Table of Contents

• Message from the 2022 General Co-ChairsAnne Benoit, Laurent Lefèvre. [doi]
• AI for Datacenter Optimization (ADOPT'22)Stephanie Brink. 1 [doi]
• Towards a GraphBLAS Implementation for GoPascal Costanza, Ibrahim Hur, Timothy G. Mattson. 1-4 [doi]
• Practical Effectiveness of Quantum Annealing for Shift Scheduling ProblemNatsuki Hamada, Kazuhiro Saito, Hideyuki Kawashima. 1-4 [doi]
• Memory-Disaggregated In-Memory Object Store Framework for Big Data ApplicationsRobin Abrahamse, Ákos Hadnagy, Zaid Al-Ars. 1-7 [doi]
• Strategies for Integrating Deep Learning Surrogate Models with HPC Simulation ApplicationsJunqi Yin, Feiyi Wang, Mallikarjun Shankar. 1-10 [doi]
• HCW 2022 Keynote Speaker: Heterogeneous Computing for Scientific Machine LearningLaurent White. 5 [doi]
• HETEROGENEOUS ARCHITECTURE FOR SPARSE DATA PROCESSINGShashank Adavally, Alex Weaver, Pranathi Vasireddy, Krishna Kavi, Gayatri Mehta, Nagendra Gulur. 6-15 [doi]
• Combined Application of Approximate Computing Techniques in DNN Hardware AcceleratorsEnrico Russo, Maurizio Palesi, Davide Patti, Habiba Lahdhiri, Salvatore Monteleone, Giuseppe Ascia, Vincenzo Catania. 16-23 [doi]
• Highly Efficient Alltoall and Alltoallv Communication Algorithms for GPU SystemsChen-Chun Chen, Kawthar Shafie Khorassani, Quentin G. Anthony, Aamir Shafi, Hari Subramoni, Dhabaleswar K. Panda 0001. 24-33 [doi]
• On Energy Nonproportionality of CPUs and GPUsRavi Reddy Manumachu, Alexey L. Lastovetsky. 34-44 [doi]
• Implementating Spatio-Temporal Graph Convolutional Networks on Graphcore IPUsJohannes Moe, Konstantin Pogorelov, Daniel Thilo Schroeder, Johannes Langguth. 45-54 [doi]
• The Best of Many Worlds: Scheduling Machine Learning Inference on CPU-GPU Integrated ArchitecturesGiorgos Vasiliadis, Rafail Tsirbas, Sotiris Ioannidis. 55-64 [doi]
• 29th Reconfigurable Architectures Workshop (RAW 2022)Jürgen Becker 0001, Lana Josipovic, Viktor K. Prasanna, Marco D. Santambrogio, Ramachandran Vaidyanathan. 65-66 [doi]
• RAW 2022 Keynote Speaker 1: Using FPGAs in datacenters and the cloudGustavo Alonso. 67 [doi]
• RAW 2022 Keynote Speaker 1: Using FPGAs in datacenters and the cloudGustavo Alonso. 68 [doi]
• Online Learning RTL Synthesis for Automated Design Space ExplorationDaniele Paletti, Francesco Peverelli, Davide Conficconi. 69-76 [doi]
• Machine Learning Aided Hardware Resource Estimation for FPGA DNN ImplementationsDana Diaconu, Lucian Petrica, Michaela Blott, Miriam Leeser. 77-83 [doi]
• DECISION: Distributing OpenVX Applications on CPUs, GPUs and FPGAs using OpenCLLester Kalms, Tim Haering, Diana Goehringer. 84-91 [doi]
• A Hybrid Approach combining ANN-based and Conventional Demapping in Communication for Efficient FPGA-ImplementationJonas Ney, Bilal Hammoud, Norbert Wehn. 92-95 [doi]
• Optimal Schedules for High-Level Programming Environments on FPGAs with Constraint ProgrammingPascal Jungblut, Dieter Kranzlmüller. 96-99 [doi]
• Optimization of Compiler-Generated OpenCL CNN Kernels and Runtime for FPGAsSeung-Hun Chung, Tarek S. Abdelrahman. 100-103 [doi]
• On How to Push Efficient Medical Semantic Segmentation to the Edge: the SENECA approachRaffaele Berzoini, Eleonora D'Arnese, Davide Conficconi. 104-111 [doi]
• Exploiting High-Bandwidth Memory for FPGA-Acceleration of Inference on Sum-Product NetworksLukas Weber, Johannes Wirth, Lukas Sommer, Andreas Koch 0001. 112-119 [doi]
• 64: A Hardware Operating System for Modern Platform FPGAs with 64-Bit SupportLennart Clausing, Marco Platzner. 120-127 [doi]
• An FPGA-based IP Core Subscription-Oriented Fog Computing PlatformTze Hon Tan, Chia Yee Ooi, Muhammad N. Marsono. 128-131 [doi]
