Analysis of Dependencies of Checkpoint Cost and Checkpoint Interval of Fault Tolerant MPI Applications

Transcription

1 Mallkarjuna Shastry P M et al. / (IJCSE) Internatonal Journal on Computer Scence and Engneerng Analyss of Dependences of Checkpont Cost and Checkpont Interval of Fault Tolerant MPI Applcatons Mallkarjuna Shastry P.M.# 1 and K. Venkatesh # 2 #1 Sapthagr College of Engneerng, #2 M. S. Ramaah Insttute of Technology, # Afflated to Vshweshwaraya Technologcal Unversty, Bangalore, Karnataka, Inda., Abstract In ths paper, we have analysed ) the relatonshp between the checkpont cost and the optmal checkpont nterval and ) the relatonshp between the checkpont cost and the number of processors (processes) and we have also determned the optmal number of processors (processes) requred for executng the fault tolerant MPI applcatons. We have presented an expermental study n whch, we have used an optmal checkpont restart model [1] wth Webull s and Exponental dstrbutons to determne the optmal checkpont nterval. We have observed that, the optmal checkpont ntervals obtaned usng Webull s dstrbuton, produce mnmal average completon tme; as compared wth the optmal checkpont ntervals obtaned usng Exponental dstrbuton. The optmal checkpont nterval s approxmately drectly proportonal to the checkpont cost and nversely proportonal to shape parameter. The study ndcates that, the checkpont cost of MPI applcatons ncreases wth the number of processors (processes) used for executon. We have determned the optmal number of processes (processors) requred to execute the MPI applcatons consdered n ths paper, as 4. Keywords: OPEN MPI, Fault Tolerance, Optmal Checkpont Interval, Checkpont Cost, Speedup, Effcency. I. Introducton As the complexty of the program ncreases, the number of processors to be added to the cluster / HPC / Super Computer also ncreases, whch n turn decreases the MTBF (mean tme between falures) of the processors or the machnes [2]. When a processor fals or aborts before the completon of executon of the MPI applcaton, the MPI applcaton s restarted from the begnnng. Hence, t s requred to provde the fault tolerance to all the MPI applcatons whch run on multple processors or processes [1][3]-[7]. Fault tolerant MPI applcatons are checkponted perodcally and the checkponts are stored ether locally or sent to a remote server. When a fault tolerant MPI applcaton fals/aborts, t s restarted from the most recently saved checkpont on a local dsk. Hence, t s not requred to restart the applcaton from the begnnng [1][3]-[7]. The rollback recovery protocols such as uncoordnated, coordnated, communcaton nduced checkpontng and message loggng protocols [5] can be used to acheve the fault tolerance. We have used the coordnated checkpontng protocol [5][7] to provde the fault tolerance to MPI applcatons consdered n ths paper. MPI collectve communcatons are used to develop the MPI applcatons [7]. The optmal checkpont restart model (OCRM) developed by M. Shastry et al. [1] has been used n ths paper, to determne the optmal checkpont nterval, when the scale parameter and shape parameter of Webull s dstrbuton are known along wth checkpont cost. Fxed checkpont nterval s used to checkpont the MPI applcatons consdered n ths paper [8]. The checkpontng of MPI applcatons ncludes dfferent costs lke, checkpont cost, rollback cost, and restart cost [1][8][9]. These costs are collectvely called the checkpont overheads. The average completon tme of an MPI applcaton s obtaned by addng the checkpont overheads to the executon tme of MPI applcaton [1][8][9]. We would lke to determne ) the optmal number of processes (processors) requred to execute the MPI applcatons of dfferent sze, so that the average completon tme of these MPI applcatons wth checkpontng would be mnmal, ) the relatonshp between the checkpont cost and the optmal checkpont nterval, and ) the relatonshp between checkpont cost and number of processes used to execute the MPI applcatons consdered n ths paper. ISSN :

