Tips and Best Practices


Teaching: 20 min
Exercises: 0 min
  • What best practices should I know before I write my program?

  • Useful tips and best practices.

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Some algorithms are fast in serial, but cannot be made parallel, or are inefficient in parallel. If the algorithm has large serial regions, there is little hope. A different algorithm may be less efficient in serial, but have more parallel regions and less need for communication.

How will I split the work? Will different ranks work on different tasks? Will I split the data between the tasks, and if I do, how should I split it? The way the task is distributed will determine how efficient the resulting program can be, and how well the run time scales with the number of ranks.

It’s easy to make mistakes. Start with a working serial code and continually compare between the parallel and the serial versions. Make changes a section at a time until the program is sufficiently efficient. Don’t spend time parallellising sections that have no effect on performance.

Write tests and make sure they don’t fail when making changes.


You should start with straightforward, easy-to-read, correct code. Optimisation usually makes the code less readable can lead to mistakes.

Once you have a working code only optimise the important parts. Use a profiler to determine where the program spends its time. Spend your time on the same lines of code. Keep everything else nice, clean, and readable.

Especially when running on a large system, it is more important to reduce communication than it is to avoid computation. The processor can run quite a few floating point operations in the time it takes for the network to send a number from one rank to the other.

When communication time starts to become a significant bottleneck, think about what is the earliest time you can send the data and the latest time you need it. Start transfers early and do as much work as possible while the data is moving from one rank to another.

At a slightly lower level, it is also important to optimise the data transfers. Each transfer has a latency, it takes time to set up, to move the data between the ranks and to write it to the buffer. It’s more efficient to pack as much data as possible into each transfer and reduce the number of individual transfers.

Using the MPI functions for collective communication is almost always more efficient. They can be implemented specifically for the machine you’re running on, and may use hardware you don’t have direct control over.

There are multiple implementations of the MPI standard. They are often optimised for a specific set of machines. In an HPC system, it’s worth using one that’s designed for that system.

When performance really matters, post receives early. You can only post the send once you have the data, but you can post a receive when ever you want. This allows the communication device to set up a buffer and prepare to receive the message. It also avoids the other rank waiting for the receive when it already has the data.

Do this within reason. Don’t start all your transfers at the beginning of the program.

Key Points