UNIVERSITY OF MORATUWA

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1 UNIVERSITY OF MORATUWA FACULTY OF ENGINEERING DEPARTMENT OF COMPUTER SCIENCE & ENGINEERING B.Sc. Egieerig 010 Itake Semester 8 Examiatio CS453 CONCURRENT PROGRAMMING Time allowed: Hours March 015 ADDITIONAL MATERIAL: Noe INSTRUCTIONS TO CANDIDATES: 1. This paper cosists o 5 questios i 7 pages.. Aswer ay 4 questios. 3. Start aswerig each o the mai questios o a ew page. 4. The maximum attaiable mark or each questio is give i brackets. 5. This examiatio accouts or 60% o the module assessmet. 6. This is a closed book examiatio. NB: It is a oece to be i possessio o uauthorised material durig the examiatio. 7. Oly calculators approved by the Faculty o Egieerig are permitted. 8. Assume reasoable values or ay data ot give i or with the examiatio paper. Clearly state such assumptios made o the script. 9. I case o ay doubt as to the iterpretatio o the wordig o a questio, make suitable assumptios ad clearly state them o the script. 10. This paper should be aswered oly i Eglish.

2 Questio 1 (5 marks) Suppose a weather orecastig program typically takes 18 hours to produce tomorrow s weather orecast. Thereore, the Meteorology departmet is orced to ru it suiciet time ahead ad oly oce a day. As more severe weather evets are reported recetly, meteorology departmet is iterested i producig weather orecasts more requetly ad with less turaroud time. They are thikig o achievig this by beeitig rom more advaced hardware. Beore decidig to upgrade their computig acility, the meteorology departmet wats your eedback o the ollowig cocers they have. a) Should we go or a -core processor while parallelizig our weather orecastig program or should we id a aster processor without modiyig the program? Suggest your recommedatio while cosiderig the advatages ad disadvatages o each approach. State ay assumptios. [3] b) We wat to provide a ew weather orecast every 6 hours. I case we decide to go or a -core desig with a modiied program, how may cores are eeded? It was also oted that a ractio o the weather orecastig program caot be parallelized. accouts or 5% o the program s executio time. The remaiig code p is parallelized. Hit: Amdahl s law i the cotext o cocurret programmig ca be give as: 1 p 1 p c) Should we upgrade our server to -cores or should we replace the server with a cluster o odes havig a total o -cores? Suggest your recommedatio while cosiderig the advatages ad disadvatages o each approach. State ay assumptios. [4] [3] Cosider the ollowig program with 3 threads. Lock l1, l, l3; Thread 1 while(1) l1.lock() l.lock() prit Red l3.ulock() l1.ulock() Thread while(1) l.lock() l3.lock() prit Gree l1.ulock() l.ulock() Thread 3 while(1) l3.lock() l1.lock() prit Blue l.ulock() l3.ulock() a) Provide 3 possible outcomes o the above program. [3] b) Will this code lead to a deadlock? Explai usig a Deadlock Modellig graph. [4] c) Rewrite the above program usig a semaphore(s) such that we get the sequece Red, Gree, Blue, Red, Gree, Blue,. [8] Page o 7

3 Questio (5 marks) Compare ad cotrast (i.e., idetiy the similarities ad dissimilarities o) semaphores, moitors, ad coditioal variables. [6] Cosider the ollowig program with 3 threads. Thread 1 while(1){ prit Red + math.rad(5); Thread while(1){ prit Gree + math.rad(5); Thread 3 while(1){ prit Blue + math.rad(5); Math.rad(5) geerates a radom value betwee 1 ad 5. Chage the above program to make sure the sum o all the Red values (SumRed) it had prited so ar is always greater tha the sum o all the Gree values (SumGree) it has prited. Similarly, SumGree must be always greater tha the sum o all Blue values (SumBlue). SumRed > SumGree > SumBlue For example, i Red had prited 3 ad 5 the it is OK or Gree to prit 4 ad 3. Similarly, Blue may prit, 1, ad because > > [16] (iii) Ca you implemet the solutio or part usig a coditioal variable(s)? Briely discuss your aswer. [3] Page 3 o 7