2 Mallkarjuna Shastry P M et al. / (IJCSE) Internatonal Journal on Computer Scence and Engneerng Rest of the paper s organzed as follows. In secton 2, the related works carred out on the study of relatonshp between checkpont cost and checkpont nterval, checkpont / restart models used to determne checkpont ntervals are dscussed. In secton 3, the dfferent parameters used and the assumptons made n ths paper are presented. In secton 4, determnaton of the optmal checkpont nterval usng Webull s and Exponental dstrbutons s dscussed brefly. In secton 5, the equatons used for determnng the average completon tme of the MPI applcatons are presented. In secton 6, the results of the case studes consdered n ths paper are presented. In secton 7, the expermental setup used n the analyss s dscussed and n secton 8, conclusons are presented. II. Related Works An excellent survey on rollback recovery protocols has been carred out by Elnozahy et al.[5]. Chandy[10][11] and Treaster[12] have presented a survey on rollback and recovery strateges used n fault tolerant MPI applcatons. M. Shastry et al. [8] have shown that the fxed checkpont nterval reduces the checkpont overheads of fault tolerant MPI applcatons as compared wth the ncremental or varyng checkpont nterval. Hence, we have used the fxed checkpont nterval n our experment to checkpont the MPI applcatons. Geff et al. [9] have used a smulator to determne the relatonshp between the checkpont nterval and the varous checkpont overheads. The OCRM dscussed by M.Shastry et al. [1] have shown the sgnfcant mprovement wth regard to the total wastage tme as compared wth the checkpont restart models developed by Yudun lu et al. [13][14] and Bouguerra Mohammed Slm et al. [15], The checkpont nterval obtaned by OCRM [1] usng Webull s and Exponental dstrbutons, produces mnmal checkpont overheads as compared wth Yudun lu et al. [13][14] and Bouguerra Mohammed Slm et al. [15]. Hoda El-Sayed et al. [16] and Xn Wang [17] have dscussed about the speedup and the effcency of MPI applcatons. III. Parameters/ Notatons Used and Assumptons Made. A. Parameters and Notatons Used. The parameters/notatons used n ths paper are presented n the table 1. Parameter /Notaton T C T S β α T N RB CC R TL F TL ET ACT T 1 T P Mat4k Mat5k Prme4L Prme5L Table 1. Parameters/Notatons used. Meanng Optmal Checkpont nterval. Tme requred to save the checkpont on a local dsk (Checkpont Cost). Shape Parameter of Webull s dstrbuton. Scale Parameter of Webull s dstrbuton. Executon tme tll a falure occurs n th cycle. Number of checkponts taken tll a falure occurs n th cycle. Rollback cost due to a falure n th cycle Checkpont cost n th cycle Restart cost(tme requred to resume the executon of the applcaton after a falure). Total tme lost n th cycle. Number of falures. Total tme lost due to F falures durng the executon of MPI applcaton. Executon Tme of MPI applcaton wthout checkpontng. Average Completon Tme of MPI Applcaton. Tme requred to execute the applcaton sequentally (usng a sngle processor) wthout checkpontng. Tme requred for parallel executon of the same applcaton usng p processors wthout checkpontng. MPI Applcaton, whch multples two matrces of sze 4000 * 4000 ntegers. MPI Applcaton, whch multples two matrces of sze 5000 * 5000 ntegers. MPI Applcaton, whch generates 4 lakh prme numbers on each process(processor). MPI Applcaton, whch generates 5 lakh prme numbers on each process (processor). ISSN :

3 Mallkarjuna Shastry P M et al. / (IJCSE) Internatonal Journal on Computer Scence and Engneerng B. Assumptons Made We have made the followng assumptons, whch are smlar to the assumptons made n [1] to checkpont the MPI applcatons consdered n ths paper. 1. A seres of random falures F ( = 1, 2, 3,..N) may nterrupt the executon of MPI applcaton. 2. A separate montorng software system s used to montor contnuously the falure of a fault tolerant MPI applcaton. 