Practical guide (with code) on performance optimization techniques in Fortran (2024)

Tutorial with code: performance optimization in Fortran.

Paul Norvig


January 2, 2024


I’ve spent a good chunk of my programming life working with Fortran, squeezing out performance where I can. Over the years, I’ve learned that while the language is robust for scientific computing, it also leaves a lot of room for the programmer to optimize. Below I share some of the insights and techniques for compiler optimizations, memory hierarchy, and parallel computing that I’ve picked up along the way.

Understanding Compiler Optimizations in Fortran

In Fortran programming, compiler optimizations are your silent workhorses. They diligently churn through your high-level code and turn it into an efficient machine code that can sometimes make your jaw drop at the speedups. Let’s demystify what goes on under the hood when you tell your Fortran compiler to optimize your program.

To get things rolling, consider a simple example. You’ve got a loop that does some floating-point math:

program optimize_example
implicit none
integer, parameter :: n = 1000000
real(kind=8) :: x(n), y(n), a
integer :: i

a = 2.0
do i = 1, n
y(i) = a*x(i) + y(i)
end do
end program optimize_example

Now, your average compiler will turn this into respectable machine code, but crank up the optimization flags, and it starts to rearrange the furniture to save time. I’ll typically enable optimizations with -O2 or -O3 flags using GNU Fortran or Intel Fortran compilers. Compiling with these flags might look like this:

gfortran -O3 -o optimize_example optimize_example.f90


ifort -O3 -o optimize_example optimize_example.f90

What these optimizations do is leverage a whole toolbox of tricks: loop unrolling, vectorization, constant propagation, and more. For instance, take loop unrolling. It’s like the compiler splits the loop into multiple chunks and processes them simultaneously. It reduces the overhead and often allows for other optimizations to kick in.

But here’s the part where I caution you: more optimization isn’t always better. Sometimes, cranking up to -O3 can lead to subtle bugs if your code isn’t ready for aggressive changes the compiler might impose. Always test thoroughly!

It’s also essential to remember that vectorization is a significant win for numerical computations. Modern processors love crunching an array of data in one go. But your arrays need to be aligned in memory for best performance—something that might be covered in detail in another section about data locality.

! Uncomment the following line when compiling with Intel Fortran for vector report
!$ use omp_lib
!$! dir$ vector always
do i = 1, n
y(i) = a*x(i) + y(i)
end do

One trick I often use to give the compiler a nudge is to insert directives (like the one above) that encourage vectorization of loops. Be warned, though—they’re somewhat like playing with fire. Compiler directives are powerful, and with great power… well, you know the rest.

In addition to vectorization and unrolling, your compiler is doing some crafty work eliminating dead code, reducing function call overhead, and inlining functions where it sees fit. You’ll want to look at the documentation specific to your Fortran compiler to see the full spectrum of tricks it has up its sleeve.

I remember when I was first introduced to the -g flag combined with optimizations. Debugging optimized code can be tricky, but it’s possible. It looks somewhat like this:

gfortran -O2 -g -o optimize_example optimize_example.f90

Let’s not forget that while the Fortran compiler does its magic, you, the developer, need to write ‘optimizable’ code. Avoid overly complex logic, keep your dependencies straightforward, and your data structures cache-friendly.

Ultimately, compiler optimizations in Fortran are about knowing your tools and how to wield them with precision. No one optimization strategy fits all, but with a bit of trial, error, and a lot of testing, you can often coax significant performance gains out of your Fortran codebase.

Memory Hierarchy and Data Locality Strategies

When tackling performance optimization in Fortran, understanding the memory hierarchy and data locality are crucial. Memory hierarchy is the layered structure of various storage types from registers at the top, down to caches (L1, L2, L3), RAM, and disk storage. Each layer has differing access speeds and sizes; knowing how to navigate this pyramid can squeeze significant performance out of your programs.

Here’s a quick rundown: registers are super fast but scarce. Caches are a bit slower but also faster than RAM, which is plentiful but not as swift. And don’t get me started on disk access; that’s like a snail mail in the world of instant messaging.