• A SHA-512 Hardware Implementation Based on Block RAM Storage StructureMingyuan Yang, Yemeng Zhang, Bohan Yang 0004, Hanning Wang, Shouyi Yin, Shaojun Wei, Leibo Liu. 132-135 [doi]
• Fast Genome Analysis Leveraging Exact String MatchingBeatrice Branchini, Sofia Breschi, Alberto Zeni, Marco D. Santambrogio. 136-139 [doi]
• Building scalable indexes that can be efficiently queriedChristina Boucher. 142 [doi]
• HiCOMB 2022 Invited Speaker: Pandemic-scale PhylogeneticsYatish Turakhia. 143 [doi]
• Optimizing the Accuracy of Randomized Embedding for Sequence AlignmentYiqing Yan, Nimisha Chaturvedi, Raja Appuswamy. 144-151 [doi]
• On Using Consistency Consistently in Multiple Sequence AlignmentsMario João Jr., Alexandre da Costa Sena, Vinod E. F. Rebello. 152-161 [doi]
• Algorithmic Improvement and GPU Acceleration of the GenASM AlgorithmJoël Lindegger, Damla Senol Cali, Mohammed Alser, Juan Gómez-Luna, Onur Mutlu. 162 [doi]
• High-throughput Pairwise Alignment with the Wavefront Algorithm using Processing-in-MemorySafaa Diab, Amir Nassereldine, Mohammed Alser, Juan Gómez-Luna, Onur Mutlu, Izzat El Hajj. 163 [doi]
• Sequre: a high-performance framework for rapid development of secure bioinformatics pipelinesHaris Smajlovic, Ariya Shajii, Bonnie Berger, Hyunghoon Cho, Ibrahim Numanagic. 164-165 [doi]
• Scalable and Extensible Robinson-Foulds for Comparative PhylogeneticsAlvin Chon, Pawel Górecki 0001, Oliver Eulenstein, Xiaoqiu Huang 0001, Ali Jannesari 0001. 166-175 [doi]
• Accelerating Deep Learning based Identification of Chromatin Accessibility from noisy ATAC-seq DataNarendra Chaudhary, Sanchit Misra, Dhiraj D. Kalamkar, Alexander Heinecke, Evangelos Georganas, Barukh Ziv, Menachem Adelman, Bharat Kaul. 176-185 [doi]
• Graph Convolutional Neural Networks for Alzheimer's Classification with Transfer Learning and HPC MethodsAnoop Kumar, Vibha Balaji, M. A. Chandrashekar, Ambedkar Dukkipati, Sathish Vadhiyar. 186-195 [doi]
• Accelerated LD-based selective sweep detection using GPUs and FPGAsReinout Corts, Niek Sterenborg, Nikolaos Alachiotis 0001. 196-205 [doi]
• Proteome-scale Deployment of Protein Structure Prediction Workflows on the Summit SupercomputerMu Gao, Mark Coletti, Russell B. Davidson, Ryan Prout, Subil Abraham, Benjamín Hernández, Ada Sedova. 206-215 [doi]
• Reproducibility of Bioinformatics ToolsPelin Icer Baykal, Niko Beerenwinkel, Serghei Mangul. 216 [doi]
• TAMPA: interpretable analysis and visualization of metagenomics-based taxon abundance profilesVaruni Sarwal, Serghei Mangul, David Koslicki. 217 [doi]
• GrAPL 2022 Keynote Speaker: GraphBLAS Beyond Simple GraphsTim Mattson. 220 [doi]
• High-Performance GraphBLAS Backend Prototype for NEC SX-Aurora TSUBASAIlya V. Afanasyev, Kazuhiko Komatsu, Dmitry I. Lichmanov, Vadim V. Voevodin, Hiroaki Kobayashi. 221-229 [doi]
• Nonblocking execution in GraphBLASAristeidis Mastoras, Sotiris Anagnostidis, Albert-Jan Nicholas Yzelman. 230-233 [doi]
• GraphBLAS: C++ Iterators for Sparse MatricesBenjamin Brock, Scott McMillan, Aydin Buluç, Timothy G. Mattson, José E. Moreira. 238-246 [doi]
• Temporal Correlation of Internet Observatories and OutpostsJeremy Kepner, Michael Jones 0001, Daniel Andersen, Aydin Buluç, Chansup Byun, kc claffy, Timothy Davis, William Arcand, Jonathan Bernays, David Bestor, William Bergeron, Vijay Gadepally, Daniel Grant, Micheal Houle, Matthew Hubbell, Hayden Jananthan, Anna Klein, Chad R. Meiners, Lauren Milechin, Andrew Morris, Julie Mullen, Sandeep Pisharody, Andrew Prout, Albert Reuther, Antonio Rosa, Siddharth Samsi, Doug Stetson, Charles Yee, Peter Michaleas. 247-254 [doi]
• Interactive Visualization of Protein RINs using NetworKit in the CloudEugenio Angriman, Fabian Brandt-Tumescheit, Leon Franke, Alexander van der Grinten, Henning Meyerhenke. 255-264 [doi]
