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This guide is intended for application programmers, scientists and engineers proficient in programming with the Fortran language. The OpenACC Application Program Interface is a collection of compiler directives and runtime routines that allows the programmer to specify loops and regions of code for offloading from a host CPU to an attached accelerator, such as a GPU. OpenMP is a specification for a set of compiler directives, an applications programming interface API , and a set of environment variables that can be used to specify parallel execution from Fortran and other languages.

The methods for passing device arrays to library functions from host code differ only in syntax compared to those used in OpenACC. This document does not contain explanations or purposes of the library functions, nor does it contain details of the approach used in the CUDA implementation to target GPUs.

This module provides a standard way for dealing with isues such as inter-language data types, capitalization, adding underscores to symbol names, or passing arguments by value. In some cases, NVIDIA has written small wrappers around the C library function, to make the Fortran call site more “Fortran-like”, hiding some issues exposed in the C interfaces like handle management, host vs. All of the libraries covered in this document contain functions which are callable from OpenACC host code.

Most functions take some arguments which are expected to be device pointers the address of a variable in device global memory. There are several ways to do that in OpenACC. A Fortran interface is made explicit when you use the module that contains it, as in the line use cublas in the example above. If you look ahead to the actual interface for cublasSswap, you will see that the arrays a and b are declared with the CUDA Fortran device attribute, so they take only device addresses as arguments.

Every library in this document has a function for setting the CUDA stream which the library runs on. Two libraries are currently available from within OpenACC compute regions. In device code, it is designed to return one or a small number of random numbers per thread. The thread’s random generators run independently of each other, and it is usually advised for performance reasons to give each thread a different seed, rather than a different offset.

The predominant usage model for the library functions listed in this document is to call them from CUDA Host code. CUDA Fortran allows some special capabilities in that the compiler is able to recognize the device and managed attribute in resolving generic interfaces.

Device actual arguments can only match the interface’s device dummy arguments; managed actual arguments, by precedence, match managed dummy arguments first, then device dummies, then host. Using the cudafor module, the full set of CUDA functionality is available to programmers for managing CUDA events, streams, synchronization, and asynchronous behaviors.

Further examples are included in chapter Examples. The module interfaces are very similar to the modules used in OpenACC device code, but for CUDA Fortran, each subroutine and function is declared attributes [host,]device , and the subroutines and functions do not need to be marked as acc routine seq.

This adds some negligible but non-zero overhead to the calls. To avoid the implicit getting and setting of the pointer mode with every invocation of a library function do the following:. It is the programmer’s responsibility to properly set the pointer mode when needed.

In the v2 interfaces shown in the chapter 2, these scalar arguments will have the comment! Examples of scalar arguments which do not require setting the pointer mode are increments, extents, and lengths such as incx, incy, n, lda, ldb, and ldc.

The CUDA default pointer mode is that the scalar arguments reside on the host. There are some standard techniques to use, and some non-standard NVIDIA extensions which can make creating working interfaces easier.

This interface calls the C library function directly. You can deal with Fortran’s capitalization issues by putting the properly capitalized C function in the bind C attribute. If the C function expects input arguments passed by value, you can add the value attribute to the dummy declaration as well. A nice feature of Fortran is that the interface can change, but the code at the call site may not have to. The compiler changes the details of the call to fit the interface.

There are two ways to do this:. Here the C name hasn’t changed. The compiler will now accept actual arguments corresponding to idata and odata that are declared REAL. A generic interface is created named cufftExecC2C. If you have problems debugging your generic interface, as a debugging aid you can try calling the specific name, cufftExecR2R in this case, to help diagnose the problem.

A programmer can use it in an interface to instruct the compiler to ignore any combination of the type, kind, and rank during the interface matching process. Now the compiler will ignore both the type and rank F77 could also be sloppy in its handling of array dimensions of idata and odata when matching the call site to the interface.

An unfortunate side-effect is that the interface will now allow integer, logical, and character data for idata and odata. It is up to the implementor to determine if that is acceptable. Of course, if you write a wrapper, a narrow strip of code between the Fortran call and your library function, you are not limited by the simple transormations that a compiler can do, such as those listed here.