3. Checkpont nterval (T C ) s fxed and a checkpont s taken perodcally after the tme T C. 4. When a falure occurs durng the executon of MPI applcaton, MPI applcaton s rolled back to the most recent checkpont usng locally stored checkpont fle. 5. Tme requred for wrtng a checkpont (T S ) onto a local dsk s a constant and only one copy of the most recent checkpont s stored on a local dsk. 6. Tme requred for resumng/restartng (restart cost, R) the MPI applcaton from the most recent checkpont s a constant. IV. Determnaton of Optmal Checkpont Interval A. Usng Webull s Dstrbuton The optmal checkpont nterval T C s obtaned by usng the optmal checkpont restart model (OCRM) developed by [1] usng Webull s dstrbuton. The algorthm Estmate-T C ( ) of [1] s used wth the shape parameter β and the scale parameter α for the gven checkpont cost, T S. The shape parameter β s set to 0.5. B. Usng Exponental Dstrbuton. The optmal checkpont nterval T C s obtaned by usng the optmal checkpont restart model (OCRM) developed by [1] usng Exponental dstrbuton. The algorthm Estmate- T C ( ) of [1] s used wth the shape parameter β and the scale parameter α for the gven checkpont cost, T S. The shape parameter β s set to 1.0. V. Determnaton of Total Tme Lost The number of checkponts to be taken n a cycle before the occurrence of a falure can be determned by [1][8] N T ( T T ) (1 / C S ) Then, the cost of checkpont n th cycle s computed as follows [1][8]. CC N T S (2) The cost of rollback n th cycle s then computed as follows [1][8]. Rb ( T N ( T C T S )) (3) The tme lost n th cycle TL due to a falure can be obtaned by addng checkpont cost, rollback cost and restart cost together as follows[1][8]. TL CC Rb R (4) The tme lost n th cycle can also be computed [1] as follows by substtutng (2) and (3) n (4). TL T N T C R (5) The total tme lost durng the executon of a fault tolerant MPI applcaton s determned usng the followng equaton [1][8]. TL F 1 TL The average completon tme of a MPI applcaton s computed as follows ACT ET TL VI. Case Studes (6 ) (7 ) A. Varaton n Executon Tme (ET) wth Number of Processors (Processes). Varaton n executon tme of Mat4k and Mat5k wth number of processes s represented n fgure 1 a). Fgure 1a) shows that, the executon tme of Mat4k and Mat5k s very hgh, when number of processes s 1. When number of processes s ncreased to 2, the executon tme of both applcatons s reduced to almost 50%. When the number of processes s ncreased to 4 and 8, the executon tme of Mat5k ncreases lnearly, but, the executon tme of Mat4k remans almost constant. When the number of processes s ncreased to 16 and 32, the executon tme remans almost constant for both applcatons as shown n fgure 1a). Varaton n executon tme of Prme4L and Prme5L wth number of processes s represented n fgure 1 b). Fgure 1b) shows that, the executon tme of Prme4L and Prme5L s very hgh when number of processes s 1. When number of processes s ncreased to 2, 4 and 8 the executon tme of both applcatons s reduced by 26% to 40%. The executon tme of both applcatons remans almost constant, when the number of processes s ncreased to 16 and 32 as shown n fgure 1b). ISSN :

4 Mallkarjuna Shastry P M et al. / (IJCSE) Internatonal Journal on Computer Scence and Engneerng When P processors (processes) are used to execute the MPI applcatons, the speed up and the effcency [16][17] are computed as follows. Speed up = T 1 / T P. Effcency = Speed up / P. Varaton n speed up wth the number of processes used for executng the MPI applcatons consdered n ths paper, s represented n the fgure 1c). The fgure 1c) shows that, when the number of processes s between 1 and 4, the speed up s between 1 and 2 n all the 4 cases. Speedup remans same n case of Mat4k and Mat5k, when the number of processes s between 1 and 32. But the speedup ncreases gradually n case of Prme4L and Prme5L, when the number of processes s between 1 and 16 and remans constant, when the number of processes s between 16 and 32. It s clear from the fgure 1c) that, the speedup remans same n all the four cases, when the number of processes s 4. Varaton n effcency wth the number of processes used for executng the MPI applcatons consdered n ths paper, s represented n the fgure 1d). The fgure 1d) shows that, when the number of processes s between 1 and 4, effcency s between 0.5 and 1 n all the 4 cases. Effcency reduces gradually n all the 4 cases, as the number of processes s ncreased from 4 to 16 and reaches almost zero, when number of processes s 32. When the number of processors (processes) used for executng the MPI applcatons s P, speedup s above 2 and the effcency s between 0.5 and 1, the optmal number of processors (processes), P O, to be used for executng MPI applcatons s, P O = P. Snce the speedup s 2 and the effcency s above 0.5 as shown n fgures 1c) and 1d) respectvely, n all the 4 cases, we conclude that, the optmal number of processors (processes) to be used for executng all the 4 MPI applcatons consdered n ths paper, s, 4 (P O = 4). Fg 1 b). Varaton n Executon Tme of Prme4L and Prme5L. Fg 1c). Varaton n Speedup. Fg 1 a). Varaton n Executon Tme of Mat4k and Mat5k. Fg 1d). Varaton n Effcency. ISSN :