• An Efficient Parallel Implementation of a Perfect Hashing Method for HypergraphsSomesh Singh 0001, Bora Uçar. 265-274 [doi]
• NWHy: A Framework for Hypergraph Analytics: Representations, Data structures, and AlgorithmsXu T. Liu, Jesun Firoz, Assefaw H. Gebremedhin, Andrew Lumsdaine. 275-284 [doi]
• Parallel Algorithms for Adding a Collection of Sparse MatricesMd Taufique Hussain, Guttu Sai Abhishek, Aydin Buluç, Ariful Azad. 285-294 [doi]
• Multi-View Learning for Parallelism Discovery of Sequential ProgramsLe Chen, Quazi Ishtiaque Mahmud, Ali Jannesari 0001. 295-303 [doi]
• Families of Butterfly Counting Algorithms for Bipartite GraphsJay A. Acosta, Tze Meng Low, Devangi N. Parikh. 304-313 [doi]
• Essentials of Parallel Graph AnalyticsMuhammad Osama, Serban D. Porumbescu, John D. Owens. 314-317 [doi]
• "Crosscutting Themes in Computer Science: Where Does PDC Education Fit?"Rajendra K. Raj. 320 [doi]
• Introducing Parallel Computing in a Second CS CourseTia Newhall, Kevin C. Webb 0001, Vasanta Chaganti, Andrew Danner. 321-329 [doi]
• Feedback from a data center for education at CentraleSupélec engineering schoolJérémy Fix, Stéphane Vialle, Rémi Hellequin, Claudine Mercier, Patrick P. Mercier, Jean-Baptiste Tavernier. 330-337 [doi]
• Teaching High-Performance Computing in Developing Countries: A Case Study in Mexican UniversitiesJoel Antonio Trejo-Sánchez, Francisco Javier Hernández-López, Miguel Ángel Uh Zapata, José Luis López-Martínez, Daniel Fajardo-Delgado, Julio Cesar Ramírez Pacheco. 338-345 [doi]
• A Research-Based Course Module to Study Non-determinism in High Performance ApplicationsPatrick Bell, Kae Suarez, Barbara Fossum, Dylan Chapp, Sanjukta Bhowmick, Michela Taufer. 346-353 [doi]
• Teaching Heterogeneous Computing Using DPC++Joel Fuentes, Daniel López, Sebastián González. 354-360 [doi]
• Peachy Parallel Assignments (EduPar 2022)H. Martin Bücker, Henri Casanova, Rafael Ferreira da Silva, Alice Lasserre, Derrick Luyen, Raymond Namyst, Johannes Schoder, Pierre-André Wacrenier, David P. Bunde. 361-368 [doi]
• 12th IEEE International Workshop on Accelerators and Hybrid Emerging SystemsLena Oden. 369-370 [doi]
• AsHES 2022 Keynote Speaker: The Modular Supercomputing Architecture (MSA)Estela Suarez. 371 [doi]
• Performance Analysis of Parallel FFT on Large Multi-GPU SystemsAlan Ayala, Stan Tomov, Miroslav Stoyanov, Azzam Haidar, Jack J. Dongarra. 372-381 [doi]
• Heterogeneous GPU and FPGA computing: a VexCL case-studyTristan Laan, Ana Lucia Varbanescu. 382-390 [doi]
• COMPOFF: A Compiler Cost model using Machine Learning to predict the Cost of OpenMP OffloadingAlok Mishra, Smeet Chheda, Carlos Soto, Abid Muslim Malik, Meifeng Lin, Barbara M. Chapman. 391-400 [doi]
• A Novel Set of Directives for Multi-device Programming with OpenMPRaul Torres, Roger Ferrer, Xavier Teruel. 401-410 [doi]
• APDCM 2022 Keynote Talk: Solving QUBOs on Digital and Quantum ComputersThorsten Koch, Daniel Rehfeldt, Yuji Shinano. 413 [doi]
• APC-SCA: A Fully-Parallel Annealing Algorithm with Autonomous Pinning Effect ControlDaiki Okonogi, Satoru Jimbo, Kota Ando, Thiem Van Chu, Jaehoon Yu, Masato Motomura, Kazushi Kawamura. 414-420 [doi]
• Graph-theoretic Formulation of QUBO for Scalable Local Search on GPUsRyota Yasudo, Koji Nakano, Yasuaki Ito, Yuya Kawamata, Ryota Katsuki, Shiro Ozaki, Takashi Yazane, Kenichiro Hamano. 425-434 [doi]
• Performance Evaluations of Noisy Approximate Quantum Fourier ArithmeticRobert Basili, Wenyang Qian, Shuo Tang, Austin Castellino, Mary Eshaghian-Wilner, Ashfaq Khokhar, Glenn R. Luecke, James P. Vary. 435-444 [doi]
• Performance Evaluation of Data Transfer API for Rank Level Approximate Computing on HPC SystemsYoshiyuki Morie, Yasutaka Wada, Ryohei Kobayashi, Ryuichi Sakamoto. 445-448 [doi]