Using data locality strategies involves arranging data and computations strategically so that data access patterns align with the memory hierarchy. Simply put, keep the data close to where it will be used and keep it there as long as possible.

Let’s look at some code to see what this means in practice. Assume we have a matrix multiplication operation in Fortran:

subroutine matrix_multiply(A, B, C, N)
real, dimension(N,N) :: A
real, dimension(N,N) :: B
real, dimension(N,N) :: C
integer :: i, j, k

do j=1,N
do i=1,N
C(i,j) = 0.0
do k=1,N
C(i,j) = C(i,j) + A(i,k) * B(k,j)
end do
end do
end do
end subroutine matrix_multiply

One way to improve data locality is by restructuring the loop order. Instead of accessing B(k,j) during the innermost loop, which causes more cache misses due to B not being accessed contiguously, we can switch the two inner loops:

do j=1,N
do k=1,N
do i=1,N
C(i,j) = C(i,j) + A(i,k) * B(k,j)
end do
end do
end do

Now, A(i,k) and C(i,j) are accessed row-wise, exploiting spatial locality, because elements are stored contiguously in memory in Fortran (column-major order). This minor change can lead to a major boost in how the cache is utilized, reducing the time waiting on memory fetches.

Block tiling is another method. Instead of working on the entire array, work on sub-blocks that fit into the cache. This could look like this:

integer, parameter :: BLOCKSIZE = 64
! Other code...

do j=jj,min(jj+BLOCKSIZE-1,N)
do k=kk,min(kk+BLOCKSIZE-1,N)
do i=1,N
C(i,j) = C(i,j) + A(i,k) * B(k,j)
end do
end do
end do
end do
end do

In the example above, we’re working on chunks of the array that are likely to fit entirely within a level of cache, keeping our data close and accessible. It’s like organizing a desk before diving into work; everything you need is within arm’s reach.

I can’t overstate the importance of data locality. I’ve seen this type of optimization result in 2x, 5x, even 10x performance improvements due to better cache usage. To put these strategies into practice, start with a clear understanding of your system’s memory hierarchy. Tools like hwloc or lscpu can provide useful insights into cache sizes and geometry.

While there isn’t an exhaustive resource solely dedicated to Fortran and memory optimization, resources like the Intel Fortran Compiler Optimization guide, Fortran Best Practices (, or NVIDIA’s CUDA Fortran Programming Guide give insight into these and other high-performance programming aspects. Also, getting familiar with gfortran options like -ftree-vectorize, which can affect how the compiler handles loops and data layout, can be incredibly beneficial.

Remember, optimize with intention. Don’t just tweak; have a clear rationale behind your approach, as excessive optimization can make your code less readable. Keep it simple, apply these principles, and watch the milliseconds drop off your runtimes.

Parallel Computing with Fortran Coarrays and MPI

As a Fortran enthusiast delving into the world of high-performance computing, I’ve found that leveraging parallel computing is crucial for tackling large-scale computational problems. Here, I’ll share insights on how to use Fortran coarrays and Message Passing Interface (MPI) for parallel programming, focusing on getting beginners started with these tools.

Let’s start with coarrays, a feature introduced in Fortran 2008 that enables a clear and concise parallel programming model within the language itself. Coarrays allow you to write parallel code almost as naturally as you would write your serial Fortran programs.

To demonstrate, consider a simple example that sums an array of numbers across multiple processors. We’ll declare a coarray and use it to perform the sum in parallel:

program sum_coarray
implicit none
integer :: i, num_procs, this_proc, local_sum, total_sum
integer, allocatable :: mynums[:]
integer, dimension[*] :: part_sum

! Retrieve the number of processors and this processor's ID
num_procs = num_images()
this_proc = this_image()

mynums = this_proc  ! Just an example, each processor has its own data

! Calculate local sum
local_sum = sum(mynums)

! Store local sum into the coarray
part_sum[this_proc] = local_sum

! Synchronize to make sure all coarrays are updated
sync all

! Sum the parts on the first processor
if (this_proc == 1) then
total_sum = sum(part_sum)
print *, 'Total sum is ', total_sum
end if

end program sum_coarray

In this example, each processor computes a local sum of an array segment and stores it in its section of the coarray. After a synchronization step, the first processor aggregates these sums to find the total.

