How do you identify performance bottlenecks in HPC systems?

1 Define your goals

Before you start analyzing and optimizing your HPC system, it's important to define your performance goals and metrics. Ask yourself what you're trying to achieve with your code, how you measure the success of your code, and how you compare your code with other codes or benchmarks. This will help you set realistic and meaningful targets for your performance improvement. Common performance metrics for HPC systems include execution time, speedup, efficiency, throughput, and scalability. Execution time measures the total time it takes to run your code from start to finish. Speedup is the ratio of the execution time of your code on a single processor to the execution time on multiple processors. Efficiency is the ratio of the speedup to the number of processors used. Throughput is the amount of work done per unit of time by your code, while scalability is the ability of your code to maintain or increase its performance as the number of processors increases.

4 Optimize your code

Based on your data analysis, you can decide which parts of your code need to be optimized and how. Optimization is the process of modifying your code to improve its performance, such as reducing the execution time, increasing the speedup, or enhancing the efficiency. This can be done at various levels, such as algorithmic level by choosing a more suitable or efficient algorithm for your problem and implementing parallelism, load balancing, or domain decomposition techniques. You can also optimize at the code level using better data structures, eliminating redundant computations, applying loop transformations, vectorization, or prefetching techniques. Lastly, you can optimize at the hardware level by tuning your code for the specific features or architectures of your HPC system.

5 Test your code

After you have optimized your code, you need to test your code to verify that it works correctly and meets your performance goals. Testing is the process of running your code on your HPC system and checking the results, such as the output, the accuracy, the errors, the warnings, or the performance metrics. Testing can help you ensure the quality and reliability of your code, as well as the validity and reproducibility of your performance improvement. There are many tools available for testing HPC codes, such as Valgrind, PAPI, MPIcheck, or Scalasca.

6 Repeat the process

Performance analysis and optimization is an iterative and continuous process. You may need to repeat the steps above several times until you achieve your desired performance or reach the limits of your HPC system. You may also need to update your performance goals and metrics as your code evolves or your HPC system changes. By following this process, you can identify and eliminate performance bottlenecks in your HPC system and make your code run faster and more efficiently.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?