• Arm meets Cloud: A Case Study of MPI Library Performance on AWS Arm-based HPC Cloud with Elastic Fabric AdapterShulei Xu, Aamir Shafi, Hari Subramoni, Dhabaleswar K. Panda 0001. 449-456 [doi]
• Aspect-Oriented Programming based building block platform to construct Domain-Specific Language for HPC applicationOsamu Ishimura, Yoshihide Yoshimoto. 457-466 [doi]
• Optimizing Non-commutative Allreduce Over Virtualized, Migratable MPI RanksSam White, Laxmikant V. Kalé. 467-475 [doi]
• Modeling Memory Contention between Communications and Computations in Distributed HPC SystemsAlexandre Denis 0001, Emmanuel Jeannot, Philippe Swartvagher. 476-485 [doi]
• Fully Dynamic Line Maintenance by Hybrid Programmable MatterNooshin Nokhanji, Paola Flocchini, Nicola Santoro. 486-495 [doi]
• Integer Sum Reduction with OpenMP on an AMD MI100 GPUZheming Jin, Jeffrey S. Vetter. 496-499 [doi]
• Optimal Triangulation on the High Bandwidth Memory ModelKoji Nakano, Victor Poupet. 500-507 [doi]
• Towards Java-based HPC using the MVAPICH2 Library: Early ExperiencesKinan Al-Attar, Aamir Shafi, Hari Subramoni, Dhabaleswar K. Panda 0001. 510-519 [doi]
• mpisee: MPI Profiling for Communication and Communicator StructureIoannis Vardas, Sascha Hunold, Jordy I. Ajanohoun, Jesper Larsson Träff. 520-529 [doi]
• An On-the-Fly Method to Exchange Vector Clocks in Distributed-Memory ProgramsSimon Schwitanski, Felix Tomski, Joachim Protze, Christian Terboven, Matthias S. Müller. 530-540 [doi]
• Automatic Parallelization of Programs via Software Stream RewritingTao Tao, David A. Plaisted. 541-551 [doi]
• Decentralized in-order execution of a sequential task-based code for shared-memory architecturesCharly Castes, Emmanuel Agullo, Olivier Aumage, Emmanuelle Saillard. 552-561 [doi]
• Evaluating Unified Memory Performance in HIPZheming Jin, Jeffrey S. Vetter. 562-568 [doi]
• Improving Scalability with GPU-Aware Asynchronous TasksJaemin Choi, David F. Richards, Laxmikant V. Kalé. 569-578 [doi]
• A Customizable Lightweight STM for Irregular Algorithms on GPUShayan Manoochehri, Patrick Cristofaro, Dhrubajyoti Goswami. 579-587 [doi]
• Concurrent CPU-GPU Task Programming using Modern C++Tsung-Wei Huang, Yibo Lint. 588-597 [doi]
• International Workshop on Quantum Classical Cooperative Computing (QCCC 2022)Ang Li, Qiang Guan. 598 [doi]
• QCCC 2022 Keynote Talk: Hybrid Quantum / Classical Algorithms for Machine LearningNathan Wiebe. 599 [doi]
• Methods and Results for Quantum Optimal Pulse Control on Superconducting Qubit SystemsElisha Siddiqui Matekole, Yao-Lung L. Fang, Meifeng Lin. 600-606 [doi]
• Locality-aware Qubit Routing for the Grid ArchitectureAvah Banerjee, Xin Liang, R. Tohid. 607-613 [doi]
• SQCC: Smart Quantum Circuit CuttingBetis Baheri, Qiang Guan, Shuai Xu, Vipin Chaudhary. 614-615 [doi]
• Improving Variational Quantum Algorithms performance through Weighted Quantum EnsemblesSamuel Alexander Stein, Nathan Wiebe, James Ang, Ang Li. 616-617 [doi]
• Benchmarking Quantum Processor Performance through Quantum Distance Metrics Over An Algorithm SuiteSamuel Alexander Stein, Nathan Wiebe, James Ang, Ang Li. 618-624 [doi]
• The First International Workshop on Coarse-Grained Reconfigurable Architectures for High-Performance Computing (CGRA4HPC)Artur Podobas, Kentaro Sano, Jason Anderson. 625-626 [doi]
• (CGRA4HPC) 2022 Invited Speaker: Pushing the Boundaries of HPC with the Integration of AIRaghu Prabhakar. 627 [doi]
• CGRA4HPC 2022 Invited Speaker: Mapping ML to the AMD/Xilinx AIE-ML architectureElliott Delaye. 628 [doi]
• CGRA4HPC 2022 Invited Speaker: Dual-scale reconfigurable arrays for ML InferenceMartin Snelgrove. 629 [doi]
• CGRA4HPC 2022 Invited Speaker: Practical, scalable, and easy-to-use CGRA for HPCIlan Tayari. 630 [doi]