A common request is a way for Fortran programmers to take advantage of the thrust library. General information on the compiler options which can be passed to nvfortran can be obtained by typing nvfortran -help. Uppercase file extensions,. F90 or. CUF , for example, may also be used, in which case the program is processed by the preprocessor before being compiled. It handles static and dynamic linking, and platform Linux, Windows differences unobtrusively.

Certain library functionality may require minimum compute capability of 6. Certain library functionality may require a newer or older, for deprecated functions CUDA runtime version. There are currently three separate collections of function entry points which are commonly referred to as the cuBLAS:.

These are also callable from the host and operate on device data. In Fortran terms, these entry points have been changed from subroutines to functions which return status.

Documented interfaces to the traditional BLAS names in the subsequent sections, which contain the comment! The traditional BLAS names are overloaded generic names in the cublas module. For instance, in this interface. The arrays x and y can either both be device arrays, in which case cublasScopy is called via the generic interface, or they can both be host arrays, in which case scopy from the host BLAS library is called. Using CUDA Fortran managed data as actual arguments to scopy poses an interesting case, and calling cublasScopy is chosen by default.

If you wish to call the host library version of scopy with managed data, don’t expose the generic scopy interface at the call site.

Unless a specific kind is provided, in the following interfaces the plain integer type implies integer 4 and the plain real type implies real 4. It allocates hardware resources on the host and device and must be called prior to making any other CUBLAS library calls.

For multi-threaded applications that use the same device from different threads, the recommended programming model is to create one CUBLAS handle per thread and use that CUBLAS handle for the entire life of the thread. This function returns the cuBLAS status string associated with a given status value. This function obtains the pointer mode used by the cuBLAS library. In the cublas module, the pointer mode is set and reset on a call-by-call basis depending on the whether the device attribute is set on scalar actual arguments.

See section 1. This function sets the pointer mode used by the cuBLAS library. When using the cublas module, the pointer mode is set on a call-by-call basis depending on the whether the device attribute is set on scalar actual arguments. This function sets the atomics mode used by the cuBLAS library. Some routines in the cuBLAS library have alternate implementations that use atomics to accumulate results. These alternate implementations may run faster but may also generate results which are not identical from one run to the other.

The default is to not allow atomics in cuBLAS functions. This function sets the math mode used by the cuBLAS library. Some routines in the cuBLAS library allow you to choose the compute precision used to generate results. These alternate approaches may run faster but may also generate different, less accurate results.

This function gets the cuBLAS handle currently in use by a thread. This function copies n elements from a vector x in host memory space to a vector y in GPU memory space. It is assumed that each element requires storage of elemSize bytes. In CUDA Fortran, the type of vector x and y is overloaded to take any data type, but the size of the data type must still be specified in bytes.

This functionality can also be implemented using cudaMemcpy or array assignment statements. This function copies n elements from a vector x in GPU memory space to a vector y in host memory space. This function copies a tile of rows x cols elements from a matrix A in host memory space to a matrix B in GPU memory space. This functionality can also be implemented using cudaMemcpy, cudaMemcpy2D, or array assignment statements. This function copies a tile of rows x cols elements from a matrix A in GPU memory space to a matrix B in host memory space.

This function copies n elements from a vector x in host memory space to a vector y in GPU memory space, asynchronously, on the given CUDA stream. This functionality can also be implemented using cudaMemcpyAsync. This function copies a tile of rows x cols elements from a matrix A in host memory space to a matrix B in GPU memory space, asynchronously using the specified stream. This function copies a tile of rows x cols elements from a matrix A in GPU memory space to a matrix B in host memory space, asynchronously, using the specified stream.

E0 SH11 SH12 1. E0 SH12 SH11 1. E0 , E0 SH22 , 0. Values of 1. E0, E0, or 0. No test for singularity or near-singularity is included in this routine. Such tests must be performed before calling this routine.

 
 

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