But if you need even more power, you’re likely to encounter MPI, the de facto standard for distributed memory parallel programming. MPI is more verbose than coarrays but offers greater control and is widely supported across various platforms.

Below is the same sum operation using MPI:

program sum_mpi
use mpi
implicit none
integer :: i, num_procs, this_proc, ierr, local_sum, total_sum
integer, dimension(1000) :: mynums

! Initialize MPI and get the processor information
call MPI_Init(ierr)
call MPI_Comm_size(MPI_COMM_WORLD, num_procs, ierr)
call MPI_Comm_rank(MPI_COMM_WORLD, this_proc, ierr)

mynums = this_proc  ! Just an example, each processor holds its data

! Calculate local sum
local_sum = sum(mynums)

! Reduce operation to get the total sum on the first processor
call MPI_Reduce(local_sum, total_sum, 1, MPI_INTEGER, MPI_SUM, 0, MPI_COMM_WORLD, ierr)

! First processor prints the total sum
if (this_proc == 0) then
print *, 'Total sum is ', total_sum
end if

! Finalize MPI
call MPI_Finalize(ierr)

end program sum_mpi

In the MPI version, the MPI_Init and MPI_Finalize calls are crucial for setting up and ending the parallel environment. The MPI_Reduce operation gathers local sums from each processor and computes the total sum in one go.

Both coarrays and MPI have their place in the parallel programming toolbox, and sometimes they’re even used together to exploit different parallelism levels. However, remember that Fortran coarray support depends on the compiler and sometimes requires additional configuration.

To get hands-on and explore further, check out notable resources like the MPI standard documentation at or the GCC Fortran compiler’s coarray page for practical examples at

Experimenting with coarrays and MPI can significantly boost the performance of your Fortran applications. Start small, test your code, and don’t hesitate to increase the complexity as you grow more comfortable with parallel constructs. Happy coding!

Algorithmic Optimizations for Fortran Code

When I first started dabbling in Fortran, the languages I’d used previously did a lot of heavy lifting for me. Fortran is different. It’s powerful, and with that power comes a responsibility to wring out every ounce of performance. Let’s talk about algorithmic optimizations that you can apply to your Fortran code, assuming you have a handle on the basics.

One of the simplest yet effective tricks is loop unrolling. It can give you a nice speedup by reducing loop overhead. Here’s what it looks like in practice:

Before unrolling:

do i = 1, n
a(i) = a(i) + b(i)
end do

After unrolling:

do i = 1, n, 4
a(i) = a(i) + b(i)
a(i+1) = a(i+1) + b(i+1)
a(i+2) = a(i+2) + b(i+2)
a(i+3) = a(i+3) + b(i+3)
end do

It’s not always a silver bullet, though. The sweet spot for unrolling (e.g., by how many iterations you unroll) depends on several factors, such as the size of your data and the architecture of the CPU. That’s something you’ll have to experiment with.

Another tactic I use is to handle array operations efficiently. Fortran is built for number crunching, and knowing how to wield array syntax can make or break your program’s performance.

Suppose you’re working with matrices and you need to multiply each element by a scalar. Resist the urge to write loops right away. Here’s a slick way to do it:

A = 2.0 * B

In this line, B is a matrix and A will be a matrix filled with the results. It doesn’t get much simpler than that. It’s not just about brevity—the Fortran compiler optimizes these operations internally.