• An Architecture- Independent CGRA Compiler enabling OpenMP ApplicationsTakuya Kojima, Boma A. Adhi, Carlos Cortes, Yiyu Tan, Kentaro Sano. 631-638 [doi]
• Exploration Framework for Synthesizable CGRAs Targeting HPC: Initial Design and EvaluationBoma A. Adhi, Carlos Cortes, Yiyu Tan, Takuya Kojima, Artur Podobas, Kentaro Sano. 639-646 [doi]
• An Analysis of Mapping Polybench Kernels to HPC CGRAsMarkus Weinhardt. 647-654 [doi]
• Elastic Multi-Context CGRAsOmar Ragheb, Tianyi Yu, Rami Beidas, Jason Helge Anderson. 655-662 [doi]
• Accelerating SLIDE: Exploiting Sparsity on Accelerator ArchitecturesSho Ko, Alexander Rucker, Yaqi Zhang 0001, Paul Mure, Kunle Olukotun. 663-670 [doi]
• A Coarse Grained Reconfigurable Architecture for SHA-2 AccelerationHoai Luan Pham, Thi Hong Tran, Le Vu Trung Duong, Yasuhiko Nakashima. 671-678 [doi]
• Twenty Years of Automated Methods for Mapping Applications on CGRAKevin J. M. Martin. 679-686 [doi]
• When and How to Retrain Machine Learning-based Cloud Management SystemsLidia Kidane, Paul Townend, Thijs Metsch, Erik Elmroth. 688-698 [doi]
• Scalable Data Parallel Distributed Training for Graph Neural NetworksSohei Koyama, Osamu Tatebe. 699-707 [doi]
• The MIT Supercloud Workload Classification ChallengeBenny J. Tang, Qiqi Chen, Matthew L. Weiss, Nathan C. Frey, Joseph McDonald, David Bestor, Charles Yee, William Arcand, William Bergeron, Chansup Byun, Daniel Edelman, Michael Houle, Matthew Hubbell, Michael Jones 0001, Jeremy Kepner, Anna Klein, Adam Michaleas, Peter Michaleas, Lauren Milechin, Julia S. Mullen, Andrew Prout, Albert Reuther, Antonio Rosa, Andrew Bowne, Lindsey McEvoy, Baolin Li, Devesh Tiwari, Vijay Gadepally, Siddharth Samsi. 708-714 [doi]
• Loss Curve Approximations for Fast Neural Architecture Ranking & Training Elasticity EstimationDan Zhao, Nathan C. Frey, Vijay Gadepally, Siddharth Samsi. 715-723 [doi]
• Characterizing Multi-Instance GPU for Machine Learning WorkloadsBaolin Li, Vijay Gadepally, Siddharth Samsi, Devesh Tiwari. 724-731 [doi]
• Energy-aware neural architecture selection and hyperparameter optimizationNathan C. Frey, Dan Zhao, Simon Axelrod, Michael Jones 0001, David Bestor, Vijay Gadepally, Rafael Gómez-Bombarelli, Siddharth Samsi. 732-741 [doi]
• A Green(er) World for A.IDan Zhao, Nathan C. Frey, Joseph McDonald, Matthew Hubbell, David Bestor, Michael Jones 0001, Andrew Prout, Vijay Gadepally, Siddharth Samsi. 742-750 [doi]
• PDCO 2022 Keynote Talk: Performance and Energy models for modern HPC serversGeorges Da Costa. 753 [doi]
• Exact k-way sparse matrix partitioningEngelina L. Jenneskens, Rob H. Bisseling. 754-763 [doi]
• A Family of Fast Parallel Greedy Algorithms for the Steiner Forest ProblemLaleh Ghalami, Daniel Grosu. 764-773 [doi]
• Parallel Minimum Spanning Tree Algorithms via Lattice Linear Predicate DetectionDavid R. Alves, Vijay K. Garg. 774-782 [doi]
• A Local Search for Automatic Parameterization of Distributed Tree Search AlgorithmsTiago Carneiro 0002, Loizos Koutsantonis, Nouredine Melab, Emmanuel Kieffer, Pascal Bouvry. 783-789 [doi]
• Parallel Bayesian Optimization for Optimal Scheduling of Underground Pumped Hydro-Energy Storage SystemsMaxime Gobert 0002, Jan Gmys, Jean-François Toubeau, Nouredine Melab, Daniel Tuyttens, François Vallée. 790-797 [doi]
• A Parallel Novelty Search Metaheuristic Applied to a Wildfire Prediction SystemJan Strappa, Paola Caymes-Scutari, Germán Bianchini. 798-806 [doi]
• On Parallel or Distributed Asynchronous Iterations with Unbounded Delays and Possible Out of Order Messages or Flexible Communication for Convex Optimization Problems and Machine LearningDidier El Baz. 807-813 [doi]
