What are the most important standards for optimizing algorithms for parallel processing?

2 Choose your parallel model

The second step to optimize your algorithms for parallel processing is to choose your parallel model, which is the abstraction that defines how the processors or cores communicate and coordinate with each other. There are several parallel models, such as shared memory, distributed memory, message passing, and hybrid models. Each model has its own advantages and disadvantages, depending on the characteristics of your problem, such as the size and distribution of the data, the degree of coupling and dependency between the subproblems, and the availability and cost of the hardware resources. You should choose the parallel model that best suits your problem and minimizes the overhead and complexity of parallelization.

4 Implement your parallel code

The fourth step to optimize your algorithms for parallel processing is to implement your parallel code. To do so, you should use the appropriate tools and languages that support your parallel model and platform, such as parallel libraries and frameworks, such as OpenMP, MPI, CUDA, and Spark. These provide high-level abstractions and APIs for parallel programming. Additionally, you should use parallel compilers and interpreters, such as GCC, Clang, and Python to generate and execute parallel code from sequential code or annotations. Finally, for debugging and testing purposes, you can use GDB, Valgrind, and Pytest to help you find and fix errors and bugs in your parallel code.

5 Evaluate your parallel performance

The fifth step to optimize your algorithms for parallel processing is to evaluate your parallel performance, which is the measurement and analysis of your parallel solution. You should use metrics and tools such as speedup, efficiency, and scalability to understand and improve your parallel solution. Speedup is the ratio of the execution time of the sequential solution to the execution time of the parallel solution. Efficiency is the ratio of the speedup to the number of processors or cores. Scalability indicates how adaptable your parallel solution is as the number of processors or cores increases. Profiling and benchmarking tools such as gprof, perf, and TensorFlow Benchmark can help collect and visualize performance data, pinpointing bottlenecks and hotspots in your parallel code.

6 Refine your parallel solution

The sixth and final step to optimize your algorithms for parallel processing is to refine your parallel solution. This involves improving and optimizing your parallel solution based on the feedback and insights from the previous steps. To enhance your parallel performance, you can tune parameters and settings such as the number and size of data chunks, the number and type of threads or processes, and the optimization level or flags. Additionally, you can revise the problem decomposition, parallel model, parallel algorithm, or parallel code by changing the partitioning scheme, communication pattern, data structure, or algorithm. Furthermore, you can explore alternative or new parallel models, libraries, frameworks, compilers, interpreters, or platforms such as switching from shared memory to distributed memory or from Python to C++.

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?