{"id":343,"date":"2022-09-30T19:25:44","date_gmt":"2022-09-30T17:25:44","guid":{"rendered":"https:\/\/project.inria.fr\/sbac2022\/?page_id=343"},"modified":"2022-10-12T20:57:19","modified_gmt":"2022-10-12T18:57:19","slug":"accepted-papers","status":"publish","type":"page","link":"https:\/\/project.inria.fr\/sbac2022\/conference\/accepted-papers\/","title":{"rendered":"Accepted Papers"},"content":{"rendered":"<h3>List of accepted papers<\/h3>\n<ul>\n<li>Alex Weaver, Krishna Kavi, Pranathi Vasireddy and Gayatri Mehta<br \/>\n<em>Memory-Side Acceleration and Sparse Compression for Quantized Packed Convolutions<\/em><\/li>\n<li>Alexander Goponenko, Kenneth Lamar, Christina Peterson, Benjamin Allan, Jim Brandt and Damian Dechev<br \/>\n<em>Metrics for Packing Efficiency and Fairness of HPC Cluster Batch Job Scheduling<\/em><\/li>\n<li>Alexander van der Grinten, Geert Custers, Duy Le Thanh and Henning Meyerhenke<br \/>\n<em> An MPI-Parallel Algorithm for Static and Dynamic Top-k Harmonic Centrality<\/em><\/li>\n<li>Aravind Sankaran and Paolo Bientinesi<br \/>\n<em> A Test for FLOPs as a Discriminant for Linear Algebra Algorithms<\/em><\/li>\n<li>Arthur Krause, Paulo Santos and Philippe Navaux<br \/>\n<em>Avoiding Unnecessary Caching with History-Based Preemptive Bypassing<\/em><\/li>\n<li>Brady Testa, Samira Mirbagher and Daniel Jim\u00e9nez<br \/>\n<em>Dynamic Set Stealing to Improve Cache Performance<\/em><\/li>\n<li>Daniel Wladdimiro, Luciana Arantes, Pierre Sens and Nicolas Hidalgo<br \/>\n<em>A predictive approach for dynamic replication of operators in distributed stream processing systems<\/em><\/li>\n<li>Elvis Rojas, Diego P\u00e9rez and Esteban Meneses<br \/>\n<em> Exploring the Effects of Silent Data Corruption in Distributed Deep Learning Training<\/em><\/li>\n<li>Emmanuel Agullo, Marek Fel\u0161\u00f6ci, Amina Guermouche, Herv\u00e9 Mathieu, Guillaume Sylvand and Bastien Tagliaro<br \/>\n<em> Study of the processor and memory power consumption of coupled sparse\/dense solvers<\/em><\/li>\n<li>Erhan Tezcan, Tu\u011fba Torun, Fahrican Ko\u015far, Kamer Kaya and Didem Unat<br \/>\n<em> Mixed and Multi-Precision SpMV for GPUs with Row-wise Precision Selection<\/em><\/li>\n<li>Fareed Qararyah, Muhammad Waqar Azhar and Pedro Trancoso<br \/>\n<em> FiBHA: Fixed Budget Hybrid CNN Accelerator<\/em><\/li>\n<li>Guillaume Didier, Cl\u00e9mentine Maurice, Antoine Geimer and Walid J. Ghandour<br \/>\n<em>Characterizing Prefetchers using CacheObserver<\/em><\/li>\n<li>Hammurabi Mendes, Bryce Wiedenbeck and Aidan O&#8217;Neill<br \/>\n<em> Seriema: RDMA\u00ad-based Remote Invocation with a Case\u00ad-Study on Monte\u00ad-Carlo Tree Search<\/em><\/li>\n<li>Hao Wu, Pangbo Sun, Jiangming Jin, Yifan Gong and Ziyue Jiang<br \/>\n<em>TCUDA: A QoS-based GPU Sharing Framework for Autonomous Navigation Systems<\/em><\/li>\n<li>Igor Fontana de Nardin, Patricia Stolf and Stephane Caux<br \/>\n<em>Analyzing Power Decisions in Data Center Powered by Renewable Sources<\/em><\/li>\n<li>James Almgren-Bell, Nader Al Awar, Dilip Geethakrishnan, Milos Grigoric and George Biros<br \/>\n<em>A Multi-GPU Python Solver for Low-Temperature Non-Equilibrium Plasmas<\/em><\/li>\n<li>Javier Garcia Blas, Javier Fernandez, Jes\u00fas Carretero, Fabrizio Marozzo, Domenico Talia, Daniel Martin de Blas and Alberto Fernandez-Pena<br \/>\n<em>Convergence of HPC and Big Data in extreme-scale data analysis through the DCEx programming model<\/em><\/li>\n<li>Jing Chen, Madhavan Manivannan, Bhavishya Goel, Mustafa Abduljabbar and Miquel Peric\u00e0s<br \/>\n<em>STEER: Asymmetry-aware Energy Efficient Task Scheduler for Cluster-based Multicore Architectures<\/em><\/li>\n<li>Jo\u00e3o Fabr\u00edcio Filho, Isa\u00edas Bittencourt Felzmann and Lucas Wanner<br \/>\n<em>Approximate Memory with Protected Static Allocation<\/em><\/li>\n<li>Jo\u00e3o Vieira, Nuno Roma, Gabriel Falcao and Pedro Tom\u00e1s<br \/>\n<em>gem5-ndp: Near-Data Processing Architecture Simulation From Low Level Caches to DRAM<\/em><\/li>\n<li>Jonathas Silveira, Lucas Castro, Victor Ara\u00fajo, Rodrigo Zeli, Daniel Lazari, Marcelo Guedes, Rodolfo Azevedo and Lucas Wanner<br \/>\n<em>Prof5: A RISC-V profiler tool<\/em><\/li>\n<li>Manuel F. Dolz, H\u00e9ctor Mart\u00ednez, Pedro