Számítogépes modellezés labor (MSc)

Transcription

1 Számítogépes modellezés labor (MSc) Running Simulations on Supercomputers Gábor Rácz Physics of Complex Systems Department Eötvös Loránd University, Budapest September 19, 2018, Budapest, Hungary

2 Outline Parallel computing 1 Parallel computing 2 3 4

3 Parallel computing Parallel computing is a type of computation in which many calculations or the execution of processes are carried out simultaneously. Large problems can often be divided into smaller ones, which can then be solved at the same time.

4 Fixed problem size Amdahl s law where: S latency is the potential speedup S latency = (1 p + p/s) 1 p is the proportion of the problem that can be made parallel s is the number of computing units (e.g. CPU cores, CUDA cores, etc.) used in the calculation "You can spend a lifetime getting 95% of your code to be parallel, and never achieve better than 20x speedup no matter how many processors you throw at it!"

5 Hardware: Parallel Computer Memory Architectures Shared Memory Every processor have access to the same memory e.g., Personal computers, (small) servers, laptops, smartphones

6 Hardware: Parallel Computer Memory Architectures Distributed Memory Every processor have its own local memory, and they can communicate each other usually through network e.g., Old supercomputers (NEC SX-1)

7 Hardware: Parallel Computer Memory Architectures Hybrid solutions e.g., Modern supercomputers (Atlasz, MARCC, IBM Blue

8 Software: Parallel Programming Models There are several parallel programming model exist. The three most important is: Threads Model (shared memory) [] easy to use, Message Passing Model (distributed memory) [MPI] Hybrid (shared and distributed) Hard parts: Syncronisation, race condition

9 Open Multi-Processing () Shared memory threads model implementation, industry standard. Can be very easy to use Multi platform: Unix, Linux, Windows part of most Linux/Unix systems C/C++ and Fortran

10 For loop example

11 (MPI) Specification for the developers and users of message passing libraries. (the only) indrusty standard. Originally, MPI was designed for distributed memory architectures. Today, MPI runs on virtually any hardware platform: shared, distributed, hybrid. There are over 430 routines defined in MPI-3, which includes the majority of those in MPI-2 and MPI-1 C/C++, Fortran (and Python) Major implementations: I MVAPICH Intel-MPI

12 C example Parallel computing 1 # i n c l u d e <mpi. h> 2 # i n c l u d e < s t d i o. h> 3 4 i n t main ( i n t argc, char argv ) { 5 / / I n i t i a l i z e the MPI environment 6 M P I _Init (NULL, NULL) ; 7 8 / / Get the number of processes 9 i n t world_size ; 10 MPI_Comm_size (MPI_COMM_WORLD, &world_size ) ; / / Get the rank of the process 13 i n t world_rank ; 14 MPI_Comm_rank (MPI_COMM_WORLD, &world_rank ) ; / / Get the name of the processor 17 char processor_name [MPI_MAX_PROCESSOR_NAME ] ; 18 i n t name_len ; 19 MPI_Get_processor_name ( processor_name, &name_len ) ; / / Print o f f a hello world message 22 p r i n t f ( " Hello world from processor %s, rank %d out of %d processors \ n ", 23 processor_name, world_rank, world_size ) ; / / F i n a l i z e the MPI environment. 26 MPI_Finalize ( ) ; 27 }

13 MPI_COMM_WORLD and communicators (groups)

14 Important MPI Routines MPI_Init Initializes the MPI execution environment. This function must be called in every MPI program. MPI_Send Basic blocking send operation. Routine returns only after the application buffer in the sending task is free for reuse. MPI_Recv Receive a message and block until the requested data is available in the application buffer in the receiving task. MPI_Barrier Synchronization operation. Creates a barrier synchronization in a group.

15 Important MPI Routines MPI_Bcast Data movement operation. Broadcasts (sends) a message from the process with rank "root" to all other processes in the group. MPI_Scatter Data movement operation. Distributes distinct messages from a single source task to each task in the group. MPI_Gather Data movement operation. Gathers distinct messages from each task in the group to a single destination task. This routine is the reverse operation of MPI_Scatter.

16 Parallel computing Supercomputers G. Rácz Parallel computing

17 Parallel computing Supercomputers G. Rácz Parallel computing

18 Supercomputers OS: Gnu/Linux (earlier: UNIX) Usually no or very limited GUI = using command line is unavoidable Job scheduler (SLURM, OpenPBS)

19 SLURM - Simple Linux Utility for Resource Management Submitting jobs: $ sbatch m y j o b s c r i p t. sh myjobscript.sh: #!/bin/bash -l #SBATCH #SBATCH --job-name=lcdm_1860mpc #SBATCH --time=55:00:00 #SBATCH --nodes=4 #SBATCH -p hpc2009 #SBATCH --ntasks-per-node=8 #SBATCH --cpus-per-task=1 #SBATCH --exclusive mpirun./gadget2 /home/<username>/gadget/paramfiles/lcdm_1860mpc.param

20 View information about jobs located in the Slurm scheduling queue: squeue

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23 List all current jobs for a user: $ squeue u <username> To kill a job: $ scancel < j o b i d > To pause a particular job: $ s c o n t r o l hold < j o b i d > To resume a particular job: $ s c o n t r o l resume < j o b i d >