Prof. Thomas Sterling
Prof. Hartmut Kaiser
Department of Computer Science
Louisiana State University
April 12th, 2011
HIGH PERFORMANCE COMPUTING: MODELS, METHODS, &
MEANS
PARALLEL FILE I/O 3
LIBRARIES 2
CSC 7600 Lecture 23 : Parallel File I/O 3
Spring 2011
Puzzle of the Day
I thought the following C program is perfectly valid (after reading about
the comma operator in C). But there is a mistake in the following
program, can you identify it?
#include <stdio.h>
int main()
{
int a = 1, 2;
printf("a : %d\n",a);
return 0;
}
CSC 7600 Lecture 23 : Parallel File I/O 3
Spring 2011
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Puzzle of the Day
I thought the following C program is perfectly valid (after reading about
the comma operator in C). But there is a mistake in the following
program, can you identify it?
#include <stdio.h>
int main()
{
int a = (1, 2);
printf("a : %d\n",a);
return 0;
}
Comma operator has lowest precendence, even lower than
assignment.
CSC 7600 Lecture 23 : Parallel File I/O 3
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Topics
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Introduction
Scientific I/O Interface: netCDF
Scientific Data Package: HDF5
Application domain specific libraries
SMP programming support: CILK, TBB
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NetCDF: Introduction
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Stands for Network Common Data Form
Portable format to represent scientific data
Developed at the Unidata Program Center in Boulder, Colorado, with many
contributions from user community
Project page hosted by the Unidata program at University Corporation for
Atmospheric Research (UCAR): http://www.unidata.ucar.edu/software/netcdf/
Provides a set of interfaces for array-oriented data access and a collection of
data access libraries for C, Fortan (77 and 90), C++, Java, Perl, Python, and
other languages
Available on UNIX and Windows platforms
Features simple programming interface
Supports large data files (and 64-bit offsets)
Open source, freely available
Commonly used file extension is “.nc” (changed from “.cdf” to avoid confusion
with other formats)
Current stable release is version 4.1.2 (released on March 29, 2011)
Used extensively by a number of climate modeling, land and atmosphere,
marine, naval data storage, satellite data processing, theoretical physics
centers, geological institutes, commercial analysis, universities, as well as other
research institutions in over 30 countries
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NetCDF Rationale
• To facilitate the use of common datasets by distinct applications
• Permit datasets to be transported between or shared by
dissimilar computers transparently, i.e., without translation
(automatic handling of different data types, endian-ness, etc.)
• Reduce the programming effort usually spent interpreting formats
• Reduce errors arising from misinterpreting data and ancillary
data
• Facilitate using output from one application as input to another
• Establish an interface standard which simplifies the inclusion of
new software into already existing application set (originally:
Unidata system)
• However: not another DBMS!
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Key Properties of NetCDF Format
• Self-describing
– A netCDF file includes information about the data it contains
• Portable
– Files are accessible by computers that use different ways of representing
and storing of integers, floating-point numbers and characters
• Direct-access
– Enabling an efficient access to small subsets of a large dataset without the
need to read through all preceding data
• Appendable
– Additional data may be appended to a properly structured netCDF file
without copying the dataset or redefining its structure
• Sharable
– One writer and multiple readers may simultaneously access the same
netCDF file
• Archivable
– Access to all earlier forms of netCDF data will be supported by current and
future versions of the software
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NetCDF Dataset Building Blocks
• Data in netCDF are represented as n-dimensional arrays, with
n being 0, 1, 2, … (scalars are 0-dimensional arrays)
• Array elements are of the same data type
• Three basic entities:
– Dimension: has name and length; one dimension per array may be
UNLIMITED for unbounded arrays
– Variable: identifies array of values of the same type (byte, character,
short, int, float, or double)
• In addition, coordinate variables may be named identically to
dimensions, and by convention define physical coordinate set
corresponding to that dimension
– Attribute: provides additional information about a variable, or global
properties of a dataset
• There are established conventions for attribute names, e.g., unit,
long_name, valid_range, etc.
