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Where is the Eno Law Office Preservation Elop in Pine Plains New York located?
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∙ 9y ago
The address of the Eno Law Office Preservation Elop is: 403 Lake Road, Pine Plains, NY 12567-5558
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∙ 9y ago
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When was Stephen Elop born?
Stephen Elop was born on 1963-12-31.
Where is the Nokia company or who is the founder of Nokia?
Nokia was originally from Finland. And founder of nokia is Stephen elop.
Who is the ceo of Nokia in india?
The CEO of Nokia in India is currently Rajeev Suri. The previous CEO of Nokia India was Stephen Elop.
What has the author Emanuel de Oliveira Pereira written?
Emanuel de Oliveira Pereira has written: 'Os casos de Elop Morgan'
When you switch on nokia mobile you see two hands shaking whose are those hands?
One is yours and the other is nokia's president and CEO Stephen Elop.
What are some seven letter words with 1st letter E and 2nd letter L and 3rd letter O and 4th letter P and 7th letter G?
According to SOWPODS (the combination of Scrabble dictionaries used around the world) there are 1 words with the pattern ELOP--G. That is, seven letter words with 1st letter E and 2nd letter L and 3rd letter O and 4th letter P and 7th letter G. In alphabetical order, they are: eloping
What is difference between android and symbian mobile phones?
Android is a modern, Google - based operating system used in phones and Tablets which is open source. Android is the most versatile and popular of the new, modern mobile phone OS. Symbian is a proprietary (Nokia) operating system for mobile phones and on 11 February 2011, Nokia's CEO Stephen Elop, a former Microsoft employee, unveiled a new strategic alliance with Microsoft, and announced it would replace Symbian and MeeGo with Windows Phone 7. This places doubts over the future of Symbian which is now looking a bit dated as an OS.
What actors and actresses appeared in Arto Nyberg - 2004?
The cast of Arto Nyberg - 2004 includes: Veikko Aaltonen as himself Jouko Ahola as himself Anitta Ahonen as herself Lenita Airisto as herself Ismo Alanko as himself Muska as herself Darude as himself Hector as himself Frederik as himself Amin Asikainen as himself Valtteri Bottas as himself Pamela Des Barres as herself Samuli Edelmann as himself Stephen Elop as himself Tarja Halonen as herself Renny Harlin as himself Pasi Heikura as himself Maria Heiskanen as herself Jalmari Helander as himself Pedro Hietanen as himself Tuomas Holopainen as himself Antti Isokangas as himself Antti Jokinen as himself Mikael Jungner as himself Timo Kahilainen as himself Katja Kallio as herself Angelika Kallio as herself Ile Kallio as himself Ismo Kallio as himself Asko Kallonen as himself Ilkka Kanerva as himself Tapani Kansa as Himself - Guest Hannu Karpo as himself Anssi Kela as himself Leea Klemola as herself Markku Koivisto as himself Timo Kojo as himself Ere Kokkonen as himself Jaakko Kolmonen as himself Kaija Koo as herself Arja Koriseva as herself Kerkko Koskinen as himself Kosti Kotiranta as Joulupukki Heikki Kovalainen as himself Laura Lagercrantz as herself Aarno Laitinen as himself Lasse Lehtinen as himself Mikko Leppilampi as himself Juice Leskinen as himself Jyrki Liikka as himself Arvi Lind as himself Olli Lindholm as himself Pentti Linkola as himself Katri Manninen as herself Pirkko Mannola as herself Auli Mantila as herself Anne Marie Pohtamo as herself Karita Mattila as herself Andy McCoy as himself Nina Mikkonen as herself Pelle Miljoona as himself Eero Milonoff as himself Minttu Mustakallio as herself Ville Nieminen as himself Alex Nieminen as himself Marita Nordberg as herself Tuomari Nurmio as himself Miia Nuutila as herself Arto Nyberg as Himself - Host Lilli Paasikivi as herself Jussi Parviainen as himself Kari Peitsamo as himself Ari Peltonen as himself Emilia Pikkarainen as herself Kimmo Pohjonen as himself Samuli Putro as himself Simo Rantalainen as himself Mari Rantasila