University:
Boston University
Course:
MET CS 201 | Introduction to Programming
Academic year:

2024
Views:

256

Pages:

13
Author:

Lafuentedqri

HPC user environment • Operation system: Linux (Redhat/CentOS, Ubuntu, etc), Unix. • Login: ssh, gsissh. • File transfer: secure ftp (scp), grid ftp (globus). • Job scheduler: Slurm, PBS, SGE, Loadleveler. • Software management: module. • Compilers: Intel, GNU, PGI. • MPI implementations: OpenMPI, MPICH, MVAPICH, Intel MPI. • Debugging and profiling tools: Totalview, Tau, DDT, Vtune. • Programming Languages: C, C++, Fortran, Python, Perl, R, MATLAB, Julia Scientific disciplines in HPC  Typical scientific computing catalogs that can benefit from HPC: • Computational Physics • Linear algebra • High-energy physics • Computer science • Astrophysics • Data science • Geophysics • Machine/deep learning • Climate and weather science • Biophysics • Computational fluid dynamics • Bioinformatics • Computer aided engineering • Finance informatics • Material sciences • Scientific Visualization • Computational chemistry • Social sciences • Molecular dynamics CPU-hours by field of science • Statistics from XSEDE Scientific computing software • Numerical Libraries: Lapack/Blas, FFTw, MKL, GSL, PETSc, Slepc, HDF5, NetCDF, Numpy, Scipy. • Physics and Engineering: BerkeleyGW, Root, Gurobi, Abaqus, Openfoam, Fluent, Ansys, WRF • Chemistry and material science: Gaussian, NWChem, VASP, QuantumEspresso, Gamess, Octopus • Molecular dynamics: Lammps, Namd, Gromacs, Charmm, Amber • Bioinformatics: Bowtie, BLAST, Bwa, Impute, Minimac, Picard, Plink, Solar, Tophat, Velvet. • Data science and machine learning: Hadoop, Spark, Tensorflow, Caffe, Torch, cuDNN, Scikit-learn. Parallel Computing  Parallel computing is a type of computation in which many calculations are carried out simultaneously, based on the principle that large problems can often be divided into smaller ones, which are then solved at the same time.  Speedup of a parallel program, p: number of processors/cores, α: fraction of the program that is serial. Distributed or shared memory systems • Shared memory system • Distributed memory system • For example, a single node on a cluster • For example, multi-nodes on a cluster • Open Multi-processing (OpenMP) or MPI • Message Passing Interface (MPI)  Figures are from the book Using OpenMP: Portable Shared Memory Parallel Programming An example: weather science • Serial weather model • Shared-memory weather model (for several cores within one node) • Distributed-memory weather model (for many nodes within one cluster) National-wide HPC resources: XSEDE • XSEDE (eXtreme Science and Engineering Discovery Environment) is a virtual system that provides compute resources for scientists and researchers from all over the US. • Its mission is to facilitate research collaboration among institutions, enhance research productivity, provide remote data transfer, and enable remote instrumentation. • A combination of supercomputers in many institutions in the US. XSEDE resources (1) XSEDE resources (2) XSEDE resources (3)