How do you design and implement effective parallel algorithms for biomedical simulations?

1 Parallelism types

The first step to design a parallel algorithm is to identify the type of parallelism that suits your problem. There are two main types of parallelism: data parallelism and task parallelism. Data parallelism means that you split the data into chunks and assign each chunk to a processor, which performs the same operations on it. Task parallelism means that you divide the problem into independent subtasks and assign each subtask to a processor, which performs different operations on it. Depending on the nature and structure of your model, you may use one or both types of parallelism.

2 Parallel models

The second step to design a parallel algorithm is to choose a parallel model that defines how the processors communicate and synchronize with each other. There are three common parallel models: shared memory, distributed memory, and hybrid memory. Shared memory means that all processors access a common memory space, where they can read and write data. Distributed memory means that each processor has its own memory space, where it stores its data and communicates with other processors through messages. Hybrid memory means that you combine both shared and distributed memory, using clusters of shared memory nodes connected by a network.

3 Parallel patterns

The third step to design a parallel algorithm is to apply a parallel pattern that guides the decomposition, coordination, and optimization of your algorithm. A parallel pattern is a reusable template that captures the common aspects of parallel problems and solutions. Some examples of parallel patterns are map, reduce, scan, pipeline, and farm. Map means that you apply a function to each element of a data collection in parallel. Reduce means that you combine the results of applying a function to a data collection in parallel. Scan means that you compute the partial sums of applying a function to a data collection in parallel. Pipeline means that you divide a task into stages and pass the output of one stage to the next stage in parallel. Farm means that you distribute a stream of tasks to a pool of workers in parallel.

4 Parallel libraries

The fourth step to design a parallel algorithm is to use a parallel library that provides the tools and functions to implement your algorithm. A parallel library is a software package that abstracts the details of parallel programming and offers a high-level interface to the user. Some examples of parallel libraries are OpenMP, MPI, CUDA, and TensorFlow. OpenMP is a library that supports shared memory parallelism using directives and runtime functions. MPI is a library that supports distributed memory parallelism using message passing functions. CUDA is a library that supports data parallelism using GPU programming. TensorFlow is a library that supports data parallelism using tensors and graphs.

5 Parallel testing

The fifth step to design a parallel algorithm is to test and debug your algorithm using parallel testing tools and techniques. Parallel testing is the process of verifying the correctness and performance of your algorithm on multiple processors or cores. Some examples of parallel testing tools are GDB, Valgrind, PAPI, and TAU. GDB is a tool that supports parallel debugging using breakpoints and commands. Valgrind is a tool that supports parallel debugging using memory checking and profiling. PAPI is a tool that supports parallel testing using hardware counters and metrics. TAU is a tool that supports parallel testing using instrumentation and visualization.

6 Parallel evaluation

The sixth step to design a parallel algorithm is to evaluate and compare your algorithm using parallel evaluation methods and criteria. Parallel evaluation is the process of measuring the quality and efficiency of your algorithm on multiple processors or cores. Some examples of parallel evaluation methods are speedup, efficiency, scalability, and load balancing. Speedup is the ratio of the execution time of the sequential algorithm to the execution time of the parallel algorithm. Efficiency is the ratio of the speedup to the number of processors or cores. Scalability is the ability of the algorithm to maintain or increase the speedup as the number of processors or cores increases. Load balancing is the degree of uniformity of the work distribution among the processors or cores.

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?