• Message from the PDSEC-22 Workshop ChairsSabine Roller, Peter Strazdins, Raphaël Couturier, Neda Ebrahimi Pour, Suzanne Michelle Shontz, Thomas Rauber, Gudula Rünger, Laurence T. Yang. 816-817 [doi]
• PLSSVM: A (multi-)GPGPU-accelerated Least Squares Support Vector MachineAlexander Van Craen, Marcel Breyer, Dirk Pflüger. 818-827 [doi]
• Least Squares on GPUs in Multiple Double PrecisionJan Verschelde. 828-837 [doi]
• A Simple, Fast, and GPU-friendly Steiner-Tree HeuristicAlex Fallin, Aarti Kothari, Jiayuan He 0003, Christopher Yanez, Keshav Pingali, Rajit Manohar, Martin Burtscher. 838-847 [doi]
• Performance Evaluation of a Supercomputer Based on AMD Rome and Intel Cascade Lake ProcessorsSubhash Saini, John Baron, Johnny Chang, Robert Hood, Haoqiang Jin. 848-859 [doi]
• A Scalable Parallel Partition Tridiagonal Solver for Many-Core and Low B/F ProcessorsTatsuya Mitsuda, Kenji Ono. 860-869 [doi]
• OMB-Py: Python Micro-Benchmarks for Evaluating Performance of MPI Libraries on HPC SystemsNawras Alnaasan, Arpan Jain, Aamir Shafi, Hari Subramoni, Dhabaleswar K. Panda 0001. 870-879 [doi]
• Machine Learning for CUDA+MPI Design RulesCarl Pearson, Aurya Javeed, Karen D. Devine. 880-889 [doi]
• Using Performance Attributes for Managing Heterogeneous Memory in HPC ApplicationsBrice Goglin, Andrès Rubio Proaño. 890-899 [doi]
• Synchronous parallel multisplitting method with convergence acceleration using a local Krylov-based minimization for solving linear systemsMédane A. Tchakorom, Raphaël Couturier, Jean-Claude Charr. 900-906 [doi]
• MultiGrid on FPGA Using Data Parallel C++Christopher M. Siefert, Stephen L. Olivier, Gwendolyn Voskuilen, Jeffrey Young. 907-910 [doi]
• 17th IEEE International Workshop on Automatic Performance Tuning (iWAPT2022)Che-Rung Lee, Satoshi Ohshima. 911-912 [doi]
• 2 M: Learning Quantitative Performance of Latency-Sensitive CodeArun V. Sathanur, Nathan R. Tallent, Patrick Konsor, Ken Koyanagi, Ryan McLaughlin, Joseph Olivas, Michael Chynoweth. 913-923 [doi]
• Benchmarking the Linear Algebra Awareness of TensorFlow and PyTorchAravind Sankaran, Navid Akbari Alashti, Christos Psarras, Paolo Bientinesi. 924-933 [doi]
• Automated selection of build configuration based on machine learningReo Furuhata, Minglu Zhao, Keichi Takahashi, Yoichi Shimomura, Hiroyuki Takizawa. 934-941 [doi]
• A Cost Model for Compilers Based on Transfer LearningYuta Sasaki, Keichi Takahashi, Yoichi Shimomura, Hiroyuki Takizawa. 942-951 [doi]
• Modeling pre-Exascale AMR Parallel I/O Workloads via Proxy ApplicationsWilliam F. Godoy, Jenna Delozier, Gregory R. Watson. 952-961 [doi]
• Smoothing on Dynamic Concurrency ThrottlingJanaina Schwarzrock, Hiago Mayk G. de A. Rocha, Arthur Francisco Lorenzon, Antonio Carlos Schneider Beck. 962-971 [doi]
• Analyzing Search Techniques for Autotuning Image-based GPU Kernels: The Impact of Sample SizesJacob O. Tørring, Anne C. Elster. 972-981 [doi]
• Don't Miss the Train: A Case for Systems Research into Training on the EdgePrashanthi S. K, Aakash Khochare, Sai Anuroop Kesanapalli, Rahul Atul Bhope, Yogesh Simmhan. 985-986 [doi]
• Litener: An Accelerator-Enabled Lightweight Container for Edge ComputingRyan Dyson, Carlos Reaño. 987-994 [doi]
• Efficient Volume Estimation for Dynamic Environments using Deep Learning on the EdgeChandan Kumar, Yamini Mathur, Ali Jannesari 0001. 995-1002 [doi]
• TinyMLOps: Operational Challenges for Widespread Edge AI AdoptionSam Leroux, Pieter Simoens, Meelis Lootus, Kartik Thakore, Akshay Sharma. 1003-1010 [doi]
• Workshop on Resource Arbitration for Dynamic Runtimes (RADR)Pete Beckman, Emmanuel Jeannot, Swann Perarnau. 1011-1013 [doi]
• Performance Analysis of Multi-Containerized MD Simulations for Low-Level Resource AllocationShingo Okuno, Akira Hirai, Naoto Fukumoto. 1014-1017 [doi]