as herself Mitro Repo as himself Jukka Rintala as himself Vicky Rosti as herself Simo Routarinne as himself Alpo Rusi as himself Jarkko Ruutu as himself Olli Saarela as himself Pirkko Saisio as herself Krisse Salminen as herself Eppu Salminen as himself Matti Salminen as himself Esko Salminen as himself Markus Selin as himself Jaakko Selin as himself Martti Servo as himself Johanna Sinisalo as herself Anna Sorainen as herself Katariina Souri as herself Alexander Stubb as himself Toivo Sukari as Himself - Guest Raimo Summanen as himself Martti Suosalo as himself Rita Tainola as herself Villu Tamme as himself Jari Tervo as himself Jonna Tervomaa as herself Esa Tikkanen as himself Erkki Toivanen as himself Pamela Tola as herself Jukka Tolonen as himself Samuli Torssonen as himself Erkki Tuomioja as himself Paula Tuovinen as herself Allan Tuppurainen as himself Tarja Turunen as herself Ville Valo as himself Mato Valtonen as himself Maija Vilkkumaa as herself Peter von Bagh as himself Laura Voutilainen as herself Toni Wirtanen as himself Essi Wuorela as herself Sami Yaffa as himself Ben Zyskowicz as himself
Discuss the differences and similarities between MPI and PVM Discuss the benefits and?
MPI (Message P assing In terface) is sp eciØcation for message-passing libraries that can b e used for writing p ortable parallel programs. What do es MPI do? When w e sp eak ab out parallel programming using MPI, w e imply that: ≤ A Øxed set of pro cesses is created at program initialization, one pro cess is created p er pro cessor ≤ Eac h pro cess kno ws its p ersonal n um b er ≤ Eac h pro cess kno ws n um b er of all pro cesses ≤ Eac h pro cess can comm unicate with other pro cesses ≤ Pro cess can't create new pro cesses (in MPI{1), the group of pro cesses is static What is PVM? PVM (P arallel Virtual Mac hine) is a soft w are pac k age that allo ws a heterogeneous collection of w orkstations (host p o ol) to function as a single high p erformance parallel virtual mac hine. PVM, through its virtual mac hine, pro vides a simple y et useful distributed op erating system. It has daemon running on all computers making up the virtual mac hine. PVM daemon (p vmd) is UNIX pro cess, whic h o v ersees the op eration of user pro cesses within a PVM application and co ordinates in ter-mac hine PVM comm unications. Suc h p vmd serv es as a message router and con troller. One p vmd runs on eac h host of a virtual mac hine, the Ørst p vmd, whic h is started b y hand, is designated the master, while the others, started b y the master, are called sla v es. It means, that in con trast to MPI, where master and sla v es start sim ultaneously , in PVM master m ust b e started on our lo cal mac hine and then it automatically starts daemons on all other mac hines. In PVM only the master can start new sla v es and add them to conØguration 7 or delete sla v e hosts from the mac hine. Eac h daemon main tains a table of conØguration and handles information relativ e to our parallel virtual mac hine. Pro cesses comm unicate with eac h other through the daemons: they talk to their lo cal daemon via the library in terface routines, and lo cal daemon then sends/receiv es messages to/from remote host daemons. General idea of using MPI and PVM is the follo wing: The user writes his application as a collection of co op erating pro cesses (tasks), that can b e p er- formed indep enden tly in diÆeren t pro cessors. Pro cesses access PVM/MPI resources through a library of standard in terface routines. These routines allo w the initiation and termination of pro- cesses across the net w ork as w ell as comm unication b et w een pro cesses. 3.3 What is not diÆeren t? Despite their diÆerences, PVM and MPI certainly ha v e features in common. In this section w e review some of the similarities. 3.3.1 P ortabilit y Both PVM and MPI are p ortable; the sp eciØcation of eac h is mac hine indep enden t, and im- plemen tations are a v ailable for a wide v ariet y of mac hines. P ortabilit y means, that source co de written for one arc hitecture can b e copied to a second arc hitecture, compiled and executed without mo diØcation. 