5 Mallkarjuna Shastry P M et al. / (IJCSE) Internatonal Journal on Computer Scence and Engneerng B. Varaton n Checkpont Cost wth Number of Processes (Processors). Fgures 2a) and 2b) present the varaton n checkpont cost of Mat4K, Mat5K and Prme4L, Prme5L MPI applcatons wth the number of processes respectvely. It s clear from the fgures 2a) and 2b) that, the checkpont cost of MPI applcatons ncreases lnearly as the number of processes ncreases from 1 to 32. Thus, the checkpont cost s drectly proportonal to the number of processes used for executng the MPI applcatons. Fgure 3 represents the optmal checkpont ntervals obtaned from OCRM [1] usng Webull s dstrbuton wth shape parameter, β = 0.5 and Exponental dstrbuton wth shape parameter, β = 1.0 for the dfferent values of checkpont cost, T S of MPI applcatons consdered n ths paper. The scale parameter α, s set to 4.8 mnutes (λ = 300 falures per day). In fgure 3, when β = 0.5, the T C remans 1.5 mnutes, when T S s less than 8 seconds and T C ncreases to 2 mnutes, when T S s between 9 and 16 seconds. The T C value ncreases gradually when T S s between 16 and 30 seconds. The value of T C decreases gradually further when T S s above 30 seconds. When β = 1.0, the optmal T C remans 0.5 mnutes, when T S s less than 5 seconds and T C ncreases to 1 mnute when T S s between 5 and 6 seconds. The T C value ncreases gradually when T S s between 10 and 42 seconds. The value of T C remans almost constant, when T S s above 42 seconds. Fg 2 a). Varaton n Checkpont Cost of Mat4k and Mat5k. Thus, t s clear from the fgure 3 that, the optmal checkpont ntervals obtaned from OCRM [1] usng Exponental dstrbuton wth β = 1.0 are low as compared to the optmal checkpont ntervals obtaned from OCRM [1] usng Webull s dstrbuton wth β = 0.5. Ths clearly ndcates that, the number of checkponts to be taken for an MPI applcaton s more n the case of Exponental dstrbuton (β = 1.0) as compared wth the Webull s dstrbuton(β = 0.5). It s also clear from the fgure 3 that, the optmal checkpont nterval s approxmately drectly proportonal to the checkpont cost, T S. of MPI applcaton and nversely proportonal to the shape parameter, β of Webull s dstrbuton. Fg 2 b). Varaton n Checkpont Cost of Prme4L and Prme5L. C. Varaton n Optmal Checkpont Interval wth Checkpont Cost. Fg 3. Varaton n Optmal Checkpont Interval ISSN :

6 Mallkarjuna Shastry P M et al. / (IJCSE) Internatonal Journal on Computer Scence and Engneerng D. Varaton n Average Completon tme wth Number of Processors (Processes). Fgures 4 and 5 represent the varaton n the average completon tme of Mat4k and Mat5k wth number of processes respectvely. Fgures 6 and 7 represent the varaton n average completon tme of Prme4L and Prme5L wth number of processes, respectvely. The average completon tme of all 4 MPI applcatons s computed usng the equaton (7). The average completon tme of Mat4k, as shown n the fgure 4, s almost same for β = 0.5 and β = 1.0, when the number of processes s between 1 and 16. Smlarly, the average completon tme of Mat5k, as shown n fgure 5, s almost same for β = 0.5 and β = 1.0, when the number of processes s between 1 and 16 Snce, the optmal checkpont ntervals obtaned from OCRM [1] usng exponental dstrbuton wth β = 1.0 are low as compared wth the optmal checkpont ntervals obtaned from OCRM [1] usng Webull s dstrbuton wth β = 0.5 as shown n fgure 3, the average completon tme of Mat4k s 9% less and Mat5k s 7% less, when β = 0.5, as shown n the fgures 4 and 5 respectvely, when the number of processes s above 16. Ths result s as expected from OCRM[1], because, the number of checkponts to be taken wll be more n the case of Exponental dstrbuton wth β = 1.0 as compared to the Webull s dstrbuton wth β = 0.5. Fg 4. Average Completon Tme of Mat4k The average completon tme of the Prme4L, as