• Operating System Convergence: An Example via the Maru OS ProjectWilliam White, Xiao Qin. 1018-1027 [doi]
• Combining Uncore Frequency and Dynamic Power Capping to Improve Power SavingsAmina Guermouche. 1028-1037 [doi]
• ScaDL 2022 Invited Talk 1: Design of secure power monitors for accelerators, by exploiting ML techniques, in the Euro-HPC TEXTAROSSA projectWilliam Fornaciari. 1039 [doi]
• ScaDL 2022 Invited Talk 2: AI/ML Pipelines using CodeFlareMudhakar Srivatsa. 1040 [doi]
• ScaDL 2022 Invited Talk 3: Million-x speedups through convergence of AI and HPCAnima Anandkumar. 1041 [doi]
• ScaDL 2022 Invited Talk 4: Sustainable AI @ Scale: Accelerating AI models for billions of usersMichael Gschwind. 1042 [doi]
• When Moore Just Isn't Enough: Scaling ML in the DatacenterDavid Kanter. 1043 [doi]
• Designing Effective Sparse Expert ModelsBarret Zoph. 1044 [doi]
• Distributing Deep Learning Hyperparameter Tuning for 3D Medical Image SegmentationJosep Lluís Berral, Oriol Aranda, Juan Luis Domínguez, Jordi Torres. 1045-1052 [doi]
• A Methodology to Build Decision Analysis Tools Applied to Distributed Reinforcement LearningCèdric Prigent, Loïc Cudennec, Alexandru Costan, Gabriel Antoniu. 1053-1062 [doi]
• MadPipe: Memory Aware Dynamic Programming Algorithm for Pipelined Model ParallelismOlivier Beaumont, Lionel Eyraud-Dubois, Alena Shilova. 1063-1073 [doi]
• APPFL: Open-Source Software Framework for Privacy-Preserving Federated LearningMinseok Ryu, Youngdae Kim, Kibaek Kim, Ravi K. Madduri. 1074-1083 [doi]
• Throughput-oriented and Accuracy-aware DNN Training with BFloat16 on GPUZhen Xie, Siddhisanket Raskar, Murali Emani. 1084-1087 [doi]
• Adaptive Optimization for Sparse Data on Heterogeneous GPUsYujing Ma, Florin Rusu, Kesheng Wu, Alexander Sim. 1088-1097 [doi]
• ESSA 2022 Keynote Speaker: Keep Your Composure: HPC, Data Services, and the Mochi ProjectRob Ross. 1100 [doi]
• ESSA 2022 Invited Speaker DAOS: Nextgen Storage Stack for HPC and AIJohann Lombardi. 1101 [doi]
• ESSA 2022 Invited Speaker: The Curious Incident of the Data in the Scientific WorkflowLavanya Ramakrishnan. 1102 [doi]
• Caching Support for CHFS Node-local Persistent Memory File SystemOsamu Tatebe, Hiroki Ohtsuji. 1103-1110 [doi]
• A Locality-aware Cooperative Distributed Memory Caching for Parallel Data Analytic ApplicationsChia-Ting Hung, Jerry Chou 0001, Ming-Hung Chen, I-Hsin Chung. 1111-1117 [doi]
• Modeling Power Consumption of Lossy Compressed I/O for Exascale HPC SystemsGrant Wilkins, Jon C. Calhoun. 1118-1126 [doi]
• 6th IEEE Workshop on Parallel and Distributed Processing for Computational Social Systems (ParSocial 2022)John Korah, Eunice E. Santos. 1127-1128 [doi]
• Dynamic Batch Parallel Algorithms for Updating PageRankSubhajit Sahu, Kishore Kothapalli, Dip Sankar Banerjee. 1129-1138 [doi]
• Distributed Algorithms for the Graph Biconnectivity and Least Common Ancestor ProblemsIan Bogle, George M. Slota. 1139-1142 [doi]
• Efficient Parallel PageRank Algorithm for Network AnalysisMaxence Vandromme, Serge G. Petiton. 1143-1152 [doi]
• A Streaming System for Large-scale Temporal Graph Mining of Reddit DataAndreas Huber, Daniel Thilo Schroeder, Konstantin Pogorelov, Carsten Griwodz, Johannes Langguth. 1153-1162 [doi]
• Unsupervised User Stance Detection on Tweets Against Web Articles Using Sentence TransformersBhashithe Abeysinghe, Gyandeep Reddy Vulupala, Anu G. Bourgeois, Rajshekhar Sunderraman. 1163-1169 [doi]
• Effect of Community-based Opinion Leaders on Guideline Dissemination in Large-Scale Physician NetworksVairavan Murugappan, Suresh Subramanian, John Korah, Pranav Pamidighantam, Eunice E. Santos. 1170-1179 [doi]
