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// -------------------------------------------------------------
// CUDPP -- CUDA Data Parallel Primitives library
// -------------------------------------------------------------
// $Revision$
// $Date$
// -------------------------------------------------------------
// This source code is distributed under the terms of license.txt in
// the root directory of this source distribution.
// -------------------------------------------------------------
/**
* @file
* cudpp.h
*
* @brief Main library header file. Defines public interface.
*
* The CUDPP public interface is a C-only interface to enable
* linking with code written in other languages (e.g. C, C++,
* and Fortran). While the internals of CUDPP are not limited
* to C (C++ features are used), the public interface is
* entirely C (thus it is declared "extern C").
*/
/**
* \mainpage
*
* \section introduction Introduction
*
* CUDPP is the CUDA Data Parallel Primitives Library. CUDPP is a
* library of data-parallel algorithm primitives such as
* parallel-prefix-sum ("scan"), parallel sort and parallel reduction.
* Primitives such as these are important building blocks for a wide
* variety of data-parallel algorithms, including sorting, stream
* compaction, and building data structures such as trees and
* summed-area tables.
*
* \section overview Overview Presentation
*
* A brief set of slides that describe the features, design principles,
* applications and impact of CUDPP is available here:
* <a href="http://cudpp.googlecode.com/svn/trunk/cudpp/doc/CUDPP_slides.pdf">CUDPP Presentation</a>.
*
* \section homepage Homepage
* Homepage for CUDPP: http://code.google.com/p/cudpp
*
* Announcements and discussion of CUDPP are hosted on the
* <a href="http://groups.google.com/group/cudpp?hl=en">CUDPP Google Group</a>.
*
* \section getting-started Getting Started with CUDPP
*
* You may want to start by browsing the \link publicInterface CUDPP Public
* Interface\endlink. For information on building CUDPP, see
* \ref building-cudpp "Building CUDPP".
*
* The "apps" subdirectory included with CUDPP has a few source code samples
* that use CUDPP:
* - \ref example_simpleCUDPP "simpleCUDPP", a simple example of using
* cudppScan()
* - satGL, an example of using cudppMultiScan() to generate a summed-area
* table (SAT) of a scene rendered in real time. The SAT is then used to simulate
* depth of field blur.
* - cudpp_testrig, a comprehensive test application for all the functionality
* of CUDPP
*
* We have also provided a code walkthrough of the
* \ref example_simpleCUDPP "simpleCUDPP" example.
*
* \section getting-help Getting Help and Reporting Problems
*
* To get help using CUDPP, please use the
* <a href="http://groups.google.com/group/cudpp?hl=en">CUDPP Google Group</a>.
*
* To report CUDPP bugs or request features, you may use either the above
* CUDPP Google Group, or you can file an issue directly using
* <a href="http://code.google.com/p/cudpp/issues/list">Google Code</a>.
*
* \section release-notes Release Notes
*
* For specific release details see the \ref changelog "Change Log".
*
* This release (1.1.1) is a bugfix release to CUDPP 1.1 that includes
* fixes to support CUDA 3.0 and the new NVIDIA Fermi architecture,
* including GeForce 400 series and Tesla 20 series GPUs. It also has
* bug fixes for 64-bit OSes.
*
* \section opSys Operating System Support
*
* This release (1.1.1) has been thoroughly tested on the following OSes.
* - Windows XP (32-bit) (CUDA 2.2, 3.0)
* - Windows 7 (64-bit) (CUDA 3.0)
* - Redhat Enterprise Linux 5 (64-bit) (CUDA 3.0)
* - and Mac OS X 10.6 (Snow Leopard, 64-bit) (CUDA 3.0)
*
* We expect CUDPP to build and run correctly on other flavors of Linux
* and Windows, but these are not actively tested by the developers at
* this time.
*
* Notes: CUDPP is not compatible with CUDA 2.1. A compiler bug in 2.1
* causes the compiler to crash. Also, starting with CUDPP 1.1.1, we are
* no longer testing CUDA device emulation, because it is deprecated in
* CUDA 3.0 and will be removed from future CUDA versions.
