Advanced Scalable Decomposition Method with MPICH Environment for HPC

IJSRD - International Journal for Scientific Research & Development| Vol. 3, Issue 10, 2015 | ISSN (online): 2321-0613

Advanced Scalable Decomposition Method with MPICH Environment for HPC Hiral Patel1 Ishan Rajani2 Department of Computer Engineering 1,2 Darshan Institute of Engg. & Tech.

1,2

Abstract— MPI (Message Passing Interface) has been effectively used in the high performance computing community for years and is the main programming model.

The MPI is being widely used to developed parallel programs on computing system such as clusters. The major component of high performance computing (HPC) environment MPI is becoming increasingly prevalent. MPI implementations typically equate an MPI process with an OS-process, resulting in decomposition technique where MPI processes are bound to the physical cores. It integrated approach makes it possible to add more concurrency than available parallelism, while minimizing the overheads related to context switches, scheduling and synchronization. Fiber is used by it to support multiple MPI processes inside an operating system process. There are three widely used MPI libraries, including OPENMPI, MPICH2 and MVAPICH2.

This paper works on the decomposition techniques and also integrates the MPI environment with using MPICH2.

Key word: HPC, MPI, MPICH

incomes of the machine, in actual the number of cores and OS processes that can be created. Programs written using MPI tend to be coarse-grain and cannot easily exploit more finegrain parallelism without resorting to threads or combining MPI with other APIs like OpenMPI[2]. In this paper, we estimate the data decomposition technique to improve the efficiency of MPI-implementation.

II. MPICH VERSIONS There are various versions available of MPICH2. The latest versions of MPICH2 are: A. MPICH 3.2rc2 Released     

Released on 4th November,2015 Displaying source after attaching to an MPI job Hydra: Automatically handle bad ssh setup Build fails with missing routines Cancel sand not implemented in the MXM netmod

B. MPICH 3.2b3 Released I. INTRODUCTION MPI is originally intended for distributed memory schemes.

Unlike OpenMPI, pthread or other parallelization explanations for shared memory, it does not certification shared data. Instead, MPI programs broadcast data by message passing. Because of the memory partition, when there are thousands of centers on one computer, message passing will demonstration its advantage on scalability. It will be more accomplished than shared statistics accessing. Thus, it is significant for MPI implementations to increase the rapidity of data communication. There are many repeatedly used exposed MPI implementations, such as MPICH2 and OpenMPI. To fully exploit multicore architectures, these requests may use certain novel technologies. MPI has been very successful in High Presentation Calculating for applying message-passing programs on calculate clusters. There are many requests and a variety of records that have been inscribed using MPI. Many of these programs are inscribed as SPMD programs where the program is parameterized by ―N‖ the amount of MPI processes. Parameter N regulates the granularity of the package and provides the measure of available concurrency. In executing MPI programs, one typically matches the number of MPI courses to the quantity of cores, the measure of available parallelism. MPI (Message Passing Interface) is the leading model used for similar programming in great performance computing [1]. MPI is fruitful because of the work over the last 15 years on the MPI normal and middleware that assistance that MPI programs continue to achieve well on parallel and cluster constructions across a wide variety of net fabrics. Almost all MPI applications bind the implementation of an MPI process to an operating system (OS) process where usually a ―one process‖ per ―processor core‖ planning is used. As a result, the notion of an MPI process is tightly bound to the physical

  

   

Released on 4th june ,2015 Support MPI-3.1 standard Support full fortran 2008 Support for the Mellanox MXM interface for Infiniband Support for the Mellanox HCOLL interface for the collective communication Significant improvement to the MPICH portals for implementation Completely revamped RMA infrastructure providing scalability and performance improvement

C. MPICH 3.2b2 Released     

Released on 14th April ,2015 Support MPI-3.1 standard Support full fortran 2008 Support for the Mellanox MXM interface for Infiniband Support for the Mellanox HCOLL interface for the collective communication III. MPICH

MPICH is a high-performance and usually moveable functioning of the Message Passing border (MPI) standard (MPI-1, MPI-2 and MPI-3). The goals of MPICH are: 

To supply an MPI functioning that powerfully supports different calculation and message platform including high speed network like InfniBand , Myrinet.  It is easy to modular framework for other implementation.

