جست‌وجوی استاد و پژوهشگر

• High Performance Computing
• Parallel Computing
• Computer Systems
• +۴ مورد دیگر

Preeti Malakar is an Assistant Professor in the Department of Computer Science and Engineering at the Indian Institute of Technology Kanpur. She leads the Scalable Parallel Computing Laboratory (SParCL) and previously held positions as a postdoc and Assistant Computer Scientist at Argonne National Laboratory (USA) and was a Visiting Affiliate at Lawrence Berkeley National Laboratory (USA). She completed her PhD from the Indian Institute of Science Bangalore under Prof. Vijay Natarajan and Prof. Sathish Vadhiyar. Her research focuses on high-performance computing systems, parallel I/O optimization, in-situ analysis, and scientific visualization. Key areas include job scheduling, communication optimization for MPI applications, machine learning for performance prediction, and adaptive frameworks for large-scale simulations. Her work integrates HPC with climate modeling and extreme weather prediction. Recent publications emphasize job scheduling with SLURM, deep learning for climate applications, in-situ visualization, and auto-tuning of parallel I/O parameters. Her research consistently targets optimization of HPC systems for scientific domains. Awards: Best Paper Award, EduHiPC 2019 TCPP Best Paper Award, HiPC 2009 She actively advises students in HPC research, with alumni pursuing careers at NVIDIA, Microsoft, Apple, and SAP Labs. Her lab (SParCL) hosts internships and winter schools, including the ACM Winter School on HPC at IIT Kanpur.

Torsten Hoeflerمشاهده پروفایل

استاد

• High Performance Computing
• Parallel Programming
• Scalable Networks
• +۲ مورد دیگر

Torsten Hoefler is a Professor of Computer Science at ETH Zurich, a member of Academia Europaea, and a Fellow of the ACM and IEEE. He previously led performance modeling for the Blue Waters supercomputer at the University of Illinois. His work includes key contributions to the Message Passing Interface (MPI) standard. His research focuses on performance-centric system design , spanning scalable networks, parallel programming techniques, and performance modeling for large-scale simulations and artificial intelligence. Core interests include optimizing computing architectures through mathematical models. Hoefler holds a Ph.D. from Indiana University, where he received the Young Alumni Award (2014) and Distinguished Alumni Award (2022). Major Awards: ACM Gordon Bell Prize (2019) IEEE Sidney Fernbach Memorial Award (2022) 6× Best Paper Awards at ACM/IEEE Supercomputing ERC Starting & Consolidator Grants ACM/IEEE Fellowships He chairs MPI working groups and serves on ACM SIGHPC's steering committee since 2013.

Ryan Grantمشاهده پروفایل

استادیار

• Cloud Computing
• High Performance Networks
• Low-level Hardware-Software Interfaces
• +۶ مورد دیگر

Dr. Ryan Grant is an Assistant Professor in the Department of Electrical and Computer Engineering at Queen’s University, Canada. He leads the Computing at Extreme Scale Advanced Research (CAESAR) lab and is affiliated with the Ingenuity Labs Research Institute. His expertise spans cloud computing, high-performance networks, low-level hardware-software interfaces, and energy-efficient supercomputing systems. Dr. Grant holds a PhD from Queen’s University (2012) and previously worked at Sandia National Laboratories (2012–2021), where he contributed to critical supercomputer communication protocols now deployed globally. He has authored over 80 peer-reviewed articles and received prestigious awards including the R&D100 Award and Queen’s University’s 125th Engineering Alumni Award. His research emphasizes advancing Canada’s supercomputing infrastructure to support AI, climate science, and national security applications. Education: PhD in Computer Engineering, Queen’s University (2012) MSc in Computer Engineering, Queen’s University (2005) BSc in Computer Engineering, Queen’s University (2004) Research Interests: Dr. Grant’s work focuses on optimizing supercomputing architectures for extreme-scale systems, with an emphasis on: High-performance networking and MPI communication protocols Power/energy management in HPC systems AI-driven network traffic prediction and resource disaggregation GPU-accelerated computing and cloud infrastructure integration National sovereignty in supercomputing for sensitive applications (e.g., defense, healthcare) Awards & Recognition: R&D100 Award (Oscars of Research) U.S. Defense Programs Awards Public Good Innovator Award Queen’s University 125th Engineering Alumni Award Grants & Labs: Dr. Grant directs the CAESAR lab, one of the world’s leading supercomputing architecture research groups. His work is supported by grants from Canadian and international agencies, focusing on sovereign supercomputing and HPC-AI convergence. Labs/Teams: CAESAR Lab (Queen’s University) Ingenuity Labs Research Institute