• Multiple attributes per dataset are allowed
• The only kind of data structures supported by netCDF classic
are collections of named arrays with attached vector attributes
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Common Data Form Language (CDL)
• NetCDF uses CDL to provide a way to describe data model
• CDL represents the information stored in binary netCDF files in a
human-readable form, e.g.:
netcdf example_1 { // example of CDL notation for a netCDF dataset
dimensions:
// dimension names and lengths are declared first
lat = 5, lon = 10, level = 4, time = unlimited;
variables:
// variable types, names, shapes, attributes
float temp(time,level,lat,lon);
temp:long_name = "temperature";
temp:units
= "celsius";
int lat(lat), lon(lon), level(level);
lat:units
= "degrees_north";
lon:units
= "degrees_east";
level:units = "millibars";
short time(time);
time:units = "hours since 1996-1-1";
// global attributes
:source = "Fictional Model Output";
data:
// optional data assignments
level = 1000, 850, 700, 500;
lat
= 20, 30, 40, 50, 60;
lon
= -160,-140,-118,-96,-84,-52,-45,-35,-25,-15;
time = 12;
}
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NetCDF Utilities
• ncgen
– takes input in CDL format and creates a netCDF file, or a C or
Fortran program that creates a netCDF dataset
ncgen [-b] [-o netcdf-file] [-c] [-f] [-k kind] [-x]
[input-file]
• ncdump
– generates the CDL text representation of a netCDF dataset on
standard output, optionally including some or all variable data
– Output from ncdump is an acceptable input to ncgen
ncdump [-c|-h] [-v var1,…] [-b lang] [-f lang] [-l len]
[-p fdig[,ddig]] [-n name] [-k] [input-file]
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NetCDF API: Create a Dataset
Function:
nc_create()
int nc_create(const char *path, int cmode, int *id);
Description:
Creates a new dataset returning its id that can be used in subsequent calls. The file name for the
dataset is specified in path. The cmode argument determines creation mode, and may contain
zero or more of the following flags or’d: NC_NOCLOBBER (to avoid overwriting existing files),
NC_SHARE (limits buffering in scenarios where one or more other processes concurrently read
the file being updated by a single writer process), NC_64BIT_OFFSET (create a file with 64-bit
offsets). The default zero value is aliased to NC_CLOBBER, i.e. no overwrite protection for
existing files. On success NC_NOERR is returned.
#include <netcdf.h>
...
int status;
int ncid;
...
status = nc_create("foo.nc", NC_NOCLOBBER, &ncid);
if (status != NC_NOERR) handle_error(status);
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NetCDF API: Open a Dataset
Function:
nc_open()
int nc_open(const char *path, int omode, int *id);
Description:
Opens an existing dataset stored in a file identified by path, returning its id. The omode argument
may contain zero or more of the following flags or’d: NC_WRITE (to open in read/write mode),
NC_SHARE (same meaning as for nc_create). The default (zero) is aliased to NC_NOWRITE,
which opens the file in read-only mode without sharing. On success NC_NOERR is returned.
#include <netcdf.h>
...
int status;
int ncid;
...
status = nc_open("foo.nc", 0, &ncid);
if (status != NC_NOERR) handle_error(status);
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NetCDF API: Create a Dimension
Function:
nc_def_dim()
int nc_def_dim(int id, const char *name, size_t len, int *dimid);
Description:
Adds a new dimension to an open dataset identified by id. The dimension name is pointed to by
name, and its length, a positive integer or constant NC_UNLIMITED, is passed in len. On
success NC_NOERR is returned and dimension id is stored in *dimid.
#include <netcdf.h>
...
int status, ncid, latid, recid;
...
status = nc_create("foo.nc", NC_NOCLOBBER, &ncid);
if (status != NC_NOERR) handle_error(status);
...
status = nc_def_dim(ncid, "lat", 18L, &latid);
if (status != NC_NOERR) handle_error(status);
status = nc_def_dim(ncid, "rec", NC_UNLIMITED, &recid);
if (status != NC_NOERR) handle_error(status);
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NetCDF API: Create a Variable
Function:
nc_def_var()
int nc_def_var(int id, const char *name, nc_type xtype, int ndims,
const int dimids[], int *varid);
Description:
Adds a new variable with name pointed to by name to an open dataset identified by id. The new
variable id is stored in *varid. xtype defines the external data type, and must be one of: NC_BYTE,
NC_CHAR, NC_SHORT, NC_INT, NC_FLOT, or NC_DOUBLE. The arguments ndims and dimids
specify respectively the number of dimensions and their ids. On success NC_NOERR is returned.
