![\"\"](\"https:\/\/ae.iti.kit.edu\/img\/content\/sm_sanders_5584_rdax_230x218s.jpg\")
<\/figure>\n\n\n\nAbstract<\/strong><\/p>\n\n\n\n Algorithm engineering is a view on the methodology of algorithmic research. At its core is a\u00a0feedback cycle of modeling, design, analysis, implementation, and experimental evaluation\u00a0[Bader, Moret, Sanders,\u00a0Experimental Algorithmics<\/em>, 2002; Sanders,\u00a0Efficient Algorithms<\/em>, 2009]. At each stage, we learn new things about an\u00a0algorithmic problem that drives the development of better solutions. This is inspired by the\u00a0hypothesis-driven cycle of scientific discovery prevalent in the natural sciences [Popper,\u00a0Logik der Forschung<\/em>, 1934].<\/p>\n\n\n\n The invited talk reflects on the role of algorithm engineering with particular focus on parallel\u00a0computing. \u00a0Modeling is particularly important there because no standard model has emerged after more\u00a0than half a century of research on parallel algorithms. For example, MapReduce computations\u00a0have become a popular high-level theoretical model but their connection to the real world is an\u00a0interesting question [Sanders,\u00a0IEEE Conf on Big Data<\/em>, 2020]. On the low-level end of this spectrum, we need models that\u00a0enable extensive algorithm-hardware codesign needed for future energy-efficient AI hardware.<\/p>\n\n\n\n On the design side, it emerges that we do not just need a single algorithm but rather a\u00a0spectrum of solutions selected from a huge design space of possible algorithms. This\u00a0spectrum can then cover a range of inputs and machines. To grasp this on the analysis side,\u00a0we need to take constant factors into account and handle complex models. Implementation\u00a0has moved from a simple transition stage to extensive performance engineering to take\u00a0various kinds of parallelism into account, e.g., bits, SIMD, instructions, threads in hierarchies of\u00a0caches, and multipe compute nodes. This can result in orders of magnitude performance\u00a0differences, often outstripping asymptotic behavior.<\/p>\n\n\n\n Experiments and the entire cycle are now in a revolutionary stage where AI assisted algorithm\u00a0engineering can drive the cycle at unprecedented speed. As human researchers, we rapidly\u00a0have to learn how to control it.<\/p>\n\n\n\n Biography<\/strong><\/p>\n\n\n\n Peter Sanders received his PhD in Computer Science from Universit\u00e4t Karlsruhe in 1996. After a short postdoctoral period at Chalmers University in Gothenburg, Sweden, he joined the Max Planck Institute for Informatics in Saarbr\u00fccken. In 2004, he returned to Karlsruhe as a full professor. He currently leads the Algorithm Engineering Group in the Institute for Theoretical Computer Science at the Karlsruhe Institute of Technology (KIT). He also works part-time for Google.<\/p>\n\n\n\n Professor Sanders has received numerous honors and awards, including the DFG Leibniz Award in 2012 and a 2020 ERC Advanced Grant for the project “Engineering Scalable Algorithms for the Basic Toolbox (ScAlBox)”. He has authored more than 300 publications, primarily in the area of algorithms for large-scale data processing.<\/p>\n\n\n\n His research spans a broad range of topics, including parallel algorithms (such as sorting, SAT solving, data structures, multicore algorithm libraries, load balancing, and communication scheduling), memory hierarchies (caches, disks, and storage servers), graph algorithms (including route planning and graph partitioning), randomized algorithms, compressed data structures, and full-text indexing.<\/p>\n\n\n\n Robert Tarjan (Princeton University)<\/p>\n\n\n\n Abstract<\/strong><\/p>\n\n\n\n This talk will explore whether and by how much operations on data structures can be sped up by using multiple highly unsynchronized threads. Taking advantage of concurrency in this setting is a challenge. The talk will describe work by Siddhartha Jayanti and the speaker on the efficiency of concurrent disjoint set union algorithms, including recent unpublished work that uses new ideas to eliminate the need for randomization. It will also discuss ongoing work with Laxman Dhulipala, Jakub \u0141\u0105cki, and Siddhartha Jayanti on finding maximal matchings, as well as the general question of what we should require in a realistic but theoretically tractable model of highly asynchronous concurrency.