DNA 序列比对:为 OpenMP、MPI 和 CUDA/OpenCL 分配任务

Arturo Gonzalez-EscribanoUniversidad de Valladolid, Spain, Diego García-ÁlvarezUniversidad de Valladolid, Spain, Jesús CámaraUniversidad de Valladolid, Spain
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引用次数: 0

摘要

我们介绍了一门完整的并行计算课程的作业。自 2017/2018 学年以来,我们每学年都会提出一个不同的问题,以展示使用不同并行编程模型处理同一计算问题的各种方法。这些问题可以使用 OpenMP 进行共享内存编程,使用 MPI 进行分布式内存编程,使用 CUDA 或 OpenCL 进行 GPU 编程。今年选择的问题是实现多模式 DNA 序列精确配对的暴力解法。该程序搜索长 DNA 序列中多个核苷酸字符串的精确重合点。程序的顺序实现设计得清晰易懂,同时提供了许多并行化和优化的机会。本作业涉及许多学生认为难以在实际场景中应用的关键概念:竞赛条件、还原、集体操作和点对点通信。它还涉及伪随机序列的并行生成问题,以及在发现匹配时通知和停止累加计算的策略。本作业可作为强化基础知识的练习,为学生学习更全面的并行计算概念和结构做好准备。该作业作为计算机工程学位课程三年级并行计算课程的实践作业已成功实施。本系列作业及以前作业的辅助材料均可公开获取。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
DNA sequence alignment: An assignment for OpenMP, MPI, and CUDA/OpenCL
We present an assignment for a full Parallel Computing course. Since 2017/2018, we have proposed a different problem each academic year to illustrate various methodologies for approaching the same computational problem using different parallel programming models. They are designed to be parallelized using shared-memory programming with OpenMP, distributed-memory programming with MPI, and GPU programming with CUDA or OpenCL. The problem chosen for this year implements a brute-force solution for exact DNA sequence alignment of multiple patterns. The program searches for exact coincidences of multiple nucleotide strings in a long DNA sequence. The sequential implementation is designed to be clear and understandable to students while offering many opportunities for parallelization and optimization. This assignment addresses key concepts many students find difficult to apply in practical scenarios: race conditions, reductions, collective operations, and point-to-point communications. It also covers the problem of parallel generation of pseudo-random sequences and strategies to notify and stop speculative computations when matches are found. This assignment serves as an exercise that reinforces basic knowledge and prepares students for more complex parallel computing concepts and structures. It has been successfully implemented as a practical assignment in a Parallel Computing course in the third year of a Computer Engineering degree program. Supporting materials for this and previous assignments in this series are publicly available.
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