#include <netcdf.h>
int status, ncid;
/* error status and dataset ID */
int lat_dim, lon_dim, time_dim; /* dimension IDs */
int rh_id, rh_dimids[3];
/* variable ID and shape */
...
status = nc_create("foo.nc", NC_NOCLOBBER, &ncid);
if (status != NC_NOERR) handle_error(status);
/* define dimensions */
status = nc_def_dim(ncid, "lat", 5L, &lat_dim);
if (status != NC_NOERR) handle_error(status);
status = nc_def_dim(ncid, "lon", 10L, &lon_dim);
if (status != NC_NOERR) handle_error(status);
status = nc_def_dim(ncid, "time", NC_UNLIMITED, &time_dim);
if (status != NC_NOERR) handle_error(status);
/* define variable */
rh_dimids[0] = time_dim; rh_dimids[1] = lat_dim; rh_dimids[2] = lon_dim;
status = nc_def_var(ncid, "rh", NC_DOUBLE, 3, rh_dimids, &rh_id);
if (status != NC_NOERR) handle_error(status);
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NetCDF API: Leave Define Mode
Function:
nc_enddef()
int nc_enddef(int id);
Description:
Finalizes define mode and commits to disk changes made to the dataset. Returns NC_NOERR
on success.
#include <netcdf.h>
...
int status;
int ncid;
...
status = nc_create("foo.nc", NC_NOCLOBBER, &ncid);
if (status != NC_NOERR) handle_error(status);
...
/* create dimensions, variables, attributes */
...
status = nc_enddef(ncid); /*leave define mode*/
if (status != NC_NOERR) handle_error(status);
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NetCDF API: Quering Variable Information
Function:
nc_inq_varid(), nc_inq_var*()
int nc_inq_varid(int id, const char *name, int *varid);
int nc_inq_var(int id, int varid, char *name, nc_type *xtype, int *ndims, int
dimids[], int *natts);
int nc_inq_varname(int id, int varid, char *name);
int nc_inq_vartype(int id, int varid, nc_type *xtype);
int nc_inq_varndims(int id, int varid, *ndims);
int nc_inq_vardimid(int id, int varid, int dimids[]);
int nc_inq_varnatts(int id, int varid, int *natts);
Description:
The first function returns in *varid variable ID identified by name in dataset id.
The second function returns information about variable identified by varid, including its name (null
terminated, in area pointed to by name), type (in *xtype), number of dimensions (in *ndims),
dimension IDs (in dimids[]), and number of attributes (in *natts). The buffer to store variable name
has to be allocated by user and should be at least NC_MAX_NAME+1 characters long if the
name size is not known in advance. NC_NOERR is returned on success in both calls.
The remaining functions retrieve individual pieces of information, all of which nc_inq_var() returns
in a single call.
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NetCDF API: Variable Information (Example)
#include <netcdf.h>
...
int status;
/* error status */
int ncid;
/* netCDF ID */
int rh_id;
/* variable ID */
nc_type rh_type;
/* variable type */
int rh_ndims;
/* number of dims */
int rh_dimids[NC_MAX_VAR_DIMS]; /* dimension ids */
int rh_natts;
/* number of attributes */
...
status = nc_open("foo.nc", NC_NOWRITE, &ncid);
if (status != NC_NOERR) handle_error(status);
...
status = nc_inq_varid(ncid, "rh", &rh_id);
if (status != NC_NOERR) handle_error(status);
/* we don't need name, since we already know it */
status = nc_inq_var(ncid, rh_id, 0, &rh_type, &rh_ndims, rh_dimids, &rh_natts);
if (status != NC_NOERR) handle_error(status);
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NetCDF API: Read a Variable
Function:
nc_get_var_type()
int nc_get_var_text (int id, int varid, const char *ptr);
int nc_get_var_uchar (int id, int varid, const unsigned char *ptr);
int nc_get_var_schar (int id, int varid, const signed char *ptr);
int nc_get_var_short (int id, int varid, const short *ptr);
int nc_get_var_int
(int id, int varid, const int *ptr);
int nc_get_var_long (int id, int varid, const long *ptr);
int nc_get_var_float (int id, int varid, const float *ptr);
int nc_get_var_double(int id, int varid, const double *ptr);
Description:
Reads all the values from a netCDF variable referred to by varid of an open dataset with handle
id. The dataset must be in data mode. The values of multidimensional arrays are read into
consecutive memory locations with the last dimension varying fastest, starting at location pointed
to by ptr. Type conversion will occur if the type of data differs from the netCDF variable type.