3.3.2 MPMD Both MPI and PVM p ermit diÆeren t pro cesses of a parallel program to execute diÆeren t exe- cutable binary Øles (This w ould b e required in a heterogeneous implemen tation, in an y case). That is, b oth PVM and MPI supp ort MPMD programs as w ell as SPMD programs, although again some implemen tation ma y not do so (MPICH, LAM { supp ort). 3.3.3 In terop erabilit y The next issue is in terop erabilit y { the abilit y of diÆeren t implemen tations of the same sp eciØ- cation to exc hange messages. F or b oth PVM and MPI, v ersions of the same implemen tation (Oak Ridge PVM, MPICH, or LAM) are in terop erable. 3.3.4 Heterogeneit y The next imp ortan t p oin t is supp ort for heterogeneit y . When w e wish to exploit a collection of net w ork ed computers, w e ma y ha v e to con tend with sev eral diÆeren t t yp es of heterogeneit y [GBD + 94]: ≤ arc hitecture The set of computers a v ailable can include a wide range of arc hitecture t yp es suc h as PC class mac hines, high-p erformance w orkstations, shared-memory m ultipro cessors, v ector sup ercom- puters, and ev en large MPPs. Eac h arc hitecture t yp e has its o wn optimal programming metho d. Ev en when the arc hitectures are only serial w orkstations, there is still the prob- lem of incompatible binary formats and the need to compile a parallel task on eac h diÆeren t mac hine. 8 ≤ data format Data formats on diÆeren t computers are often incompatible. This incompatibilit y is an imp or- tan t p oin t in distributed computing b ecause data sen t from one computer ma y b e unreadable on the receiving computer. Message passing pac k ages dev elop ed for heterogeneous en viron- men ts m ust mak e sure all the computers understand the exc hanged data; they m ust include enough information in the message to enco de or deco de it for an y other computer. ≤ computational sp eed Ev en if the set of computers are all w orkstations with the same data format, there is still heterogeneit y due to diÆeren t computational sp eeds. The problem of computational sp eeds can b e v ery subtle. The programmer m ust b e careful that one w orkstation do esn't sit idle w aiting for the next data from the other w orkstation b efore con tin uing. ≤ mac hine load Our cluster can b e comp osed of a set of iden tical w orkstations. But since net w ork ed com- puters can ha v e sev eral other users on them running a v ariet y of jobs, the mac hine load can v ary dramatically . The result is that the eÆectiv e computational p o w er across iden tical w orkstations can v ary b y an order of magnitude. ≤ net w ork load Lik e mac hine load, the time it tak es to send a message o v er the net w ork can v ary dep ending on the net w ork load imp osed b y all the other net w ork users, who ma y not ev en b e using an y of the computers in v olv ed in our computation. This sending time b ecomes imp ortan t when a task is sitting idle w aiting for a message, and it is ev en more imp ortan t when the parallel algorithm is sensitiv e to message arriv al time. Th us, in distributed computing, heterogeneit y can app ear dynamically in ev en simple setups. Both PVM and MPI pro vide supp ort for heterogeneit y . As for MPI, diÆeren t datat yp es can b e encapsulated in a single deriv ed t yp e, thereb y allo wing comm unication of heterogeneous messages. In addition, data can b e sen t from one arc hitecture to another with data con v ersion in heterogeneous net w orks (big-endian, little-endian). Although MPI sp eciØcation is designed to encourage heterogeneous implemen tation, some implemen tations of MPI ma y not b e used in a heterogeneous en vironmen t. Both the MPICH and LAM are implemen tations of MPI, whic h supp ort heterogeneous en vironmen ts. The PVM system supp orts heterogeneit y in terms of mac hines, net w orks, and applications. With regard to message passing, PVM p ermits messages con taining more than one datat yp e to b e exc hanged b et w een mac hines ha ving diÆeren t data represen tations. In summary , b oth PVM and MPI are systems designed to pro vide users with libraries for writing p ortable, heterogeneous, MPMD programs. 