Algorithms are not just about loops and array operations; understanding your problem domain is crucial. I learned this the hard way when I was trying to optimize a sorting routine. The built-in SORT was handy for small datasets, but it hit performance issues with larger ones. That’s when I switched to a custom implementation of the quicksort algorithm. Here’s a condensed version:

recursive subroutine quicksort(arr, low, high)
integer :: pivot, i, j
real :: temp

if (low < high) then
pivot = partition(arr, low, high)
call quicksort(arr, low, pivot-1)
call quicksort(arr, pivot+1, high)
end if
end subroutine quicksort

The partition function is where the rearrangement magic happens. QuickSort can outperform SORT with the right dataset and when implemented correctly. So, it pays to understand the computational complexity of built-in functions and when it might be beneficial to roll out your own algorithms.

Now, let’s not overlook the concept of ‘temporary arrays’. Fortran might create them under the hood without you noticing, leading to potentially unwanted memory allocations. Especially in the case of array sections, like in the expression A(2:5). So, if you find yourself using a slice of an array multiple times, store it in a temporary variable. This can avoid re-allocating memory and significantly improve performance.

integer, dimension(10) :: A, temp
temp = A(2:5)

These are just a few optimizations from my own toolbox. To truly get the most out of Fortran, I recommend looking at examples from well-established code bases like this one, where you can see how expert-level Fortran is written.

Remember, while these tricks can offer a leg-up, the best optimization is always a better understanding of what your code is really doing. Don’t be afraid to test, measure, and adjust until your compile times—and your patience—are no longer being tested.

Profiling and Benchmarking Techniques in Fortran

Throughout my journey into Fortran optimization, one thing has become abundantly clear: you can’t improve what you don’t measure. I’ve found this to echo true when working with Fortran code – knowing how to profile and benchmark is essential. This is the final stone to turn in your path to mastering performance optimization for Fortran.

Let’s start with profiling. Profiling is as much an art as it is a science. Essentially, it’s about collecting data on the runtime of your program - which parts of the code are taking up most of the time. I use gprof, a tool that comes with the GNU Compiler Collection (GCC). Here’s a quick example to use gprof with a Fortran program:

program main
implicit none
call computationally_intensive()
end program main

subroutine computationally_intensive()
implicit none
! Your complex code here
end subroutine computationally_intensive

Compile the program with the -pg option to enable profiling:

gfortran -pg -o my_program my_program.f90

Run your program as usual, and gprof will generate a file named gmon.out. Then, execute gprof with your binary:

gprof my_program gmon.out > analysis.txt

The output in analysis.txt gives invaluable insights about where the time goes in your code.

Now, for benchmarking. I’m always careful to establish a baseline performance for my code before starting to tinker with it. I often find that the simplest way to do this in Fortran is to write a timing script around the part of the code I am optimizing. The system_clock subroutine is handy for this. Here’s how you could wrap a code block to measure its execution time:

program benchmark_test
implicit none
integer :: start, finish, rate

call system_clock(count_rate = rate)
call system_clock(start)
! Code block to benchmark
call system_clock(finish)

print *, "Time taken (seconds): ", (finish - start) / real(rate)
end program benchmark_test

Remember, the key is consistency – run your benchmarks multiple times to ensure that the results are reliable and then calculate the average.

I’d recommend you store and track the performance of your code over time. It’s made easier by using tools like perf, or even better, setting up continuous benchmarking with your CI/CD pipeline. Essentially, what you’ll be doing is keeping a history of how changes to your code affect its performance.

For Fortran, a really solid resource for both beginners and seasoned programmers is the Fortran-lang website. They have a collection of links to libraries, tools, and further reading to hone your Fortran skills.

Pro-tip: I find myself regularly consulting the SPEC CPU benchmark suite which contains Fortran benchmarks alongside C and C++ ones. This can offer some real-world code to test your profiling and benchmarking chops against.

To sum it all up, profiling and benchmarking aren’t just checkboxes in the optimization process. They embody a continuous cycle of measuring, adjusting, and re-measuring to boost your Fortran code’s speed. Coupled with the other strategies outlined in this article, they empower you with hard data to back the effectiveness of every tweak and tune you apply.

Remember, good performance is not just about the speedups you achieve, it’s also about understanding why those speedups occurred. And as always, practice makes perfect. So grab your tools and start measuring!