Returns NC_NOERR on success.
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NetCDF API: Read a Variable (Example)
#include <netcdf.h>
...
#define TIMES 3
#define LATS 5
#define LONS 10
int status;
/* error status */
int ncid;
/* netCDF ID */
int rh_id;
/* variable ID */
double rh_vals[TIMES*LATS*LONS]; /* array to hold values */
...
status = nc_open("foo.nc", NC_NOWRITE, &ncid);
if (status != NC_NOERR) handle_error(status);
...
status = nc_inq_varid(ncid, "rh", &rh_id);
if (status != NC_NOERR) handle_error(status);
...
/* read values from netCDF variable */
status = nc_get_var_double(ncid, rh_id, rh_vals);
if (status != NC_NOERR) handle_error(status);
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NetCDF API: Write a Variable
Function:
nc_put_var_type()
int nc_put_var_text (int id, int varid, const char *ptr);
int nc_put_var_uchar (int id, int varid, const unsigned char *ptr);
int nc_put_var_schar (int id, int varid, const signed char *ptr);
int nc_put_var_short (int id, int varid, const short *ptr);
int nc_put_var_int
(int id, int varid, const int *ptr);
int nc_put_var_long (int id, int varid, const long *ptr);
int nc_put_var_float (int id, int varid, const float *ptr);
int nc_put_var_double(int id, int varid, const double *ptr);
Description:
Writes all values of a possibly multidimensional variable referred to by varid to an open dataset
with handle id. The location of the block of data values to be written is pointed to by ptr. The
values may be implicitly converted to the external data type specified in variable definition.
Returns NC_NOERR on success.
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NetCDF API: Write a Variable (Example)
#include <netcdf.h>
...
#define TIMES 3
#define LATS 5
#define LONS 10
int status;
/* error status */
int ncid;
/* netCDF ID */
int rh_id;
/* variable ID */
double rh_vals[TIMES*LATS*LONS]; /* array to hold values */
int i;
...
status = nc_open("foo.nc", NC_WRITE, &ncid);
if (status != NC_NOERR) handle_error(status);
...
status = nc_inq_varid(ncid, "rh", &rh_id);
if (status != NC_NOERR) handle_error(status);
...
for (i = 0; i < TIMES*LATS*LONS; i++)
rh_vals[i] = 0.5;
/* write values into netCDF variable */
status = nc_put_var_double(ncid, rh_id, rh_vals);
if (status != NC_NOERR) handle_error(status);
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NetCDF API: Close a Dataset
Function:
nc_close()
int nc_close(int id);
Description:
Closes an open dataset referred to by id. If the dataset is in define mode, nc_enddef() will be
called implicitly. After close, the id value may be reassigned to another newly opened or created
dataset. NC_NOERR is returned on success.
#include <netcdf.h>
...
int status;
int ncid;
...
status = nc_create("foo.nc", NC_NOCLOBBER, &ncid);
if (status != NC_NOERR) handle_error(status);
...
/* create dimensions, variables, attributes */
...