*
* \section cuda CUDA
* CUDPP is implemented in
* <a href="http://developer.nvidia.com/cuda">CUDA C/C++</a>. It requires the
* CUDA Toolkit version 2.2 or later. Please see the NVIDIA
* <a href="http://developer.nvidia.com/cuda">CUDA</a> homepage to download
* CUDA as well as the CUDA Programming Guide and CUDA SDK, which includes many
* CUDA code examples. Some of the samples in the CUDA SDK (including
* "marchingCubes", "lineOfSight", and radixSort) also use CUDPP.
*
* \section design-goals Design Goals
* Design goals for CUDPP include:
*
* - Performance. We aim to provide best-of-class performance for our
* primitives. We welcome suggestions and contributions that will improve
* CUDPP performance. We also want to provide primitives that can be easily
* benchmarked, and compared against other implementations on GPUs and other
* processors.
* - Modularity. We want our primitives to be easily included in other
* applications. To that end we have made the following design decisions:
* - CUDPP is provided as a library that can link against other applications.
* - CUDPP calls run on the GPU on GPU data. Thus they can be used
* as standalone calls on the GPU (on GPU data initialized by the
* calling application) and, more importantly, as GPU components in larger
* CPU/GPU applications.
* - CUDPP is implemented as 4 layers:
* -# The \link publicInterface Public Interface\endlink is the external
* library interface, which is the intended entry point for most
* applications. The public interface calls into the
* \link cudpp_app Application-Level API\endlink.
* -# The \link cudpp_app Application-Level API\endlink comprises functions
* callable from CPU code. These functions execute code jointly on the
* CPU (host) and the GPU by calling into the
* \link cudpp_kernel Kernel-Level API\endlink below them.
* -# The \link cudpp_kernel Kernel-Level API\endlink comprises functions
* that run entirely on the GPU across an entire grid of thread blocks.
* These functions may call into the \link cudpp_cta CTA-Level API\endlink
* below them.
* -# The \link cudpp_cta CTA-Level API\endlink comprises functions that run
* entirely on the GPU within a single Cooperative Thread Array (CTA,
* aka thread block). These are low-level functions that implement core
* data-parallel algorithms, typically by processing data within shared
* (CUDA \c __shared__) memory.
*
* Programmers may use any of the lower three CUDPP layers in their own
* programs by building the source directly into their application. However,
* the typical usage of CUDPP is to link to the library and invoke functions in
* the CUDPP \link publicInterface Public Interface\endlink, as in the
* \ref example_simpleCUDPP "simpleCUDPP", satGL, and cudpp_testrig application
* examples included in the CUDPP distribution.
*
* In the future, if and when CUDA supports building device-level libraries, we
* hope to enhance CUDPP to ease the use of CUDPP internal algorithms at all
* levels.
*
* \subsection uses Use Cases
* We expect the normal use of CUDPP will be in one of two ways:
* -# Linking the CUDPP library against another application.
* -# Running our "test" application, cudpp_testrig, that exercises
* CUDPP functionality.
*
* \section references References
* The following publications describe work incorporated in CUDPP.
*
* - Mark Harris, Shubhabrata Sengupta, and John D. Owens. "Parallel Prefix Sum (Scan) with CUDA". In Hubert Nguyen, editor, <i>GPU Gems 3</i>, chapter 39, pages 851&ndash;876. Addison Wesley, August 2007. http://graphics.idav.ucdavis.edu/publications/print_pub?pub_id=916
* - Shubhabrata Sengupta, Mark Harris, Yao Zhang, and John D. Owens. "Scan Primitives for GPU Computing". In <i>Graphics Hardware 2007</i>, pages 97&ndash;106, August 2007. http://graphics.idav.ucdavis.edu/publications/print_pub?pub_id=915
* - Shubhabrata Sengupta, Mark Harris, and Michael Garland. "Efficient parallel scan algorithms for GPUs". NVIDIA Technical Report NVR-2008-003, December 2008. http://mgarland.org/papers.html#segscan-tr
* - Nadathur Satish, Mark Harris, and Michael Garland. "Designing Efficient Sorting Algorithms for Manycore GPUs". In <i>Proceedings of the 23rd IEEE International Parallel & Distributed Processing Symposium</i>, May 2009. http://mgarland.org/papers.html#gpusort
* - Stanley Tzeng, Li-Yi Wei. "Parallel White Noise Generation on a GPU via Cryptographic Hash". In <i>Proceedings of the 2008 Symposium on Interactive 3D Graphics and Games</i>, pages 79&ndash;87, February 2008. http://research.microsoft.com/apps/pubs/default.aspx?id=70502
*
* Many researchers are using CUDPP in their work, and there are many publications
* that have used it \ref cudpp_refs "(references)". If your work uses CUDPP, please
* let us know by sending us a reference (preferably in BibTeX format) to your work.