MPICH2 implement extra features of the MPI-2 standard over what was implemented in the unique MPICH (now referred to as MPICH-1). Here we describe how to and install the latest version of MPICH. Steps to install MPICH3.2rc2

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Advanced Scalable Decomposition Method with MPICH Environment for HPC (IJSRD/Vol. 3/Issue 10/2015/100)

1) Unzip the tar file Tar xfz mpich3.2rc2.tar.gz 2) Decide an installation directory mkdir /home/student/mpich-install 3) Decide a build directory mkdir /home/student/mpich-3.2rc2sub 4) Decide any configure options prefix set the setting up directories for MPICH. 5) Configure MPICH, specify the installation directory cd /home/student/mpich-3.2rc2sb /home/student/mpich3.2rc2/configure\ -prefix=/home/student/mpich-install |& tee c.txt 6) Build MPICH3.2rc2 make |& tee m.txt (for csh and tcsh) OR make 2>&1 | tee m.txt (for bash and sh) 7) nstall the MPICH commands make install |& tee mi.txt 8) Add the bin subdirectory of the installation directory to your path Setenv PATH /home/student/mpich-install/bin:$PATH For csh and tcsh, or Export PATH=/home/student/mpichinstall/bin:$PATH For bash, and PATH=/home/student/mpich-install/bin:$PATH export PATH for sh which mpicc which mpiexec 9) Run the job with mpiecc mpiexec -n 5 ./cpi The cpi example will tell you which hosts it are running on. C. IV. MPICH ARCHITECTURE A. Romio     

It is a great-performance, portable implementation of MPI-IO that works with any MPI application on numerous file system.

It is included as part of MPICH2, MPICH1, seller MPI implementations for PVFS, SGI, XFS, PanFS and UFS folder system.

ROMIO achieves sophisticated optimization that enables applications to accomplish high I/O performance.

Shared I/O, data sifting and I/O aggregation are integrated by ROMIO. ROMIO also admits a number of hints from the user for refining I/O performance, such as file striping and algorithm change parameters.

E. Infniband  

It is a mainframe network communication connection used in high performance computing fecturing very high thoughput & low-latency.

It is used for data interconnect both concerning and within computer.

F. Myrinet 

It is rate effective, high performance, package communication &converting technology that is widely used to interconnect cluster & workstation.

V. HOW TO BUILD DISTRIBUTED

A. Environment with Using MPICH in Windows 1) Hardware Before starting, you should have the following hardware and software:  Take 2 computers with same operating system.  Must be installed correct driver.

2) Software Mpi is a de-facto standard.

1) Step 1: you can download the latest version of mpich2 then unpack mpich2 with the admin permission.

2) Step 2: All files of the lib folder in MPICH2 copy to the other folder.

3) Step 3: Install the Cluster Manager facility on each swarm you want to use for distant completing of MPI processes.

4) Step 4: Follow step 1 and 2 for each host in the cluster 5) Step 5: Now start mpiexec (from folder C:\MPICH2\bin) by double-clicking it.

B. Nemesis  

Nemesis is a scalable, high routine, shared commemoration, multi-network message subsystem within MPICH2.

Nemesis offers low-latency, high bandwidth message, particularly for intra-node communication.

C. Lustre 

It is a parallel dispersed file system, normally used for great scale cluster encoding.

D. GPFS 

It is a high performance clustered label system developed by IBM.

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Advanced Scalable Decomposition Method with MPICH Environment for HPC (IJSRD/Vol. 3/Issue 10/2015/100)

VI. FLOW OF FLOYD ALGORITHM

VII. RESULTS

Fig. 3: VIII. CONCLUSION MPI has wide applicability and many years of effort have gone into ensuring its performance portability. Our work is an attempt to extend the use of the MPI model with decomposition technique. Domain decomposition technique extend the MPICH2 completion of MPI to build it probable to have manifold MPI processes within an OS-process. By decomposition technique improved the MPI environment using MPICH2.

Fig. 1: Flow of Floyd Algorithm

Fig. 2:

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Advanced Scalable Decomposition Method with MPICH Environment for HPC (IJSRD/Vol. 3/Issue 10/2015/100)

standard,‖ Available from http://www.mcs.anl.gov/research/projects/mpich2/index. php.

[9] D. Buntinas, W. Gropp, and G. Mercier, ―Design and evaluation of Nemesis, a scalable, low-latency, messagepassing communication subsystem,‖ in CCGRID ’06: Proceedings of the Sixth IEEEInternational Symposium on Cluster Computing and the Grid.Washington, DC, USA: IEEE Computer Society, 2006, pp. 521– 530.[Online].

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