Accelerating an Application with OpenACC
This section will detail an incremental approach to accelerating an application using OpenACC. When taking this approach it is beneficial to revisit each step multiple times, checking the results of each step for correctness. Working incrementally will limit the scope of each change for improved productivity and debugging.
OpenACC Directive Syntax
This guide will introduce OpenACC directives incrementally, as they become
useful for the porting process. All OpenACC directives have a common syntax,
however, with the acc sentinel, designating to the compiler that the text
that follows will be OpenACC, a directive, and clauses to that directive, many
of which are optional but provide the compiler with additional information.
In C and C++, these directives take the form of a pragma. The example code
below shows the OpenACC kernels directive without any additional clauses
#pragma acc kernels
In Fortran, the directives take the form of a special comment, as demonstrated below.
!$acc kernels
Some OpenACC directives apply to structured blocks of code, while others are
executable statements. In C and C++ a block of code can be represented by
curly braces ({ and }). In Fortran a block of code will begin with an
OpenACC directive (!$acc kernels) and end with a matching ending directive
(!$acc end kernels).
Porting Cycle
Programmers should take an incremental approach to accelerating applications using OpenACC to ensure correctness. This guide will follow the approach of first assessing application performance, then using OpenACC to parallelize important loops in the code, next optimizing data locality to remove unnecessary data migrations between the host and accelerator, and finally optimizing loops within the code to maximize performance on a given architecture. This approach has been successful in many applications because it prioritizes changes that are likely to provide the greatest returns so that the programmer can quickly and productively achieve the acceleration.
There are two important things to note before detailing each step. First, at times during this process application performance may actually slow down. Developers should not become frustrated if their initial efforts result in a loss of performance. As will be explained later, this is generally the result of implicit data movement between the host and accelerator, which will be optimized as a part of the porting cycle. Second, it is critical that developers check the program results for correctness after each change. Frequent correctness checks will save a lot of debugging effort, since errors can be found and fixed immediately, before they have the chance to compound. Some developers may find it beneficial to use a source version control tool to snapshot the code after each successful change so that any breaking changes can be quickly thrown away and the code returned to a known good state.
Assess Application Performance
Before one can begin to accelerate an application it is important to understand in which routines and loops an application is spending the bulk of its time and why. It is critical to understand the most time-consuming parts of the application to maximize the benefit of acceleration. Amdahl’s Law informs us that the speed-up achievable from running an application on a parallel accelerator will be limited by the remaining serial code. In other words, the application will see the most benefit by accelerating as much of the code as possible and by prioritizing the most time-consuming parts. A variety of tools may be used to identify important parts of the code, including simple application timers.
Parallelize Loops
Once important regions of the code have been identified, OpenACC directives should be used to accelerate these regions on the target device. Parallel loops within the code should be decorated with OpenACC directives to provide OpenACC compilers the information necessary to parallelize the code for the target architecture.
Optimize Data Locality
Because many accelerated architectures, such as CPU + GPU architectures, use distinct memory spaces for the host and device it is necessary for the compiler to manage data in both memories and move the data between the two memories to ensure correct results. Compilers rarely have full knowledge of the application, so they must be cautious in order to ensure correctness, which often involves copying data to and from the accelerator more often than is actually necessary. The programmer can give the compiler additional information about how to manage the memory so that it remains local to the accelerator as long as possible and is only moved between the two memories when absolutely necessary. Programmers will often realize the largest performance gains after optimizing data movement during this step.
Optimize Loops
Compilers will make decisions about how to map the parallelism in the code to the target accelerator based on internal heuristics and the limited knowledge it has about the application. Sometimes additional performance can be gained by providing the compiler with more information so that it can make better decisions on how to map the parallelism to the accelerator. When coming from a traditional CPU architecture to a more parallel architecture, such as a GPU, it may also be necessary to restructure loops to expose additional parallelism for the accelerator or to reduce the frequency of data movement. Frequently code refactoring that was motivated by improving performance on parallel accelerators is beneficial to traditional CPUs as well.
This process is by no means the only way to accelerate using OpenACC, but it has been proven successful in numerous applications. Doing the same steps in different orders may cause both frustration and difficulty debugging, so it’s advisable to perform each step of the process in the order shown above.