*
* \section citing Citing CUDPP
*
* If you make use of CUDPP primitives in your work and want to cite
* CUDPP (thanks!), we would prefer for you to cite the appropriate
* papers above, since they form the core of CUDPP. To be more specific,
* the GPU Gems paper describes (unsegmented) scan, multi-scan for
* summed-area tables, and stream compaction. The NVIDIA technical report
* describes the current scan and segmented scan algorithms used in the
* library, and the Graphics Hardware paper describes an earlier
* implementation of segmented scan, quicksort, and sparse matrix-vector
* multiply. The IPDPS paper describes the radix sort used in CUDPP, and
* the I3D paper describes the random number generation algorithm.
*
* \section credits Credits
* \subsection developers CUDPP Developers
* - <a href="http://www.markmark.net">Mark Harris</a>, NVIDIA Corporation
* - <a href="http://www.ece.ucdavis.edu/~jowens/">John D. Owens</a>, University of California, Davis
* - <a href="http://graphics.cs.ucdavis.edu/~shubho/">Shubho Sengupta</a>, University of California, Davis
* - Stanley Tzeng, University of California, Davis
* - <a href="http://www.ece.ucdavis.edu/~yaozhang/">Yao Zhang</a>, University of California, Davis
* - <a href="http://www.ece.ucdavis.edu/~aaldavid/">Andrew Davidson</a>, University of California, Davis (formerly Louisiana State University)
*
* \subsection contributors Other CUDPP Contributors
* - <a href="http://www.eecs.berkeley.edu/~nrsatish/">Nadatur Satish</a>, University of California, Berkeley
*
* \subsection acknowledgments Acknowledgments
*
* Thanks to Jim Ahrens, Timo Aila, Nathan Bell, Ian Buck, Guy Blelloch,
* Jeff Bolz, Michael Garland, Jeff Inman, Eric Lengyel, Samuli Laine,
* David Luebke, Pat McCormick, and Richard Vuduc for their contributions
* during the development of this library.
*
* CUDPP Developers from UC Davis thank their funding agencies:
* - Department of Energy Early Career Principal Investigator Award
* DE-FG02-04ER25609
* - SciDAC Institute for Ultrascale Visualization (http://www.iusv.org/)
* - Los Alamos National Laboratory
* - National Science Foundation (grant 0541448)
* - Generous hardware donations from NVIDIA
*
* \section license-overview CUDPP Copyright and Software License
* CUDPP is copyright The Regents of the University of California, Davis campus
* and NVIDIA Corporation. The library, examples, and all source code are
* released under the BSD license, designed to encourage reuse of this software
* in other projects, both commercial and non-commercial. For details, please
* see the \ref license page.
*
* Note that prior to release 1.1 of CUDPP, the license used was a modified
* BSD license. With release 1.1, this license was replaced with the pure BSD
* license to facilitate the use of open source hosting of the code.
*/
/**
* @page license CUDPP License
*
* \section licenseBSD CUDPP License
*
* CUDPP is released under the
* <a href="http://www.opensource.org/licenses/bsd-license.php">BSD license</a>.
*
* @include license.txt
*
*/
/**
* @page changelog CUDPP Change Log
*
* @include changelog.txt
*/
/**
* @page cudpp_refs Publications that use CUDPP
*
* @htmlinclude doc/bib/cudpp_refs.html
*/
/**
* @page cudpp_refs_bib Bibliography for publications that use CUDPP
*
* @htmlinclude doc/bib/cudpp_refs_bib.html
*/
/**
* @page building-cudpp Building CUDPP
*
* CUDPP has currently been tested in Windows XP, Windows Vista, Mac OS X
* and Linux. See \ref release-notes for release specific platform support.
*
* \section build-win32 Building CUDPP on Windows XP
*
* CUDPP can be built using either or MSVC 8 (2005) or MSVC 9 (2008). To
* build, open cudpp/cudpp.sln. Then you can build the library
* using the "build" command as you would with any other workspace. There are
* four configurations: debug, release, emudebug, and emurelease. The first
* two are self-explanatory. The second two are built to use CUDA device
* emulation, meaning they will be run (slowly) on the CPU.
