he overall performance of boh he broadcas and mulicas message disribuion. This is equivalen o adding a funcion cos on each possible pah and inegraing his funcion cos wih he compuaion of he shores pah. In he longer erm, we hope ha SHN will become he basic message disribuion of he runime environmen wihin he FT-MPI and Open MPI runime sysems. References [1] J.J. Dongarra, H. Meuer, E. Srohmaier, TOP500 supercompuer sies, Supercompuer 13 (1) (1997) [2] MPI Forum, MPI: A message-passing inerface sandard, Tech. Rep., [3] W. Gropp, E. Lusk, N. Doss, A. Skjellum, A high-performance, porable implemenaion of MPI message passing inerface sandard, Parallel Compuing 22 (6) (1996) [4] R. Buler, W. Gropp, E.L. Lusk, A scalable process-managemen environmen for parallel program, in: Recen Advances in PVM and MPI, in: LNCS, vol. 1908, Springer, 2000, pp [5] G.E. Fagg, J.J. Dongarra, HARNESS faul oleran MPI design, usage and performance issues, Fuure Generaion Compuer Sysems 18 (8) (2002) [6] M. Beck, J.J. Dongarra, G.E. Fagg, G.A. Geis, P. Gray, J. Kohl, M. Migliardi, K. Moore, T. Moore, P. Papadopoulous, S.L. Sco, V. Sunderam, HARNESS: A nex generaion disribued virual machine, Fuure Generaion Compuer Sysems 15 (5 6) (1999) [7] R.H. Casain, T.S. Woodall, D.J. Daniel, J.M. Squyres, B. Barre, G.E. Fagg, The open run-ime environmen (openre): A ransparen muli-cluser environmen for high-performance compuing, in: Recen Advances in PVM and MPI, in: LNCS, vol. 3666, Springer, 2005, pp [8] E. Gabriel, G.E. Fagg, G. Bosilca, T. Angskun, J.J. Dongarra, J.M. Squyres, V. Sahay, P. Kambadur, B. Barre, A. Lumsdaine, R.H. Casain, D.J. Daniel, R.L. Graham, T.S. Woodall, Open MPI: Goals, concep, and design of a nex generaion MPI implemenaion, in: Recen Advances in PVM and MPI, in: LNCS, vol. 3241, Springer, 2004, pp [9] G. Burns, R. Daoud, J. Vaigl, LAM: An open cluser environmen for MPI, in: Proceedings Supercompuing Symposium, 1994, pp [10] S. Ranasamy, P. Francis, M. Handley, R. Karp, S. Shenker, A scalable conen addressable nework, Tech. Rep. TR , Berkeley, CA, [11] I. Soica, R. Morris, D. Karger, F. Kaashoek, H. Balakrishnan, Chord: A scalable peer-o-peer lookup service for inerne applicaions, in: Proceedings of he 2001 ACM SIGCOMM Conference, 2001, pp [12] A. Rowsron, P. Druschel, Pasry: Scalable, decenralized objec locaion, and rouing for large-scale peer-o-peer sysems, Lecure Noes in Compuer Science 2218 (2001) [13] B.Y. Zhao, J.D. Kubiaowicz, A.D. Joseph, Tapesry: An infrasrucure for fauloleran wide-area locaion and rouing, Tech. Rep. UCB/CSD , UC Berkeley, April [14] I. Gupa, R. van Renesse, K. Birman, Scalable faul-oleran aggregaion in large process groups, in: Proceedings of The Inernaional Conference on Dependable Sysems and Neworks, DSN, 2001, pp [15] R.V. Renesse, Y. Minsky, M. Hayden, A gossip-syle failure deecion service, Tech. Rep. TR , 28, [16] T. Angskun, G.E. Fagg, G. Bosilca, J.P. vac Grbovic, J. Dongarra, Scalable faul oleran proocol for parallel runime environmens, in: Recen Advances in PVM and MPI, in: LNCS, vol. 4192, Springer, 2006, pp [17] G.J. Holzmann, The model checker SPIN, IEEE Transacions on Sofware Engineering 23 (5) (1997) [18] L.M. Leemis, S.K. Park, Discree-Even Simulaion: A Firs Course, Prenice Hall, [19] M. Xie, C.D. Lai, Reliabiliy analysis using an addiive Weibull model wih bahub-shaped failure rae funcion, Reliabiliy Engineering and Sysem Safey 52 (1) (1995) [20] A. Plai, The Weibull disribuion wih ables, Indusrial Qualiy Conrol 19 (5) (1962) [21] G.W. Cran, Momen esimaors for he 3-parameer Weibull disribuion, IEEE Transacions on Reliabiliy 37 (4) (1988) [22] N. Balakrishnan, A.P. Basu, The Exponenial Disribuion: Theory, Mehods and Applicaions, Gordon and Breach Publishers, Thara Angskun received his Bachelor and Maser degree in Compuer Engineering from Kasesar Universiy, Thailand. Currenly, he is a Ph.D. suden and Graduae Research Assisan a he Innovaive Compuing Laboraory, Deparmen of Compuer Science, Universiy of Tennessee, Knoxville. His major research ineress are in parallel and disribued environmens, message passing, high performance compuing, compuer neworking, cluser and grid compuing. He is a developer of several projecs including OpenSCE, KSIX, ACI, CAMETA, Harness/FT-MPI and Open MPI. Graham Fagg received his B.Sc. in Compuer Science and Cyberneics from he Universiy of Reading (UK) in 1991 and a Ph.D. in Compuer Science in Currenly he is a Research Associae Professor a he Universiy of Tennessee. His curren research ineress include disribued scheduling, resource managemen, performance predicion, profiling, benchmarking, cluser managemen ools, parallel and disribued IO and high speed neworking. He is currenly involved in he developmen of a number of disribued, meacompuing and GRID middleware sysems including SNIPE/2, HARNESS, faul-oleran MPI (FT-MPI) and Open MPI. He is a member of he IEEE. George Bosilca is a Senior Research Associae a he Innovaive Compuing Laboraory (ICL). He is also an Adjunc Professor in Compuer Science Deparmen a he Universiy of Tennessee, Knoxville. He received a Ph.D. degree in parallel archiecures from he Universiy de Paris XI. He was he main developer of he channel memory subsysem for MPICH-V. Dr. Bosilca is currenly working on he Open MPI projec.

7 T. Angskun e al. / Fuure Generaion Compuer Sysems 26 (2010) Jelena Pjesivac-Grbovic is a Graduae Research Assisan a he Innovaive Compuing Laboraory a he Universiy of Tennessee a Knoxville, working oward Ph.D. degree in Compuer Science. She received M.S. in Compuer Science from he Universiy of Tennessee a Knoxville, and B.S. degrees in Compuer Science and Physics from Ramapo College of New Jersey. Her research ineress are parallel communicaion libraries and compuer archiecures, scienific and grid compuing, and modeling of biophysical sysems. She is a developer on Harness/FT-MPI projec. Jack Dongarra holds an appoinmen as Universiy Disinguished Professor of Compuer Science in he Compuer Science Deparmen a he Universiy of Tennessee and holds he ile of Disinguished Research Saff in he Compuer Science and Mahemaics Division a Oak Ridge Naional Laboraory (ORNL), and is an Adjunc Professor in he Compuer Science Deparmen a Rice Universiy. He specializes in numerical algorihms in linear algebra, parallel compuing, use of advanced-compuer archiecures, programming mehodology, and ools for parallel compuers. He is a Fellow of he AAAS, ACM, and he IEEE and a member of he Naional Academy of Engineering.