Jerónimo Sánchez Garcíaمشاهده پروفایل

پژوهشگر ارشد

• High-Performance Computing
• Quantum Cryptography
• Computer Networking
• +۳ مورد دیگر

Jerónimo Sánchez García is a Research Fellow at Aalborg University's Department of Electronic Systems, part of The Technical Faculty of IT and Design. His work focuses on high-performance computing, quantum-resistant communications, and parallel algorithm development. He contributes to the Horizon Europe-funded QUARC project (2022–2026), advancing post-quantum cryptography applications. Research Interests: Optimizing MPI message matching using optimistic approaches Protocol buffer deserialization in distributed systems Quantum-safe data transmission in data centers Parallel computing techniques for drug discovery Recent Work Trends: His publications (2023–2024) emphasize hardware offloading strategies, resilient communication protocols, and parallelism in both computational science and quantum-resistant infrastructure. Notable contributions include achieving 100 Gbit/s quantum-safe data transfer highlighted in media coverage. Awards: None explicitly listed in the provided materials. Project Contributions: Active participant in QUARC project, presenting at ExaMPI24 and ICTON conferences. Collaborations include work with institutions like the University of Copenhagen and industry partners in high-performance computing. Labs/Teams: Involved in the QUARC research network and interdisciplinary teams focused on exascale computing and quantum security.

Biagio Cosenzaمشاهده پروفایل

دانشیار

• High-performance computing
• Compiler technology
• Software optimization
• +۷ مورد دیگر

Biagio Cosenza is an Associate Professor at the Department of Computer Science, University of Salerno, Italy. He leads research in high-performance computing, compiler technology, and software optimization. Previously, he was a Senior Researcher at TU Berlin (2015-2019) and a Post-Doctoral Researcher at the University of Innsbruck, Austria (2011-2015). His educational background includes: Ph.D. from University of Salerno (2011), supervised by Prof. Vittorio Scarano Dr. Cosenza's research focuses on creating efficient programming models for heterogeneous computing systems. His work spans compiler technology, automatic performance tuning, and energy-efficient computing approaches. He has made significant contributions to SYCL-based programming models and MPI implementations, with emphasis on portability across diverse hardware platforms including GPUs and accelerators. His research often bridges theoretical computer science with practical applications in scientific computing and bioinformatics. His recent publications demonstrate a strong trend toward heterogeneous and distributed computing, with particular emphasis on energy efficiency, performance portability, and SYCL-based programming models. Many papers focus on applications in drug discovery and scientific simulations, showing how his theoretical work translates to real-world scientific problems. Dr. Cosenza has received several prestigious recognitions: Best Paper Award at the 14th BenchCouncil International Symposium (2022) Elevated to Senior Member of the IEEE (2022) Recognized as ACM Senior Member (2022) He leads multiple significant research projects including the PRIN 2022 project "LibreRT" and the EuroHPC project "LIGATE." His work has secured substantial funding from sources including the German Research Foundation (DFG), the Italian Ministry of Education, University and Research, and EuroHPC. He actively mentors students and collaborators on advanced topics in high-performance computing. Dr. Cosenza is a member of the ISIS Lab at the University of Salerno and contributes to open standards through his work with the Khronos Group and SYCL Working Group. His research group develops tools like CELERITY, a C++ SYCL-based programming model for accelerator clusters, and EMPI, an enhanced message passing interface in modern C++.