3.4 DiÆerences PVM is built around the concept of a virtual mac hine whic h is a dynamic collection of (p oten- tially heterogeneous) computational resources managed as a single parallel computer. The virtual mac hine concept is fundamen tal to the PVM p ersp ectiv e and pro vides the basis for heterogeneit y , p ortabilit y , and encapsulation of function that constitute PVM. In con trast, MPI has fo cused on message-passing and explicitly states that resource managemen t and the concept of a virtual mac hine are outside the scop e of the MPI (1 and 2) standard [GKP96 ]. 9 3.4.1 Pro cess Con trol Pro cess con trol refers to the abilit y to start and stop tasks, to Ønd out whic h tasks are running, and p ossibly where they are running. PVM con tains all of these capabilities { it can spa wn/kill tasks dynamically . In con trast MPI {1 has no deØned metho d to start new task. MPI{2 con tains functions to start a group of tasks and to send a kill signal to a group of tasks [NS02]. 3.4.2 Resource Con trol In terms of resource managemen t, PVM is inheren tly dynamic in nature. Computing resources or "hosts" can b e added and deleted at will, either from a system "console" or ev en from within the user's application. Allo wing applications to in teract with and manipulate their computing en vironmen t pro vides a p o w erful paradigm for ≤ load balancing | when w e w an t to reduce idle time for eac h mac hine in v olv ed in computation ≤ task migration | user can request that certain tasks execute on mac hines with particular data formats, arc hitectures, or ev en on an explicitly named mac hine ≤ fault tolerance Another asp ect of virtual mac hine dynamics relates to e±ciency . User applications can exhibit p oten tially c hanging computational needs o v er the course of their execution. F or example, con- sider a t ypical application whic h b egins and ends with primarily serial computations, but con tains sev eral phases of hea vy parallel computation. PVM pro vides ∞exible con trol o v er the amoun t of computational p o w er b eing utilized. Additional hosts can b e added just for those p ortions when w e need them. MPI lac ks suc h dynamics and is, in fact, sp eciØcally designed to b e static in nature to impro v e p erformance. Because all MPI tasks are alw a ys presen t, there is no need for an y time-consuming lo okups for group mem b ership. Eac h task already kno ws ab out ev ery other task, and all com- m unications can b e made without the explicit need for a sp ecial daemon. Because all p oten tial comm unication paths are kno wn at startup, messages can also, where p ossible, b e directly routed o v er custom task-to-task c hannels. 3.4.3 Virtual T op ology On the other hand, although MPI do es not ha v e a concept of a virtual mac hine, MPI do es pro vide a higher lev el of abstraction on top of the computing resources in terms of the message- passing top ology . In MPI a group of tasks can b e arranged in a sp eciØc logical in terconnection top ology [NS02, F or94] . A virtual top ology is a mec hanism for naming the pro cesses in a group in a w a y that Øts the comm unication pattern b etter. The main aim of this is to mak e subsequen t co de simpler. It ma y also pro vide hin ts to the run-time system whic h allo w it to optimize the comm unication or ev en hin t to the loader ho w to conØgure the pro cesses. F or example, if our pro cesses will comm unicate mainly with nearest neigh b ours after the fashion of a t w o-dimensional grid (see Figure 3), w e could create a virtual top ology to re∞ect this fact. What w e gain from this creation is access to con v enien t routines whic h, for example, compute the rank of an y pro cess giv en its co ordinates in the grid, taking prop er accoun t of b oundary conditions. In particular, there are routines to compute the ranks of our nearest neigh b ours. The rank can then b e used as an argumen t to message{passing op erations. 10