shown n fgure 6, s almost same for both β = 0.5 and β = 1.0, when the number of processes s between 1 and 32. Smlarly, the average completon tme of the Prme5L, as shown n fgure 7, s almost same for both β = 0.5 and β = 1.0, when the number of processes s between 1 and 32. But, stll the average completon tme of these applcatons s 3 % less, when β = 0.5, as shown n fgures 6 and 7. Thus, t s clear from the fgures 4, 5, 6 and 7 that, the optmal checkpont ntervals obtaned from OCRM [1] usng Webull s dstrbuton wth β = 0.5, yeld mnmal average completon tme as compared to the optmal checkpont ntervals obtaned from OCRM [1] usng Exponental dstrbuton wth β = 1.0, rrespectve of the number of processes used to execute the MPI applcatons consdered n ths paper. Fg 5. Average Completon Tme of Mat5k ISSN :

7 Mallkarjuna Shastry P M et al. / (IJCSE) Internatonal Journal on Computer Scence and Engneerng applcatons. BLCR s a LINUX based coordnated checkpontng protocol [19]. We have used fxed checkpont nterval to checkpont the MPI applcatons [8], as t reduces the total waste tme due to the checkpontng of MPI applcatons. The above mentoned MPI applcatons are executed and checkponted perodcally after the tme T C. We have generated λ = 300 falures per day (α = 4.8 mnutes) durng the executon of above MPI applcatons. Webull s dstrbuton wth β = 0.5 and Exponental dstrbuton wth β = 1.0 are used to determne the optmal checkpont ntervals usng OCRM [1], when α = 4.8 mnutes. Fg 6. Average Completon Tme of Prme4L The montor program s developed n a shell scrpt. Ths program runs at the background and montors contnuously the executon of the MPI applcatons. Once, the montor program learns that the MPI applcaton has faled due to an nterrupton; t uses the most recent checkpont stored locally and resumes the executon of the MPI applcaton from the most recent checkpont. VIII. Conclusons. Executon tme of the MPI applcatons as shown n fgures 1a) and 1b), reduces to more than 30% to 50% when the number of processes s ncreased from 1 to 8 and the executon tme almost remans constant when the number of processes s ncreased from 8 to 32. Snce, when the number of processes s 4, for all the 4 applcatons, the speedup s 2 and the effcency s above 0.5 as shown n fgures 1c) and 1d) respectvely, we conclude that, the optmal number of processes to be used for executng all the 4 MPI applcatons consdered n ths paper, s 4. Fg 7. Average Completon Tme of Prme5L VII. Expermental Setup An AMD Athlon II X (Quad Core) processor wth 4 GB of RAM s used to execute the MPI applcatons. We have mplemented 4 MPI applcatons, Mat4k, Mat5k, Prme4L and Prme5L n C programmng by usng OPEN MPI [18] on a standalone system. OPEN MPI [18] allows the executon and checkpontng of MPI applcatons on standalone systems also. MPI collectve communcaton methods lke MPI_Scatter() and MPI_Bcast() are used to dstrbute the data to dfferent processes nvolved n the cluster[7]. Berkeley Lab s Checkpont restart (BLCR) technque along wth OPEN MPI s used to checkpont the above MPI Fgures 2a) and 2b) show that, the checkpont cost of MPI applcatons ncreases gradually as the number of processes ncreases from 1 to 32. Thus, the checkpont cost s drectly proportonal to the number of processes used to execute an MPI applcaton. Fgure 3 shows that, the optmal checkpont ntervals obtaned usng Exponental dstrbuton wth β = 1.0 are low as compared to the optmal checkpont ntervals obtaned usng Webull s dstrbuton wth β = 0.5. Ths clearly ndcates that, the number of checkponts to be taken for an MPI applcaton s more n the case of Exponental dstrbuton (β = 1.0) as compared wth the Webull s dstrbuton (β = 0.5). It s also clear from the fgure 3 that, the optmal checkpont nterval s approxmately drectly proportonal to the checkpont cost, T S. of MPI applcaton and nversely proportonal the shape parameter, β of Webull s dstrbuton. ISSN :