• Distributed Systems
• Peer-to-Peer Networks
• Parallel Computing
• +۳ مورد دیگر

Cyril RANDRIAMARO is a Lecturer at the University of Picardie Jules Verne (UPJV), affiliated with the Algorithmic and Complexity (ALCO) research domain. His work focuses on distributed systems and parallel computing paradigms. Research Interests: Dr. RANDRIAMARO's expertise spans distributed storage architectures, peer-to-peer (P2P) network optimization, fault-tolerant systems, and parallel algorithm design. Key areas include dynamic data distribution strategies, cluster I/O performance enhancement, and efficient scheduling methods for block-cyclic array redistribution in high-performance computing environments. Publications: His 15 most recent articles (1996-2010) demonstrate a consistent focus on scalable distributed systems. Major themes include P2P storage resilience, parallel computation scheduling, and cluster I/O optimization, reflecting deep engagement with reliability engineering and performance tuning in networked environments. Affiliations: Historical associations include contributions to the Laboratoire de l’Informatique du Parallélisme (LIP), evidenced in his 1998 publication. No awards, student advisories, or part-time status are documented.

Jannek Squarمشاهده پروفایل

پژوهشگر

• High-Performance Computing
• Parallel Computing
• Code Transformation
• +۵ مورد دیگر

Dr. Jannek Squar is a researcher affiliated with the University of Hamburg and the German Climate Computing Centre (DKRZ). His work focuses on high-performance computing (HPC) infrastructure, code transformation, and parallelization techniques. Academic Rank: Researcher Key Projects: i_SSS (Integrated Support System for Sustainability), HLRE-5 Supercomputer Procurement His research spans automatic code transformation using LLVM , parallelization via OpenMP/MPI , and high-performance I/O optimization . Recent publications highlight applications in sustainable agriculture and compiler-assisted HPC performance analysis. He has supervised multiple theses on topics including elastic cloud solutions, MPI communication modeling, and LLVM-based code verification. No scientific awards are explicitly mentioned in the provided text.

• High-Performance Computing
• Parallel I/O Systems
• Scientific Computing
• +۳ مورد دیگر

Venkatram Vishwanath is a Professor in the Department of Computer Science at the University of Illinois at Urbana-Champaign (UIUC), affiliated with the College of Engineering. His research focuses on high-performance computing (HPC), parallel I/O systems, machine learning applications, and scientific computing. He has contributed to advancing scalable data management, performance optimization on supercomputers, and interdisciplinary projects such as the HACC cosmological simulation framework. Collaborations with institutions like Argonne National Laboratory highlight his work in bridging computational science with practical applications. Key areas of expertise include topology-aware data aggregation, GPU performance modeling, and accelerating I/O operations for large-scale simulations. His work on frameworks like vl3 and PIDX has improved parallel data handling and visualization. Awards and recognitions are not explicitly listed in the provided data, but his extensive publication record and leadership in HPC projects reflect significant contributions to the field.

• Parallel Computing
• Java Virtual Machine (JVM) Optimization
• Performance Profiling
• +۵ مورد دیگر

Walter Binder is a Full Professor in the Faculty of Informatics at the Università della Svizzera italiana (USI). He holds a MSc, PhD, and venia docendi from Vienna University of Technology. Previously, he was a senior researcher at the Artificial Intelligence Laboratory, EPFL. His research focuses on program transformations, virtual execution environments, aspect-oriented programming, profiling, and resource management. Education: MSc from Vienna University of Technology PhD from Vienna University of Technology Venia docendi (Habilitation) from Vienna University of Technology Research Interests: His work spans JVM optimization, parallel computing, and dynamic program analysis. Notable contributions include tools like Renaissance (a JVM benchmark suite), S2S (SQL-to-Stream translator), and Akkaprof (profiler for actor-based systems). He explores topics such as thread management, vectorization, and performance profiling in distributed systems. Articles Trends: Recent work emphasizes JVM performance (e.g., Java Vector API, Native Image optimizations), parallel execution policies (NAS benchmarks), and adaptive runtime systems (e.g., MPR framework). He also investigates big data systems (Spark, Node.js) and runtime verification tools. Grants & Labs: Leads projects on JVM profiling, polyglot runtimes, and large-scale program analysis. His lab develops tools like NodeMOP for Node.js and AccStream for stream processing systems.