*
* \section build-linux Building CUDPP on Linux and Mac OS X
*
* CUDPP can be built using standard g++ and Make tools on Linux, by typing
* "make" in the "cudpp/" subdirectory. Before building CUDPP, you should
* first build the CUDA Utility Library (libcutil) by typing "make; make dbg=1"
* in the "common/" subdirectory. This will generate libcutil.a and
* libcutilD.a.
*
* The makefile for CUDPP and all sample applications take the optional
* arguments "emu=1" and "dbg=1". The former builds CUDPP for device emulation,
* and the latter for debugging. The two flags can be combined. "verbose=1"
* can be used to see all compiler output.
*
* \section build-apps Building CUDPP Sample Applications
*
* The sample applications in the "apps/" subdirectory can be built exactly
* like CUDPP is--either by opening the appropriate .sln/.vcproj file in MSVC
* in Windows, or using "make" in Linux.
*
* On some Linux installations you will get linker errors relating to "-lXi"
* and "-lXmu". To fix this, you will need to install libXi and libXmu. On
* Debian and Ubuntu, for example, you can simply run
* "sudo apt-get install libxi-dev", and
* "sudo apt-get install libxmu-dev"
*
*/
#ifndef __CUDPP_H__
#define __CUDPP_H__
#include <stdlib.h> // for size_t
#ifdef __cplusplus
extern "C" {
#endif
/**
* @brief CUDPP Result codes returned by CUDPP API functions.
*/
enum CUDPPResult
{
CUDPP_SUCCESS = 0, /**< No error. */
CUDPP_ERROR_INVALID_HANDLE, /**< Specified handle (for example,
to a plan) is invalid. **/
CUDPP_ERROR_ILLEGAL_CONFIGURATION, /**< Specified configuration is
illegal. For example, an
invalid or illogical
combination of options. */
CUDPP_ERROR_UNKNOWN = 9999 /**< Unknown or untraceable error. */
};
/**
* @brief Options for configuring CUDPP algorithms.
*
* @see CUDPPConfiguration, cudppPlan, CUDPPAlgorithm
*/
enum CUDPPOption
{
CUDPP_OPTION_FORWARD = 0x1, /**< Algorithms operate forward:
* from start to end of input
* array */
CUDPP_OPTION_BACKWARD = 0x2, /**< Algorithms operate backward:
* from end to start of array */
CUDPP_OPTION_EXCLUSIVE = 0x4, /**< Exclusive (for scans) - scan
* includes all elements up to (but
* not including) the current
* element */
CUDPP_OPTION_INCLUSIVE = 0x8, /**< Inclusive (for scans) - scan
* includes all elements up to and
* including the current element */
CUDPP_OPTION_CTA_LOCAL = 0x10, /**< Algorithm performed only on
* the CTAs (blocks) with no
* communication between blocks.
* @todo Currently ignored. */
CUDPP_OPTION_KEYS_ONLY = 0x20, /**< No associated value to a key
* (for global radix sort) */
CUDPP_OPTION_KEY_VALUE_PAIRS = 0x40, /**< Each key has an associated value */
};
/**
* @brief Datatypes supported by CUDPP algorithms.
*
* @see CUDPPConfiguration, cudppPlan
*/
enum CUDPPDatatype
{
CUDPP_CHAR, //!< Character type (C char)
CUDPP_UCHAR, //!< Unsigned character (byte) type (C unsigned char)
CUDPP_INT, //!< Integer type (C int)
CUDPP_UINT, //!< Unsigned integer type (C unsigned int)
CUDPP_FLOAT //!< Float type (C float)
};
/**
* @brief Operators supported by CUDPP algorithms (currently scan and
* segmented scan).
*
* These are all binary associative operators.
*
* @see CUDPPConfiguration, cudppPlan
*/
enum CUDPPOperator
{
CUDPP_ADD, //!< Addition of two operands
CUDPP_MULTIPLY, //!< Multiplication of two operands
CUDPP_MIN, //!< Minimum of two operands
CUDPP_MAX //!< Maximum of two operands
};
/**
* @brief Algorithms supported by CUDPP. Used to create appropriate plans using
* cudppPlan.
