This special issue gathers revised and extended versions of selected papers presented at the 13th International Conference on Parallel Processing and Applied Mathematics, PPAM 2019, held September 8–11, 2019 in Bialystok, Poland (http://ppam.info). This conference is a continuation of a series of events started in 1994 when the first PPAM took place in Czestochowa. They have been held every 2 years in different universities in Poland. Thus, the event in Bialystok was an opportunity to celebrate the 25th anniversary of PPAM. The purpose of PPAM conferences is to exchange ideas between researchers involved in parallel and distributed computing, including theory and applications, as well as applied and computational mathematics. PPAM 2019 was organized by the Department of Computer Science of the Czestochowa University of Technology together with the Bialystok University of Technology, under the patronage of the Committee of Informatics of the Polish Academy of Sciences, in technical cooperation with the IEEE Computer Society and IEEE Computational Intelligence Society. The focus of PPAM 2019 was on models, algorithms, and software tools that facilitate efficient and convenient use of modern parallel and distributed computing systems, as well as on large-scale applications, including artificial intelligence and machine learning problems. This event gathered more than 170 participants from 26 countries. The accepted papers were presented at the regular tracks of the PPAM 2019 conference and during the workshops. With each submission evaluated by at least three reviewers, a strict reviewing process resulted in acceptance of 91 contributed papers for publication in the conference proceedings, while approximately 43% of the submissions were rejected. Forty-one papers were selected to be presented in the regular conference track, resulting in an acceptance rate of about 46%. Based on the results of the reviews, selected papers were recommended for a special journal issue. The authors were contacted after the conference and invited to submit revised and extended versions of their works. These new versions were reviewed independently again by at least three reviewers. Finally, three contributions were accepted for publication. They present research on the systematic evaluation and optimization of linear algebra kernels on GPUs (Graphics Processing Units), the efficient adaptation of machine learning and numerical simulation problems to HPC platforms with GPUs and FPGAs (fieldprogrammable gate arrays). The papers in this special issue address the following topics:

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客座编者按：极端异构时代应用性能优化专刊

本特刊收集了在 2019 年 9 月 8 日至 11 日在波兰比亚韦斯托克 (http://ppam.info) 举行的第 13 届并行处理和应用数学国际会议 PPAM 2019 上发表的选定论文的修订和扩展版本。这次会议是 1994 年在琴斯托霍瓦举行的第一次 PPAM 开始的一系列活动的延续。它们每两年在波兰的不同大学举行一次。因此，比亚韦斯托克的活动是庆祝 PPAM 成立 25 周年的机会。PPAM 会议的目的是在涉及并行和分布式计算的研究人员之间交流思想，包括理论和应用，以及应用和计算数学。PPAM 2019 由琴斯托霍瓦理工大学计算机科学系与比亚韦斯托克理工大学共同组织，在波兰科学院信息学委员会的赞助下，与 IEEE 计算机学会和 IEEE Computational 技术合作情报学会。PPAM 2019 的重点是促进现代并行和分布式计算系统的高效便捷使用的模型、算法和软件工具，以及大规模应用，包括人工智能和机器学习问题。本次活动聚集了来自26个国家的170多名参与者。被接受的论文在 PPAM 2019 会议的常规轨道和研讨会期间提交。每份提交至少由三名审稿人评估，严格的审查过程导致 91 篇投稿论文被会议录入接受，而大约 43% 的投稿被拒绝。41篇论文入选常规会议轨道，录取率约46%。根据评审结果，选定的论文被推荐为特刊。会议结束后联系了作者，并邀请他们提交他们作品的修订和扩展版本。这些新版本至少由三位评审员再次独立评审。最后，三篇论文被接受发表。他们介绍了 GPU（图形处理单元）上线性代数内核的系统评估和优化研究，将机器学习和数值模拟问题有效地应用于具有 GPU 和 FPGA（现场可编程门阵列）的 HPC 平台。本期特刊中的论文涉及以下主题：