status = nc_close(ncid); /* close netCDF dataset */
if (status != NC_NOERR) handle_error(status);
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Parallel NetCDF
Possible usage scenarios on parallel computers
– Serial netCDF to access single files from a single process
– Multiple files accessed concurrently and independently through
serial netCDF API
– Parallel netCDF API to access single files cooperatively or
collectively
Source: http://www-unix.mcs.anl.gov/parallel-netcdf/pnetcdf-sc2003.pdf
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PnetCDF Implementation
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Available from:
http://trac.mcs.anl.gov/projects/parallelnetcdf
Library layer between user space and file
system space
Processes parallel I/O requests from
compute nodes, optimizes them, and passes
them down to the MPI-IO library
Advantages:
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Optimized for the netCDF file format
Regular and predictable data patterns in netCDF
compatible with MPI-IO interface
Low overhead of header I/O (local header copies
viable)
Well defined metadata creation phase
no need for collective I/O when accessing
individual objects
Disadvantages:
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No hierarchical data layout
Additions of data and header extensions are
costly after file creation due to linear layout order
No support for combining of multiple files in
memory (like HDF5 software mounting)
NetCDF source required for installation
Source: http://www-unix.mcs.anl.gov/parallel-netcdf/pnetcdf-sc2003.pdf
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PnetCDF Sample Calling Sequence
Source: http://www-unix.mcs.anl.gov/parallel-netcdf/sc03_present.pdf
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Topics
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Scientific I/O Interface: netCDF
Scientific Data Package: HDF5
Application domain specific libraries
SMP programming support: CILK, TBB
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Introduction to HDF5
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Acronym for Hierarchical Data Format, a portable, freely distributable, and well
supported library, file format, and set of utilities to manipulate it
Explicitly designed for use with scientific data and applications
Initial HDF version was created at NCSA/University of Illinois at UrbanaChampaign in 1988
First revision in widespread use was HDF4
Main HDF features include:
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Versatility: supports different data models and associated metadata
Self-describing: allows an application to interpret the structure and contents of a file
without any extraneous information
Flexibility: permits mixing and grouping various objects together in one file in a userdefined hierarchy
Extensibility: accommodates new data models, added both by the users and developers
Portability: can be shared across different platforms without preprocessing or
modifications
HDF5 is the most recent incarnation of the format, adding support for new type
and data models, parallel I/O and streaming, and removing a number of existing
restrictions (maximal file size, number of objects per file, flexibility of type use,
storage management configurability, etc.), as well as improving the performance
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HDF5 File Layout
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Major object classes: groups and datasets
Namespace resembles file system directory hierarchy (groups ≡ directories, datasets ≡ files)
Alias creation supported through links (both soft and hard)
Mounting of sub-hierachies is possible
User’s view
Low-level
organization
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HDF5 API & Tools
Library functionality grouped by
function name prefix
• H5: general purpose functions
• H5A: attribute interface
• H5D: dataset manipulation
• H5E: error handling
• H5F: file interface
• H5G: group creation and access
• H5I: object identifiers
• H5P: property lists
• H5R: references
• H5S: dataspace definition
• H5T: datatype manipulation
• H5Z: inline data filters and
compression
Command-line utilities
• h5cc, h5c++, h5fc: C, C++ and
Fortran compiler wrappers
• h5redeploy: updates compiler tools
after installation in new location
• h5ls, h5dump: lists hierarchy and
contents of a HDF5 file
• h5diff: compares two HDF5 files
• h5repack, h5repart: rearranges or
repartitions a file
• h5toh4, h4toh5: converts between
HDF5 and HDF4 formats
• h5import: imports data into HDF5 file
• gif2h5, h52gif: converts image data
between gif and HDF5 formats
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Basic HDF5 Concepts
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Group
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Dataset
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Defines dimensionality of a dataset (rank and dimension sizes)
Determines the effective subset of data to be stored or retrieved in subsequent file
operations (aka selection)
Datatype
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A named array of data elements (possibly multi-dimensional)
Specifies the representation of the dataset the way it will be stored in HDF5 file through