<\/p>\n\n\n\n Biography<\/strong><\/p>\n\n\n\n Robert E. Tarjan, the James S. McDonnell Distinguished University Professor of Computer Science, joined Princeton in 1985. He received doctoral and master\u2019s degrees in computer science from Stanford in 1972 and 1971, respectively, after earning a bachelor\u2019s in mathematics from Caltech. His academic experience involved assistant professorships at Cornell and Stanford, and a Miller research fellowship at UC, Berkeley. Professor Tarjan\u2019s extensive involvement in the private sector includes past and continuing fellowship, research and scientific roles at the NEC Research Institute, Princeton; InterTrust Technologies, Sunnyvale, CA; Compaq Computer, Houston, TX; Hewlett-Packard, Palo Alto, CA; and Microsoft, Mountain View, CA. <\/p>\n\n\n\n Professor Tarjan was awarded the ACM Turing Award<\/a> in 1986 for fundamental achievements in the design and analysis of algorithms and data structures. He was the first winner of the Rolf Nevanlinna Prize (now called the Abacus Prize), established in 1982 and awarded every four years for outstanding contributions in mathematical aspects of information sciences by the International Mathematical Union. His other honors include Caltech\u2019s Distinguished Alumni Award in 2010, the 2009 Edelman Award from INFORMS (member of winning H-P team), fellow of the Society for Industrial and Applied Mathematics (2009), and the Blaise Pascal Medal in Mathematics and Computer Science from the European Academy of Sciences in 2004. <\/p>\n\n\n\n Gopal Pandurangan (University of Houston)<\/p>\n\n\n\n Abstract<\/strong><\/p>\n\n\n\n This talk will trace the interesting (hi)story behind the 2026 Dijkstra Prize work, “Distributed Verification and Hardness of Distributed Approximation” [STOC 2011, SICOMP 2012], by Das Sarma, Holzer, Kor, Korman, Nanongkai, Pandurangan, Peleg, and Wattenhofer. The talk will trace the origin of the $\\tilde{\\Omega}(D+\\sqrt{n})$ lower bound, first shown for the fundamental distributed Minimum Spanning Tree (MST) problem, to the discovery that the same bound applies to a wide variety of problems and settings known today. Along the way, it will talk about the impact of this work on distributed algorithms research over the last 15 years. <\/p>\n\n\n\n Biography<\/strong><\/p>\n\n\n\n Gopal Pandurangan is a Moores Professor in the Department of Computer Science at the University of Houston, USA. He received his Ph.D. in computer science from Brown University in 2002. He has held faculty positions at Purdue University and Nanyang Technological University, Singapore, as well as visiting positions at the Indian Institute of Technology at Madras, Brown University, and Rutgers University. His research interests are in the theory and algorithms for distributed computing, networks, quantum computing, machine learning, and big data, and he has published over 150 refereed papers. He is a Fellow of the Institute of Electrical and Electronics Engineers (IEEE), a recipient of the University of Houston Research Excellence Award, and a winner of the ACM PODC 2025 Best Paper Award.<\/p>\n","protected":false},"excerpt":{"rendered":" Parallel Algorithm Engineering Reconsidered Peter Sanders (Karlsruhe Insitute of Technology) Abstract Algorithm engineering is a view on the methodology of algorithmic research. At its core is a\u00a0feedback cycle of modeling, design, analysis, implementation, and experimental evaluation\u00a0[Bader, Moret, Sanders,\u00a0Experimental Algorithmics, 2002; Sanders,\u00a0Efficient Algorithms, 2009]. At each stage, we learn new things about an\u00a0algorithmic problem that drives … Continue reading “Invited Talks”<\/span><\/a><\/p>\n","protected":false},"author":29,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-166","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/www.podc.org\/podc2026\/wp-json\/wp\/v2\/pages\/166","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.podc.org\/podc2026\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/www.podc.org\/podc2026\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/www.podc.org\/podc2026\/wp-json\/wp\/v2\/users\/29"}],"replies":[{"embeddable":true,"href":"https:\/\/www.podc.org\/podc2026\/wp-json\/wp\/v2\/comments?post=166"}],"version-history":[{"count":3,"href":"https:\/\/www.podc.org\/podc2026\/wp-json\/wp\/v2\/pages\/166\/revisions"}],"predecessor-version":[{"id":172,"href":"https:\/\/www.podc.org\/podc2026\/wp-json\/wp\/v2\/pages\/166\/revisions\/172"}],"wp:attachment":[{"href":"https:\/\/www.podc.org\/podc2026\/wp-json\/wp\/v2\/media?parent=166"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}Highly Asynchronous Concurrency in Data Structures<\/h3>\n\n\n\n
![\"\"](\"https:\/\/www.cs.princeton.edu\/sites\/default\/files\/styles\/profile_image\/public\/2024-08\/ret-profile.JPG?h=fd1592e0&itok=OsuasnmC\")
<\/figure>\n\n\n\nThe Omega(D+sqrt(n)) Lower Bound Story of Distributed Algorithms<\/h3>\n\n\n\n
![\"\"](\"https:\/\/www.podc.org\/podc2026\/wp-content\/uploads\/sites\/18\/2026\/06\/gopal.jpg\")
<\/figure>\n\n\n\n