8 Mallkarjuna Shastry P M et al. / (IJCSE) Internatonal Journal on Computer Scence and Engneerng It s qute clear from the fgures 4, 5, 6 and 7 that, the optmal checkpont ntervals obtaned from OCRM [1] usng Webull s dstrbuton wth β = 0.5, yeld mnmal average completon tme as compared wth the optmal checkpont ntervals obtaned from [1] usng Exponental dstrbuton wth β = 1.0. Ths s because; the number of checkponts to be taken s more n case of Exponental dstrbuton as compared wth Webull s dstrbuton. Our results dscussed n ths paper agan emphass that, when the scale parameter α and the checkpont cost, T S of MPI applcatons are known, the Webull s dstrbuton can be used to determne optmal checkpont ntervals from OCRM [1] for any gven value of shape parameter, β. REFERENCES [1] Mallkarjuna Shastry P.M and K.Venkatesh, An Optmal Checkpont Restart Model usng Webull s and Exponental dstrbutons for Fault Tolerant Large Scale MPI applcatons, Beng processed by JPDC, Elsever, Un Publshed. [2] A.R. Adga, G.almas, and et al., An Overvew of the BlueGene/L Supercomputer, In Proceedngs of Supercomputng, IEEE/ACM Conference, pp60-60,2002. [3] Lus Moura Slva and Joao Gabrel Slva, The Performance of Coordnated and Independent Checkpontng, IEEE Trans, [4] G.E. Fagg, A. Bukovsky and J.J. Dongarra, Harness and Fault Tolerant MPI, Parallel Computng, 27(11): , [5] E. N. Elnozahy, Lorenzo Alvs, Y-Mn Wang, and B. Davd Johnson, A Survey of Rollback-Recovery Protocols n Message- Passng Systems, ACM Computng Surveys, Vol. 34, No. 3, pp , Sep [6] M.Treaster, A survey of fault-tolerance and fault-recovery technques n parallel systems, Techncal Report cs.dc / , ACM computng Research Repostory (CoRR), January [7] Mallkarjuna Shastry P.M, K.Venkatesh, Performance Evaluaton of Coordnated Checkpontng Protocol usng MPI pont to pont and collectve communcatons, IJACC, Accepted, n Press. [8] Mallkarjuna Shastry P.M. and K.Venkatesh, Selecton of a Checkpont Interval n Coordnated Checkpontng Protocol for Fault Tolerant Open MPI, IJCSE, Vol. 02, No. 06, pp , Sep [9] Geff Vallee, Anand Tkotekar, Stephen L Scott, Impact of Fault Tolerant Polces: Feasblty Study, Oak Natonal Laboratory, Apr [10] K.M. Chandy, A survey of analytc models of roll-back and recovery strateges, Computer 8, 5 (May 1975), [11] K.M. Chandy, J.C. Browne, C. W. Dssly, and W. R. Uhrg, Analytc models for rollback and recovery stratagems n data base systems, IEEE Trans Software Engg. SE-1, ( March 1975), [12] M.Treaster, A survey of fault-tolerance and fault-recovery technques n parallel systems, Techncal Report cs.dc / , ACM computng Research Repostory (CoRR), January [13] Y. Lu, Relablty Aware Optmal Checkpont/ Restart Model n Hgh Performance Computng, PhD Thess, Lousana Tech unversty, Ruston, LA, USA, May [14] Yudun Lu, Raja Nassar, Chokcha (box) Leangsuksum, Nchamon Naksnehaboon, Mhaels Paun, Stephen L. Scott, An Optmal Checkpont /Restart Model for a Large Scale Hgh Performance Computng System, IEEE Trans [15] Bouguerra Mohammed Slm, Therry Gauter, Dens Trystram, Jean,Marc Vncent, A New flexble Checkpont/Restart Model, INRIA, Dec [16] Hoda El-Sayed and Erc Wrght, Sparse Matrx Multplcaton Usng UPC, Proceedngs of the World Congress on Engneerng 2007, Vol. I, WCE 2007, July 2-4, 2007, London, U.K. [17] Xn Wang, Scalable Parallel Matrx MultplcatonAlgorthms wth Applcaton to a Maxmum Entropy Problem, Sep [18] The Open MPI Team, Open MPI Checkpont/Restart Fault Tolerance User s Gude, OPEN MPI Team, Oct [19] H. Paul Hargrove and C. Jason Duell, Berkeley lab checkpont / restart (BLCR) for Lnux clusters, Journal of Physcs, Conference seres 46 (2006), , ScDAC AUTHORS PROFILE MR. Mallkarjuna Shastry P.M has receved hs B.E. and M.Tech n Computer Scence and Engneerng from Karnataka Unversty Dharwar, and Vshweshwaraah Technologcal Unversty, Belgaum, Karnataka, Inda respectvely. He s currently pursung Ph.D on Analyss of Fault Tolerant Methods and Performance Evaluaton n Dstrbuted Systems under the gudance of Prof. Dr. K.Venkatesh at M.S.Ramaah Insttute of Technology, Bangalore-54, Karnataka, Inda. He s a Professor n the department of Computer Scence and Engg. at Sapthagr College of Engg, Bangalore, Karnataka, Inda. He has totally 18 years of teachng experence n Computer Scence and Engg. Prof. Dr. K. Venkatesh has receved hs M.Sc. n Physcs from Mysore Unversty n 1973 and MS from BITS Plan n 2001 respectvely. He has receved Ph.D n 1980 from Mysore Unversty. He s a professor at M.S.Ramaah Insttute of Technology, Bangalore-54 and has totally 30 years of teachng experence. ISSN :