associated datatype and dataspace parameters
Dataspace
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Structure containing zero or more HDF5 objects (possibly other groups)
Provides a mechanism for mapping a name (path) to an object
“Root” group is a logical container of all other objects in a file
Describes atomically accessed element of a dataset
Permits construction of derived (compound) types, such as arrays, records,
enumerations
Influences conversion of numeric values between different platforms or
implementations
Attribute
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A small, user-defined structure attached to a group, dataset or named datatype,
providing additional information
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HDF5 Spatial Subset Examples
Source: http://hdf.ncsa.uiuc.edu/HDF5/RD100-2002/All_About_HDF5.pdf
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HDF5 Virtual File Layer
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Developed to cope with large number of available storage subsystem
variations
Permits custom file driver implementations and related optimizations
Source: http://hdf.ncsa.uiuc.edu/HDF5/RD100-2002/All_About_HDF5.pdf
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Overview of Data Storage Options
Source: http://hdf.ncsa.uiuc.edu/HDF5/RD100-2002/All_About_HDF5.pdf
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Simultaneous Spatial and Type
Transformation Example
Source: http://hdf.ncsa.uiuc.edu/HDF5/RD100-2002/All_About_HDF5.pdf
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Simple HDF5 Code Example
/* Writing and reading an existing dataset. */
#include "hdf5.h"
#define FILE "dset.h5"
int main() {
hid_t
file_id, dataset_id; /* identifiers */
herr_t status;
int
i, j, dset_data[4][6];
/* Initialize the dataset. */
for (i = 0; i < 4; i++)
for (j = 0; j < 6; j++)
dset_data[i][j] = i * 6 + j + 1;
/* Open an existing file. */
file_id = H5Fopen(FILE, H5F_ACC_RDWR, H5P_DEFAULT);
/* Open an existing dataset. */
dataset_id = H5Dopen(file_id, "/dset");
/* Write the dataset. */
status = H5Dwrite(dataset_id, H5T_NATIVE_INT, H5S_ALL, H5S_ALL, H5P_DEFAULT, dset_data);
status = H5Dread(dataset_id, H5T_NATIVE_INT, H5S_ALL, H5S_ALL, H5P_DEFAULT, dset_data);
/* Close the dataset. */
status = H5Dclose(dataset_id);
/* Close the file. */
status = H5Fclose(file_id);
}
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Parallel HDF5
• Relies on MPI-IO as the file layer driver
• Uses MPI for internal communications
• Most of the functionality controlled through property lists (requires
minimal HDF5 interface changes)
• Supports both individual and collective file access
• Three raw data storage layouts: contiguous, chunking and compact
• Enables additional optimizations through derived MPI datatypes
(esp. for regular collective accesses)
• Limitations
– Chunked storage with overlapping chunks (results non-deterministic)
– Read-only compression
– Writes with variable length datatypes not supported
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Topics
•
•
•
•
Scientific I/O Interface: netCDF
Scientific Data Package: HDF5
Application domain specific libraries
SMP programming support: CILK, TBB
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Application domains
• Linear algebra
– BLAS, ATLAS, LAPACK, ScaLAPACK, Slatec, pim
• Ordinary and Partial Differential Equations
– PETSc
• Mesh Manipulation and Load Balancing
– METIS, ParMETIS, CHACO, JOSTLE, PARTY
• Graph Manipulation
– Boost.Graph library
• Vector/Signal/Image Processing
– VSIPL, PESSL (IBMs Parallel Engineering Scientific Subroutine Library
• General Parallelization
– MPI, pthreads
• Other Domain Specific Libraries
– NAMD, NWChem, Fluent, Gaussian, LS-DYNA
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Application Domain Overview
• Linear Algebra Libraries
– Provide optimized methods for constructing sets of linear equations,
performing operations on them (matrix-matrix products, matrix-vector
products) and solving them (factoring, forward & backward
substitution.
– Commonly used libraries include BLAS, ATLAS, LAPACK,
ScaLAPACK, PaLAPACK
• PDE Solvers:
– General-purpose, parallel numerical PDE libraries
– Usual toolsets include manipulation of sparse data structures,
iterative linear system solvers, preconditioners, nonlinear solvers and
time-stepping methods.
– Commonly used libraries for solving PDEs include SAMRAI, PETSc,
PARASOL, Overture, among others.
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Application Domain Overview
• Mesh manipulation and Load Balancing
– These libraries help in partitioning meshes in roughly equal sizes
across processors, thereby balancing the workload while
minimizing size of separators and communication costs.
– Commonly used libraries for this purpose include METIS, ParMetis,
Chaco, JOSTLE among others.
• Other packages:
– FFTW: features highly optimized Fourier transform package
including both real and complex multidimensional transforms in
sequential, multithreaded, and parallel versions.
– NAMD: molecular dynamics library available for Unix/Linux,
Windows, OS X
– Fluent: computational fluid dynamics package, used for such
applications as environment control systems, propulsion, reactor
modeling etc.
