How do you debug and optimize your parallel code in MATLAB and C/C++?

1 Use built-in functions and libraries

One of the easiest ways to parallelize your code in MATLAB and C/C++ is to use the built-in functions and libraries that support parallel computing. For example, in MATLAB, you can use the parfor loop, the spmd block, or the parallel.pool.DataQueue object to distribute your computations across multiple workers. In C/C++, you can use the OpenMP directives, the MPI library, or the CUDA toolkit to exploit the parallelism of your CPU or GPU. These functions and libraries handle most of the low-level details of parallelization, such as data partitioning, synchronization, and communication.

2 Test your code in serial mode first

Before you run your code in parallel mode, you should always test it in serial mode first. This will help you identify and fix any logical errors, memory leaks, or syntax errors in your code. You can use the MATLAB Editor or the MATLAB Debugger to debug your MATLAB code in serial mode. You can also use the MATLAB Code Analyzer or the MATLAB Profiler to check the quality and performance of your code. For C/C++ code, you can use various tools, such as GCC, GDB, Valgrind, or gprof, to debug and profile your code in serial mode.

3 Use parallel debugging tools

Once you are confident that your code works in serial mode, you can switch to parallel mode and use parallel debugging tools to find and fix any parallel-specific errors or issues. For MATLAB code, you can use the Parallel Computing Toolbox to debug your code in parallel mode. You can set breakpoints, inspect variables, and step through your code on multiple workers. You can also use the MATLAB Profiler to measure the execution time and memory usage of your parallel code. For C/C++ code, you can use tools, such as TotalView, DDT, or Allinea MAP, to debug and optimize your parallel code on different platforms and architectures.

4 Avoid unnecessary communication and synchronization

One of the main factors that affect the performance of parallel code is the communication and synchronization overhead. Communication refers to the transfer of data between different processes or threads. Synchronization refers to the coordination of different processes or threads to ensure the correct order of execution. Both communication and synchronization can introduce latency, contention, and overhead to your parallel code. Therefore, you should avoid unnecessary communication and synchronization by using local variables, independent loops, or asynchronous operations whenever possible.

5 Balance the workload among workers

Another important factor that affects the performance of parallel code is the workload balance among workers. Workload balance refers to the distribution of work among different processes or threads. Ideally, you want each worker to have roughly the same amount of work, so that no worker is idle or overloaded. To achieve workload balance, you should use dynamic scheduling, load balancing, or chunking techniques to assign work to workers according to their availability and capacity. You should also avoid using global variables, shared resources, or critical sections that can cause contention or serialization among workers.

6 Use parallel performance tools

Finally, you should use parallel performance tools to measure and improve the efficiency and scalability of your parallel code. Efficiency refers to the ratio of useful work to total work. Scalability refers to the ability of your parallel code to handle increasing problem size or number of workers. To measure and improve the efficiency and scalability of your parallel code, you can use tools, such as the MATLAB Parallel Profiler, the MATLAB Performance Advisor, or the MATLAB Scale Up Advisor for MATLAB code. For C/C++ code, you can use tools, such as Intel VTune Amplifier, Intel Advisor, or NVIDIA Nsight Systems for CPU or GPU code.