Introduction to HPC and Optimization Tutorial VI

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1 Felix Eckhofer Institut für numerische Mathematik und Optimierung Introduction to HPC and Optimization Tutorial VI January 8, 2013 TU Bergakademie Freiberg

2 Going parallel HPC cluster in Freiberg 144 nodes, 12 cores per node (plus some more for management) 1728 cores Intel Xeon X5670 (Westmere 2.93GHz 24GB RAM per node We can use 10 nodes exclusively for this course SSH to login node login01.hrz.tu-freiberg.de Copy files from klio using the scp command [klio]$ scp -r myfiles login01.hrz.tu-freiberg.de: [login01]$ scp -r klio.mathe.tu-freiberg.de:myfiles. F. Eckhofer (TUBAF) HPC Tutorial VI 1

3 Going parallel II The module system You must explicitly request software (versions) you want to use List available modules using module avail Load a module using module load module load pbspro module load gcc module load intel/compiler/64/12.0/191 F. Eckhofer (TUBAF) HPC Tutorial VI 2

4 Going parallel III The queue system Define a job (required resources, software to run) Let the queue system decide, where to run it Software used in Freiberg is called PBSPro Our queue is called hpcopt PBSPro s qsub qsub -q hpcopt -l select=1:ncpus=2:mem=6gb myprogram On overview over the most important options can be found at Full list and details can be found in man qsub F. Eckhofer (TUBAF) HPC Tutorial VI 3

5 Going parallel IV Requesting an interactive shell You can request a shell on one of our nodes using the -I switch qsub -q hpcopt -l select=1:ncpus=12:mem=20gb -I F. Eckhofer (TUBAF) HPC Tutorial VI 4

6 OpenMP What s OpenMP then? API for writing multithreaded applications Compiler directives Library functions Function prototypes and types in #include <omp.h> Compiler switch depending on vendor gcc -fopenmp -o foo foo.c icc -openmp -o bar bar.c #pragma omp construct [clause [clause]...] F. Eckhofer (TUBAF) HPC Tutorial VI 5

7 The fork/join model Source: Wikipedia F. Eckhofer (TUBAF) HPC Tutorial VI 6

8 OpenMP II Parallel for loops in OpenMP # pragma omp parallel for for ( i = start ; i < end ; i ++) { // do something } Task Write a simple multi-threaded program, that prints Hello world from each thread. Request interactive access on a node of the cluster Compile and run your program F. Eckhofer (TUBAF) HPC Tutorial VI 7

9 OpenMP IV for (i =0; i< SIZE ; i ++) { int val = f(i); sum += val ; } Threads communicate by sharing variables This can lead to unintended consequences: Race condition Scheduling of threads must not change result of the program Use synchronization to protect data conflicts F. Eckhofer (TUBAF) HPC Tutorial VI 8

10 OpenMP VI Variables Variables declared inside a for loop and the loop iterator are automatically private, others shared. You can manually override this using the shared and private clauses double A[ size ], B[ size ], C[ size ]; # pragma omp parallel for shared (A,B,C) private (i) for ( int i =0; i < size ; i ++) { A[i] = B[i] + C[i]; } F. Eckhofer (TUBAF) HPC Tutorial VI 9

11 OpenMP VI const int SLICES = ; double delta_x =...; for ( i = 0; i < SLICES ; i ++) { x = (i + 0.5) * delta_x ; sum += (4.0 / (1.0 + x * x )); } pi = sum * delta_ x ; Task Parallelize your piquad program from the first tutorial using OpenMP Pay close attention to private and shared variables F. Eckhofer (TUBAF) HPC Tutorial VI 10

12 OpenMP VI Task Parallelize your piquad program from the first tutorial using OpenMP Pay close attention to private and shared variables Some functions you may want to use: int omp_ get_ num_ threads (); int omp_ get_ thread_ num (); double omp_ get_ wtime (); F. Eckhofer (TUBAF) HPC Tutorial VI 10

13 OpenMP VI Critical sections Using a critical section, you can mark a potion of code, where only one thread at a time is allowed in: # pragma omp parallel for for (i =0; i< SIZE ; i ++) { int val = f( i); # pragma omp critical sum += val ; } Recuction variables Reduction variables combine the results of the different threads using a specified operation: # pragma omp parallel for reduction (+: sum ) for (i =0; i< SIZE ; i ++) { int val = f( i); sum += val ; } F. Eckhofer (TUBAF) HPC Tutorial VI 11

14 OpenMP VII You probably used an array to store the partial result from each thread If array elements share a cache line, this leads to what is called false sharing - the cache line is invalidated every time even though the threads share no data! Task Modify your program to use a reduction to avoid false sharing. Do you see a performance difference? Compare the performance of a critical section with the reduction variable. How does your program scale with the number of threads? OMP_NUM_THREADS=2./myprogram F. Eckhofer (TUBAF) HPC Tutorial VI 12

15 Let s go! Have fun! Slides and resources available from F. Eckhofer (TUBAF) HPC Tutorial VI 13