Heterogenous Computing Best Practices
Many applications have been written with little or even no parallelism exposed in the code. The applications that do expose parallelism frequently do so in a coarse-grained manner, where a small number of threads or processes execute for a long time and compute a significant amount work each. Modern GPUs and many-core processors, however, are designed to execute fine-grained threads, which are short-lived and execute a minimal amount of work each. These parallel architectures achieve high throughput by trading single-threaded performance in favor of more parallelism. This means that when accelerating an application with OpenACC, which was designed in light of increased hardware parallelism, it may be necessary to refactor the code to favor tightly-nested loops with a significant amount of data reuse. In many cases these same code changes also benefit more traditional CPU architectures as well by improving cache use and vectorization.
OpenACC may be used to accelerate applications on devices that have a discrete memory or that have a memory space that’s shared with the host. Even on devices that utilize a shared memory there is frequently still a hierarchy of a fast, close memory for the accelerator and a larger, slower memory used by the host. For this reason it is important to structure the application code to maximize reuse of arrays regardless of whether the underlying architecture uses discrete or unified memories. When refactoring the code for use with OpenACC it is frequently beneficial to assume a discrete memory, even if the device you are developing on has a unified memory. This forces data locality to be a primary consideration in the refactoring and will ensure that the resulting code exploits hierarchical memories and is portable to a wide range of devices.
Case Study - Jacobi Iteration
Throughout this guide we will use simple applications to demonstrate each step of the acceleration process. The first such application will solve the 2D-Laplace equation with the iterative Jacobi solver. Iterative methods are a common technique to approximate the solution of elliptic PDEs, like the 2D-Laplace equation, within some allowable tolerance. In the case of our example we will perform a simple stencil calculation where each point calculates it value as the mean of its neighbors’ values. The calculation will continue to iterate until either the maximum change in value between two iterations drops below some tolerance level or a maximum number of iterations is reached. For the sake of consistent comparison through the document the examples will always iterate 1000 times. The main iteration loop for both C/C++ and Fortran appears below.
while ( error > tol && iter < iter_max )
{
error = 0.0;
for( int j = 1; j < n-1; j++)
{
for( int i = 1; i < m-1; i++ )
{
Anew[j][i] = 0.25 * ( A[j][i+1] + A[j][i-1]
+ A[j-1][i] + A[j+1][i]);
error = fmax( error, fabs(Anew[j][i] - A[j][i]));
}
}
for( int j = 1; j < n-1; j++)
{
for( int i = 1; i < m-1; i++ )
{
A[j][i] = Anew[j][i];
}
}
if(iter % 100 == 0) printf("%5d, %0.6f\n", iter, error);
iter++;
}
do while ( error .gt. tol .and. iter .lt. iter_max )
error=0.0_fp_kind
do j=1,m-2
do i=1,n-2
Anew(i,j) = 0.25_fp_kind * ( A(i+1,j ) + A(i-1,j ) + &
A(i ,j-1) + A(i ,j+1) )
error = max( error, abs(Anew(i,j)-A(i,j)) )
end do
end do
do j=1,m-2
do i=1,n-2
A(i,j) = Anew(i,j)
end do
end do
if(mod(iter,100).eq.0 ) write(*,'(i5,f10.6)'), iter, error
iter = iter + 1
end do
The outermost loop in each example will be referred to as the convergence
loop, since it loops until the answer has converged by reaching some maximum
error tolerance or number of iterations. Notice that whether or not a loop
iteration occurs depends on the error value of the previous iteration. Also,
the values for each element of A is calculated based on the values of the
previous iteration, known as a data dependency. These two facts mean that this
loop cannot be run in parallel.
The first loop nest within the convergence loop calculates the new value for
each element based on the current values of its neighbors. Notice that it is
necessary to store this new value into a different array. If each iteration
stored the new value back into itself then a data dependency would exist between
the data elements, as the order each element is calculated would affect the
final answer. By storing into a temporary array we ensure that all values are
calculated using the current state of A before A is updated. As a result,
each loop iteration is completely independent of each other iteration. These
loop iterations may safely be run in any order or in parallel and the final
result would be the same. This loop also calculates a maximum error value. The
error value is the difference between the new value and the old. If the maximum
amount of change between two iterations is within some tolerance, the problem
is considered converged and the outer loop will exit.
The second loop nest simply updates the value of A with the values calculated
into Anew. If this is the last iteration of the convergence loop, A will be
the final, converged value. If the problem has not yet converged, then A will
serve as the input for the next iteration. As with the above loop nest, each
iteration of this loop nest is independent of each other and is safe to
parallelize.
In the coming sections we will accelerate this simple application using the method described in this document.