*
* @see CUDPPConfiguration, cudppPlan
*/
enum CUDPPAlgorithm
{
CUDPP_SCAN, //!< Scan or prefix-sum
CUDPP_SEGMENTED_SCAN, //!< Segmented scan
CUDPP_COMPACT, //!< Stream compact
CUDPP_REDUCE, //!< Parallel reduction (NOTE: currently unimplemented)
CUDPP_SORT_RADIX, //!< Radix sort
CUDPP_SPMVMULT, //!< Sparse matrix-dense vector multiplication
CUDPP_RAND_MD5, //!< PseudoRandom Number Generator using MD5 hash algorithm
CUDPP_ALGORITHM_INVALID, //!< Placeholder at end of enum
};
/**
* @brief Configuration struct used to specify algorithm, datatype,
* operator, and options when creating a plan for CUDPP algorithms.
*
* @see cudppPlan
*/
struct CUDPPConfiguration
{
CUDPPAlgorithm algorithm; //!< The algorithm to be used
CUDPPOperator op; //!< The numerical operator to be applied
CUDPPDatatype datatype; //!< The datatype of the input arrays
unsigned int options; //!< Options to configure the algorithm
};
#define CUDPP_INVALID_HANDLE 0xC0DABAD1
typedef size_t CUDPPHandle;
/* To use CUDPP as a static library, #define CUDPP_STATIC_LIB before
* including cudpp.h
*/
#define CUDPP_STATIC_LIB
#ifndef CUDPP_DLL
#ifdef _WIN32
#ifdef CUDPP_STATIC_LIB
#define CUDPP_DLL
#else
#ifdef BUILD_DLL
#define CUDPP_DLL __declspec(dllexport)
#else
#define CUDPP_DLL __declspec(dllimport)
#endif
#endif
#else
#define CUDPP_DLL
#endif
#endif
// Plan allocation (for scan, sort, and compact)
CUDPP_DLL
CUDPPResult cudppPlan(CUDPPHandle *planHandle,
CUDPPConfiguration config,
size_t n,
size_t rows,
size_t rowPitch);
CUDPP_DLL
CUDPPResult cudppDestroyPlan(CUDPPHandle plan);
// Scan and sort algorithms
CUDPP_DLL
CUDPPResult cudppScan(CUDPPHandle planHandle,
void *d_out,
const void *d_in,
size_t numElements);
CUDPP_DLL
CUDPPResult cudppMultiScan(CUDPPHandle planHandle,
void *d_out,
const void *d_in,
size_t numElements,
size_t numRows);
CUDPP_DLL
CUDPPResult cudppSegmentedScan(CUDPPHandle planHandle,
void *d_out,
const void *d_idata,
const unsigned int *d_iflags,
size_t numElements);
CUDPP_DLL
CUDPPResult cudppCompact(CUDPPHandle planHandle,
void *d_out,
size_t *d_numValidElements,
const void *d_in,
const unsigned int *d_isValid,
size_t numElements);
CUDPP_DLL
CUDPPResult cudppSort(CUDPPHandle planHandle,
void *d_keys,
void *d_values,
int keybits,
size_t numElements);
// Sparse matrix allocation
CUDPP_DLL
CUDPPResult cudppSparseMatrix(CUDPPHandle *sparseMatrixHandle,
CUDPPConfiguration config,
size_t n,
size_t rows,
const void *A,
const unsigned int *h_rowIndices,
const unsigned int *h_indices);
CUDPP_DLL
CUDPPResult cudppDestroySparseMatrix(CUDPPHandle sparseMatrixHandle);
// Sparse matrix-vector algorithms
CUDPP_DLL
CUDPPResult cudppSparseMatrixVectorMultiply(CUDPPHandle sparseMatrixHandle,
void *d_y,
const void *d_x);
// random number generation algorithms
CUDPP_DLL
CUDPPResult cudppRand(CUDPPHandle planHandle,void * d_out, size_t numElements);
CUDPP_DLL
CUDPPResult cudppRandSeed(const CUDPPHandle planHandle, unsigned int seed);
#ifdef __cplusplus
}
#endif
#endif
// Leave this at the end of the file
// Local Variables:
// mode:c++
// c-file-style: "NVIDIA"
// End:
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