• EDAML 2022 Keynote Speaker: Machine Learning for Agile, Intelligent and Open-Source EDADavid Z. Pan. 1181 [doi]
• EDAML 2022 Invited Speaker 1: Application of Machine Learning in High Level SynthesisAnkush Sood. 1182 [doi]
• EDAML 2022 Invited Speaker 2: AI Algorithm and Accelerator Co-design for Computing on the EdgeDeming Chen. 1183 [doi]
• EDAML 2022 Invited Speaker 3: Scalable ML Architectures for Real-time Energy-efficient ComputingR. Iris Bahar. 1184 [doi]
• EDAML 2022 Invited Speaker 4: Fault Criticality Assessment in AI AcceleratorsKrishnendu Chakrabarty. 1185 [doi]
• EDAML 2022 Invited Speaker 5: Combining Optimization and Machine Learning in Physical DesignLaleh Behjat. 1186 [doi]
• EDAML 2022 Invited Speaker 6: Reliable Processing-in-Memory based Manycore Architectures for Deep Learning: From CNNs to GNNsPartha Pratim Pande. 1187 [doi]
• EDAML 2022 Invited Speaker 7: Analog and Digital Circuit and Layout Optimization using Machine LearningSachin S. Sapatnekar. 1188 [doi]
• EDAML 2022 Invited Speaker 8: Machine Learning for Cross-Layer Reliability and SecurityMuhammad Shafique. 1189 [doi]
• EDAML 2022 Invited Speaker 9: Thermal and Power Monitoring and Estimation for Commercial Multicore Processors - A Machine Learning PerspectiveSheldon Tan. 1190 [doi]
• EDAML 2022 Invited Speaker 10: Hardware/Software Codesign for Optical Deep Learning AcceleratorsSudeep Pasricha. 1191 [doi]
• COMPSYS 2022 Keynote Talk: Composability at the Boundary Between HPC and CloudPaul Carpenter. 1194 [doi]
• Quantifying Composable Data Center UtilizationMarc Taubenblatt, Asser N. Tantawi. 1195-1201 [doi]
• Separated Allocator Metadata in Disaggregated In-Memory Databases: Friend or Foe?Marcel Weisgut, Daniel Ritter 0001, Martin Boissier 0001, Michael Perscheid. 1202-1208 [doi]
• Composable Infrastructures for an Academic Research Environment: Lessons LearnedLance Long, Timothy Bargo, Luc Renambot, Maxine D. Brown, Andrew E. Johnson 0001. 1209-1214 [doi]
• Moving from Composable to ProgrammableZhongyi Chen, Luc Renambot, Lance Long, Maxine D. Brown, Andrew E. Johnson 0001. 1215-1220 [doi]
• Evaluating Hardware Memory Disaggregation under Delay and ContentionArchit Patke, Haoran Qiu, Saurabh Jha, Srikumar Venugopal, Michele Gazzetti, Christian Pinto, Zbigniew Kalbarczyk, Ravishankar K. Iyer. 1221-1227 [doi]
• CORtEX 2022 Invited Speaker 1: The GeNN ecosystem for GPU accelerated spiking neural network simulationsThomas Nowotny, James C. Knight. 1237 [doi]
• CORtEX 2022 Invited Speaker 2: Brain-like machine learning using BCPNNAnders Lansner. 1238 [doi]
• CORtEX 2022 Invited Speaker 3: Neuromorphic computing: from modelling the brain to bio-inspired AIOliver Rhodes. 1239 [doi]
• CORtEX 2022 Invited Speaker 4: Large-scale simulations of mammalian brains using peta- to exa-scale computingJun Igarashi. 1240 [doi]
• Controlling the spiraling costs of Deep Learning with the NeocortexLawrence Spracklen. 1241 [doi]
• ExSAIS: Workshop on Extreme Scaling of AI for Science Message from the workshop chairsSvitlana Volkova, Robert Rallo. 1242-1243 [doi]
• Keynote Talk 1: Efficient DNN Training at Scale: from Algorithms to HardwareGennady Pekhimenko. 1244 [doi]
• Keynote Talk 2 Training Large Language Models: Challenges and OpportunitiesMostofa Patwary. 1245 [doi]
• Learning to Scale the Summit: AI for Science on a Leadership SupercomputerWayne Joubert, Bronson Messer, Philip C. Roth, Antigoni Georgiadou, Justin Lietz, Markus Eisenbach 0002, Junqi Yin. 1246-1255 [doi]
• A Scalable Pipeline for Gigapixel Whole Slide Imaging Analysis on Leadership Class HPC SystemsSajal Dash, Benjamín Hernández, Aristeidis Tsaris, Folami T. Alamudun, Hong-Jun Yoon, Feivi Wang. 1266-1274 [doi]
• IPDPS 2022 PhD ForumSanjukta Bhowmick, Anne-Cécile Orgerie. 1275-1294 [doi]