• High-Performance Computing
• Monte Carlo Methods
• Grid and Distributed Computing
• +۷ مورد دیگر

Vassil Alexandrov is a Professor at the School of Systems Engineering, University of Reading, specializing in high-performance computing, Monte Carlo methods, and grid-based scientific computing. His research integrates fault tolerance, virtual environments, and e-Science infrastructures, with strong collaborations in interdisciplinary projects such as eMinerals and P-GRADE. PhD in Computational Science (inferred) Extensive research in parallel and distributed algorithms His research interests lie at the intersection of computational science and distributed systems. He has pioneered the use of Monte Carlo methods for matrix computations and large-scale environmental modeling, particularly in air pollution simulation. His work extends into immersive virtual environments for medical training, psychological modeling, and collaborative e-learning. He has contributed significantly to fault-tolerant computing using intelligent agents and swarm-array paradigms. The recent publications show a strong trend toward intelligent fault tolerance, agent-based recovery, and immersive virtual environments. His work spans both theoretical numerical methods and practical implementations in grid and cluster computing. The integration of cognition models in fault-tolerant systems reflects a unique interdisciplinary approach. While no formal scientific awards are listed in the provided text, his editorial roles and leadership in major computational science conferences indicate recognition in the field. He has collaborated extensively with researchers across Europe and has been involved in advising and mentoring through collaborative projects like eMinerals and P-GRADE. His work has been supported by e-Science and grid computing initiatives, particularly in the UK. He has contributed to the development of collaborative tools, portals, and workflows for virtual organizations. He has been involved in the development of the eMinerals virtual organization, the P-GRADE grid portal, and immersive virtual environments for education and medical training, reflecting a strong focus on collaborative and user-centered research infrastructures.

Torsten Hoeflerمشاهده پروفایل

استاد

• High-Performance Computing (HPC)
• Machine Learning
• Parallel Computing
• +۲ مورد دیگر

Torsten Hoefler is a Professor at ETH Zurich , renowned for pioneering contributions to High-Performance Computing (HPC) and its application to Artificial Intelligence (AI) . His work on scalable network design, parallel algorithms, and Message Passing Interface (MPI) advancements has revolutionized supercomputing and AI infrastructure. Hoefler chairs key MPI working groups and co-developed foundational concepts like "3D parallelism" that underpin modern AI systems. Hoefler's research spans interconnection networks, congestion avoidance protocols (e.g., Slim Fly, PERCS), and performance modeling. His innovations in nonblocking collective operations (Iallreduce, Iallgather) power distributed deep learning frameworks. He also leads benchmarking and reproducibility initiatives that set global HPC research standards. ACM Prize in Computing (2024) : Recognized for enabling AI's computational scale through HPC breakthroughs ACM Gordon Bell Prize (2019) : For quantum transport simulations mapping transistor heat distribution Hoefler's work impacts millions via large-language model training (e.g., ChatGPT) and reduces data center cooling costs through thermal modeling. He leads teams at ETH Zurich's Scalable Parallel Computing Laboratory , translating theoretical advances into industry-adopted technologies.

Bengisu Elisمشاهده پروفایل

پژوهشگر

• High-Performance Computing (HPC)
• Parallel Computing
• GPU Programming
• +۵ مورد دیگر

Bengisu Elis is a researcher at the Chair of Computer Architecture & Parallel Systems at the Technical University of Munich . Her work focuses on High-Performance Computing (HPC) , GPU programming, and parallel systems. Email: bengisu.elis@tum.de Room: 01.04.037, Garching near Munich Research Interests : Elis specializes in GPU-CPU parallelism , MPI profiling , and performance modeling for HPC systems. Her work addresses challenges in: Optimizing synchronization mechanisms Reducing memory access overhead Enhancing task scheduling Scientific Awards : HPCAsia '24 Best Paper Award Winner Teaching & Advising : Elis has supervised multiple master's theses and internships, including: Jakob Schäffeler (2025) - Comparing GPU Programming Models Tizian Leonhardt (2024) - Low-overhead Sampling Techniques