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CBLAS Matrix Multiplication
#include <stdio.h>
#include <cblas.h>
#define N (sizeof(x)/sizeof(x[0]))
#define M ((sizeof(A)/sizeof(A[0]))/(N))
int main(int argc, char **argv)
{
unsigned int i;
double A[] = {1.0, 0.1, 0.01, 0.0,
10., 1.0, 0.1, 0.01,
0.0, 10., 1.0, 0.1,
0.0, 0.0, 10., 1.0,
double x[] = {1, 2, 3, 4, 5}, y[M];
0.0,
0.0,
0.01,
0.1};
/* y = 2.0*A*x + 0.0*y */
cblas_dgemv(CblasRowMajor, CblasNoTrans, M, N, 2.0,
A, N, x, 1, 0.0, y, 1);
/* print out the result */
for (i = 0; i < M; i++)
printf("y[%d] = %6.2lf\n", i, y[i]);
}
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uBLAS Matrix Multiplication 1
#include <iostream>
#include <boost/numeric/ublas/blas.hpp>
using namespace boost::numeric;
int main(int argc, char **argv)
{
ublas::matrix<double> A (4, 5);
A(0, 0) = 1.0; A(0, 1) = 0.1;
A(1, 0) = 10.0; A(1, 1) = 1.0;
A(2, 0) = 0.0; A(2, 1) = 10.0;
A(3, 0) = 0.0; A(3, 1) = 0.0;
A(0,
A(1,
A(2,
A(3,
2)
2)
2)
2)
=
=
=
=
0.01;
0.1;
1.0;
10.0;
A(0,
A(1,
A(2,
A(3,
3)
3)
3)
3)
=
=
=
=
0.0;
0.01;
0.1;
1.0;
A(0,
A(1,
A(2,
A(3,
4)
4)
4)
4)
=
=
=
=
0.0;
0.0;
0.01;
0.1;
ublas::vector<double> x(5);
x[0] = 1; x[1] = 2; x[2] = 3; x[3] = 4; x[4] = 5;
// y = 2.0*A*x
ublas::vector<double> y = prod(2*A, x);
for (unsigned int i = 0; i < y.size(); ++i)
std::cout << "y[" << i << "] = " << y[i] << std::endl;
return 0;
}
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uBLAS Matrix Multiplication 2
#include <iostream>
#include <boost/numeric/ublas/blas.hpp>
using namespace boost::numeric;
int main(int argc, char **argv)
{
ublas::banded_matrix<double> A (4, 5, 1, 2);
A(0, 0) = 1.0; A(0, 1) = 0.1; A(0, 2) = 0.01;
A(1, 0) = 10.0; A(1, 1) = 1.0; A(1, 2) = 0.1;
A(2, 1) = 10.0; A(2, 2) = 1.0;
A(3, 2) = 10.0;
A(1, 3) = 0.01;
A(2, 3) = 0.1; A(2, 4) = 0.01;
A(3, 3) = 1.0; A(3, 4) = 0.1;
ublas::vector<double> x(5);
x[0] = 1; x[1] = 2; x[2] = 3; x[3] = 4; x[4] = 5;
// y = 2.0*A*x
ublas::vector<double> y = prod(2*A, x);
for (unsigned int i = 0; i < y.size(); ++i)
std::cout << "y[" << i << "] = " << y[i] << std::endl;
return 0;
}
CSC 7600 Lecture 23 : Parallel File I/O 3
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NewMath Matrix Multiplication
#include <iostream>
#include <newmatap.h>
// need matrix applications
using namespace NEWMAT;
int main(int argc, char **argv)
{
Matrix A (4, 5);
double d[] = {
1.0, 0.1, 0.01, 0.0,
10.0, 1.0, 0.1, 0.01,
0.0, 10.0, 1.0, 0.1,
0.0, 0.0, 10.0, 1.0,
};
A << d;
0.0,
0.0,
0.01,
0.1,
ColumnVector x(5);
double xv[] = { 1.0, 2.0, 3.0, 4.0, 5.0 };
x << xv;
// y = 2.0*A*x
ColumnVector y = 2 * A * x;
for (int i = 1; i <= y.Nrows(); ++i)
std::cout << "y[" << i << "] = " << y(i) << std::endl;
return 0;
}
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Blitz++
• Blitz++ is a C++ class library for scientific computing
which provides performance on par with Fortran 77/90.
• It uses template meta-programming techniques to
achieve high performance
• The current versions provide dense arrays and vectors,
random number generators, and small vectors and
matrices.
• Blitz++ is distributed freely under an open source license
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Blitz++ Example
• Transforms a vector y using a principal rotation about the
third axis:
 x1  cos 
 x    sin 
 2 
 x3   0
 sin 
cos 
0
0  y1 
0  y2 
1  y3 
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Blitz++ Example
// Blitz++ code
void transform(double alpha, TinyVector<double, 3>& x,
const TinyVector<double, 3>& y)
{
double cosa = cos(alpha), sina = sin(alpha);
// Create the principal rotation matrix C_3(alpha)
TinyMatrix<double, 3, 3> C;
C = cosa, -sina, 0.0,
sina, cosa, 0.0,
0.0,
0.0, 1.0;
x = C * y;
}
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Blitz++ Example (cont.)