4 Questio 3 (5 marks) What are the advatages ad disadvatages o usig GPUs or solvig embarrassigly parallel programs? [4] -body iteractio is oe o the most commo simulatios ru usig GPUs. It ca be used to simulate movemet o objects such as plaets durig the Big Bag. For example, give the masses ad locatios o plaets ollowig equatio ca be used to calculate the orces that a plaet i experiece due to aother plaet j. Gm m i, j d i j i, j Where G is the gravitatioal costat, mi ad mj are masses o the plaets, ad di,j is the distace betwee the plaets. These pairwise orces ca be represeted as a matrix F. 0 1,0 F,0 1,0 0,1 0 1,1,1 0, 1 1, 1 0, 1 0, 1, 1, 0 Where is the umber o plaets. Oce the orce matrix is calculated, it ca be used to calculate the acceleratio o each plaet usig Newto s secod law (i.e., F = ma). Which iter ca be used to calculate the velocities ad ew locatios o plaets ater a give time t. The this process ca be repeated agai ad agai to calculate the locatio o plaets at time t, t, 3t, ad so o. a) Outlie a CUDA kerel to calculate the orce matrix F. Your solutio should also iclude the code required to ivocate the Kerel uctio. Assume = 1,000,000. Hit: a typical CUDA supported GPU ca oly hadle 1,04 threads per block. [15] b) Durig the -body simulatio, oe roud o computatio o matrix F eeds to iish beore iitiatig aother roud o computatios (as the locatio o plaets will chage with time). How ca we esure that dieret rouds o calculatig F will ot overlap with each other? [3] c) Is it worthwhile to calculate both i,j ad j,i i a GPU? Briely discuss. [3] Page 4 o 7

5 Questio 4 (5 marks) Cosider the ollowig 3 sigle-lae, oe-way roads joiig just beore a bridge which is oe way but has laes. To reduce cogestio o the bridge, vehicles rom oly access roads are allowed at a time. Road 1 Bridge Road Road 3 a) I this problem, what is the shared resource ad who are the accesses tryig to access that resource? [] b) Propose a solutio to this problem usig a semaphore(s) while assumig this is a shared memory problem. Pseudo code is suiciet. c) How would you solve the same problem, i it is iterpreted as a distributed mutual exclusio problem, where you do ot have access to a shared memory? The ollowig table shows the curret allocatio o resources or 3 processes (P, Q, ad R) ad their maximum resource requiremets. Is the curret state is sae or usae? Show the steps. [4] Has Max P 9 Q 1 4 R 5 Free: 3 (iii) Static or dyamic load balacig is essetial i most systems to icrease the resource utilizatio. What type o load balacig would you recommed or the ollowig problems? a) Idexig web pages oud by web crawlers. [3] b) Calculatig the area uder a give curve (i.e., itegratio) by breakig it ito a large set o trapezoids. [3] [8] [5] Page 5 o 7

6 Questio 5 (5 marks) Recommed a suitable solutio patter to parallelize the ollowig code sippets. Provide a suitable justiicatio or each case. State ay assumptios. a) or(k = 1, k < 500; k++){ x[k] = y[k 1] + 1; [3] b) or(k = 1, k < 500; k++){ x[k] = y[k 1] + 1; y[k] = z[k 1] + ; [3] c) x = readdata( xi.txt ); y = readdata( yi.txt ); or(k = 1, k < x.getsize()(; k++){ x[k] = x[k] + y[k - 1]; writedata( xout.txt, x); [4] The Cotraharmoic mea is oe o the several kids o averages. It is ote used i image processig ad Bioiormatics. Cotraharmoic mea o real umbers x1, x, x3,... x ca be calculated as ollows: C x, x, x, x 1 3, x1 x x x 1 x x 3 3 x x Outlie a MPI program (usig pseudo code) that ca be used to calculate the Cotraharmoic mea o oe millio real umbers. Oce the calculatio is complete, mea should be stored o a variable at process 0. Use relevat MPI uctios that are give i the Appedix. Note that it is impractical to create oe millio cocurret processes/threads. [15] Page 6 o 7

7 Appedix MPI Fuctios it MPI_Iit(it *argc, char **argv) it MPI_Comm_size(MPI_Comm comm, it *size) it MPI_Comm_rak(MPI_Comm comm, it *rak) it MPI_Fialize() it MPI_Sed (void *bu,it cout, MPI_Datatype datatype, it dest, it tag, MPI_Comm comm) it MPI_Recv (void *bu,it cout, MPI_Datatype datatype, it source, it tag, MPI_Comm comm, MPI_Status *status) it MPI_Reduce(void *sedbu, void *recvbu, it cout, MPI_Datatype datatype, MPI_Op op, it root, MPI_Comm comm) it MPI_Allgather(void *sedbu, it sedcout, MPI_Datatype sedtype, void *recvbu, it recvcout, MPI_Datatype recvtype, MPI_Comm comm) it MPI_Allreduce (void *sedbu, void *recvbu, it cout, MPI_Datatype datatype, MPI_Op op, MPI_Comm comm) it MPI_Bcast( void *buer, it cout, MPI_Datatype datatype, it root, MPI_Comm comm) it MPI_Gather(void *sedbu, it sedct, MPI_Datatype sedtype, void *recvbu, it recvct, MPI_Datatype recvtype, it root, MPI_Comm comm) it MPI_Scatter(void *sedbu, it sedct, MPI_Datatype sedtype, void *recvbu, it recvct, MPI_Datatype recvtype, it root, MPI_Comm comm) END OF THE PAPER Page 7 o 7