• High-Performance Computing
• Parallel Computing
• Distributed Systems
• +۶ مورد دیگر

Marc Snir is a Professor at the University of Illinois’s Siebel School of Computing and Data Science. He has led significant research contributions in high-performance parallel computing, including work on the Message Passing Interface (MPI) and IBM’s SP scalable parallel system. As Department Head from 2001–2007, he oversaw the transition to the Siebel Center and expanded the department’s capabilities. He later served as the first director of the Illinois Informatics Institute, chief software architect for the Blue Waters supercomputer, and co-director of the Universal Parallel Computing Research Center (UPCRC). His research focuses on parallel computing systems, fault resilience, and I/O optimizations. He has been recognized with the 2014 Distinguished Alumni Service Award. His work spans exascale computing, distributed systems, and machine learning applications in HPC. He has contributed to projects like Argo (an exascale OS/runtime), Aluminum (a GPU-aware communication library), and LCI (Lightweight Communication Interface). His research emphasizes improving scalability, energy efficiency, and reliability in high-performance systems. He has advised numerous students (names not listed here) and led teams in advancing HPC tools and methodologies. His involvement in initiatives like UPCRC and the Blue Waters project underscores his commitment to bridging theoretical research and practical applications in computing.

• High-Performance Computing
• Parallel Computing
• Distributed Systems
• +۴ مورد دیگر

George Bosilca is a Professor at the University of Tennessee, Knoxville, specializing in high-performance computing and parallel systems. With over two decades of research contributions, he has established himself as a leading expert in task-based runtime systems, distributed computing, and MPI implementations. His research focuses on developing and optimizing task-based runtime systems for extreme-scale computing environments, with particular emphasis on fault tolerance, performance optimization, and scalability. Bosilca's work spans multiple domains including scientific computing, climate modeling, and deep learning applications. He has made significant contributions to the PaRSEC runtime system and has extensively researched MPI optimization techniques for modern HPC architectures. The trend in Bosilca's recent publications demonstrates a strong focus on addressing challenges in exascale computing, including fault tolerance in distributed systems, GPU acceleration for scientific workloads, and energy-efficient computing techniques. His work bridges theoretical computer science with practical implementations for real-world scientific applications across various domains. Bosilca maintains extensive collaborations with leading researchers in the HPC community, most notably with Jack J. Dongarra (103 co-publications), Aurelien Bouteiller (60 co-publications), and Thomas Hérault (54 co-publications). These collaborations have resulted in numerous publications at top-tier conferences including SC, IPDPS, and EuroMPI, as well as in prestigious journals such as IEEE Transactions on Parallel and Distributed Systems and the International Journal of High Performance Computing Applications.

• Computer Vision
• Image Processing
• Lighting Technology
• +۴ مورد دیگر

Géza Várady is an Associate Professor at the University of Pécs (PTE), holding roles such as Vice Dean for Scientific Affairs at the Faculty of Engineering and Information Technology. He has served as Head of the Department of Technical Informatics and has held various academic leadership positions. His career spans over two decades, with research focusing on computer vision, image processing, and lighting technology. He holds a PhD from the Doctoral School of Informatics at the University of Pannonia. Education includes a Master’s degree in Computer Engineering and a PhD in Informatics. He has also held research positions internationally, such as a Leonardo Fellowship at Schefenacker GmbH in Stuttgart, Germany. His research interests include mesopic vision models, color correction systems, 3D depth sensing using monocular cameras, and drone-based applications. He leads the drone research team and has supervised doctoral students in areas like image data correction and 3D modeling. Notable achievements include the 2024 Publication Excellence Award, IBM Faculty Awards for adaptive lighting systems, and the Walsh Weston Award for contributions to lighting science. He actively participates in academic governance, serving on national committees like the Hungarian Academy of Sciences’ Engineering Sciences Committee and the John von Neumann Computer Society. Publications span over 100 articles in journals like Lighting Research and Technology and Technical Gazette , focusing on topics ranging from photogrammetry to autonomous drone control. He has authored textbooks on MPI programming and computer architecture.