// low-level implementation
void transform2(double alpha, double* x, double* y)
{
double c = cos(alpha), s = sin(alpha);
x[0] = c * y[0] - s * y[1];
x[1] = s * y[0] + c * y[1];
x[2] = y[2];
}
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Blitz++ Example: Results
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Outlook
Elliptic PDE discretized by Finite Volume
Mathematical notation
C++ notation
Functional specification with a Domain
Specific Embedded Language (DSEL)
equation = sum<vertex_edge>
[
sumf<edge_vertex>(0.0, _e)
[
pot * orient(_e, _1)
] * A / d * eps
] - V * rho
References: [1]
CSC 7600 Lecture 23 : Parallel File I/O 3
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Demo
• NetCDF Demo
• HDF5 Demo
• LAPACK Demo
CSC 7600 Lecture 23 : Parallel File I/O 3
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Topics
•
•
•
•
Scientific I/O Interface: netCDF
Scientific Data Package: HDF5
Application domain specific libraries
SMP programming support: CILK, TBB
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CILK
• Extends the C language with some keywords
• Programmer is responsible for exposing the parallelism
• Runtime environment (automatically) takes care of
– Load balancing
– Synchronization
– Communication protocols
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CILK
• Basic parallelism in CILK is achieved by using the
following keywords
– Spawn indicates that the procedure calling it can safely operate
in parallel with other executing code
• Note that the scheduler is not obligated to run this procedure in
parallel
– Sync indicates that execution of the current procedure cannot
proceed until all previously spawned procedures have completed
an returned their results
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CILK Example
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CILK Example
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CILK
• Work stealing
– Each processor maintains a work dequeue of ready threads, and
it manipulates the bottom of the dequeue like a stack.
– When a processor runs out of work, it steals a thread from the
top of a random victim’s queue.
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Topics
•
•
•
•
Scientific I/O Interface: netCDF
Scientific Data Package: HDF5
CILK
TBB
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TBB
• TBB
– C++ template library which takes advantage of multi-core
processors
– Developed by Intel Corporation
– Stable release ver. 3.0, May 4 2010
– Supports Microsoft Windows, Mac OS X, Linux using various
compilers (Visual C++, Intel C++ Compiler, GNU Compiler
Collection)
– For additional information
• http://threadingbuildingblocks.org/
• http://en.wikipedia.org/wiki/Intel_Threading_Building_Blocks
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TBB
• TBB Library
– Allows a programmer to avoid complications arising from use of
native threading packages, such as POSIX, Windows threads,
etc.
– Provides access to multiple processors is abstracted by treating
operations as “tasks”
• “Tasks” are allocated to individual cores dynamically by a run-time
engine
– Provides (automated) efficient use of cache
– Relies on generic programming
– Has a higher level API compared to MPI or POSIX threads
(which leads to fewer bugs and easier code development)
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TBB
• Notable Features
– Recursive splitting
• TBB relies on breaking problems up recursively as required to get to
the right level of parallel tasks
– Task stealing (instead of global task queue)
• Scalability improves due to task stealing
– Algorithms
• Easy to use due to generic programming approach
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TBB Example
01:
02:
04:
05:
06:
07:
08:
09:
…
36:
37:
38:
39:
40:
41:
42:
43:
44:
45:
46:
47:
48:
#include <iostream>
#include <string>
#include “tbb/task_scheduler_init.h”
#include “tbb/parallel_for.h”
#include “tbb/blocked_range.h”
using namespace tbb;
using namespace std;
static const size_t N = 23;
int main() {
task_scheduler_init init;
string str[N] = { string(”a”), string(”b”) };
for (size_t i = 2; i < N; ++i) str[i] = str[i-1]+str[i-2];
string &to_scan = str[N-1];
size_t *max = new size_t[to_scan.size()];
size_t *pos = new size_t[to_scan.size()];
parallel_for(blocked_range<size_t>(0, to_scan.size(), 100),
SubStringFinder( to_scan, max, pos ) );
for (size_t I = 0; I < to_scan.size(); ++i)
cout << ” ” << max[i] << ”(” << pos[i] << ”)” << endl;
return 0;
}
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Summary – Material for the Test
• NetCDF – 5-25
• HDF5 – 26-37
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