Alonso-Jorda and Enrique S. Quintana-Orti<br \/>\n<em>Convolution Operators for Deep Learning Inference on the Fujitsu A64FX Processor<\/em><\/li>\n<li>Matheus Bernardino and Alfredo Goldman<br \/>\n<em>Parallelizing Git Checkout: a Case Study of I\/O Parallelism<\/em><\/li>\n<li>Maxim Moraru, Adrien Roussel, Hugo Taboada, Christophe Jaillet, Marc Perache and Michael Krajecki<br \/>\n<em>Performance improvements of parallel applications thanks to MPI-4.0 hints<\/em><\/li>\n<li>Miguel Gomes Xavier, Carlos Henrique da Costa Cano Cano, Vin\u00edcius Meyer and C\u00e9sar A. F. De Rose<br \/>\n<em>IntP: Quantifying cross-application interference via system-level instrumentation<\/em><\/li>\n<li>Odin Ugedal and Rakesh Kumar<br \/>\n<em>Mitigating Unnecessary Throttling in Linux CFS Bandwidth Control<\/em><\/li>\n<li>Omar Shaaban, Jimmy Aguilar Mena, Vicen\u00e7 Beltran, Paul Carpenter, Eduard Ayguade and Jesus Labarta Mancho<br \/>\n<em>Automatic aggregation of subtask accesses for nested OpenMP-style tasks<\/em><\/li>\n<li>Rafaela Brum, L\u00facia Drummond, Luciana Arantes, Maria Clicia Castro and Pierre Sens<br \/>\n<em>Optimizing Execution Time and Costs of Cross-Silo Federated Learning Applications with Datasets on different Cloud Providers<\/em><\/li>\n<li>Samuel Cajahuaringa, Leandro N. Zanotto, Daniel L. Z. Caetano, Sandro Rigo, Herv\u00e9 Yviquel, Munir S. Skaf and Guido Araujo<br \/>\n<em>Ion-Molecule Collision Cross-Section Simulation using Linked-cell and Trajectory Parallelization<\/em><\/li>\n<li>Samuel Ferraz, Vinicius Dias, Carlos H. C. Teixeira, George Teodoro and Wagner Meira Jr.<br \/>\n<em>Efficient Strategies for Graph Pattern Mining Algorithms on GPUs<\/em><\/li>\n<li>Sandra Catalan, Francisco D. Igual, Rafael Rodr\u00edguez-S\u00e1nchez, Jos\u00e9 R. Herrero and Enrique S. Quintana-Orti<br \/>\n<em>NUMA-Aware Dense Matrix Factorizations and Inversion with Look-Ahead on Multicore Processors<\/em><\/li>\n<li>Thierry Arrabal, Lucas Betencourt, Eddy Caron and Laurent Lefevre<br \/>\n<em>Setting up an experimental framework for immersion cooling system and analysis<\/em><\/li>\n<li>Vanderlei Pereira, M\u00e1rcio Castro and Odorico Mendizabal<br \/>\n<em>Strategies for Fault-Tolerant Tightly-coupled HPC Workloads Running on Low-Budget Spot Cloud Infrastructures<\/em><\/li>\n<li>Yang Chen, Feng Zhang, Yinhao Hong, Yunpeng Chai, Wei Lu, Hong Chen, Xiaoyong Du, Peipei Wang, Le Mi, Jintao Li, Xilin Tang, Yanliang Zhou, Peng Zhang, Fengyi Chen, Pengfei Li and Yu Li<br \/>\n<em> Managing Petabytes of Data<\/em><\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>List of accepted papers Alex Weaver, Krishna Kavi, Pranathi Vasireddy and Gayatri Mehta Memory-Side Acceleration and Sparse Compression for Quantized Packed Convolutions Alexander Goponenko, Kenneth Lamar, Christina Peterson, Benjamin Allan, Jim Brandt and Damian Dechev Metrics for Packing Efficiency and Fairness of HPC Cluster Batch Job Scheduling Alexander van der\u2026<\/p>\n<p> <a class=\"continue-reading-link\" href=\"https:\/\/project.inria.fr\/sbac2022\/conference\/accepted-papers\/\"><span>Continue reading<\/span><i class=\"crycon-right-dir\"><\/i><\/a> <\/p>\n","protected":false},"author":2137,"featured_media":0,"parent":96,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_mc_calendar":[],"footnotes":""},"class_list":["post-343","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/project.inria.fr\/sbac2022\/wp-json\/wp\/v2\/pages\/343","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/project.inria.fr\/sbac2022\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/project.inria.fr\/sbac2022\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/project.inria.fr\/sbac2022\/wp-json\/wp\/v2\/users\/2137"}],"replies":[{"embeddable":true,"href":"https:\/\/project.inria.fr\/sbac2022\/wp-json\/wp\/v2\/comments?post=343"}],"version-history":[{"count":2,"href":"https:\/\/project.inria.fr\/sbac2022\/wp-json\/wp\/v2\/pages\/343\/revisions"}],"predecessor-version":[{"id":369,"href":"https:\/\/project.inria.fr\/sbac2022\/wp-json\/wp\/v2\/pages\/343\/revisions\/369"}],"up":[{"embeddable":true,"href":"https:\/\/project.inria.fr\/sbac2022\/wp-json\/wp\/v2\/pages\/96"}],"wp:attachment":[{"href":"https:\/\/project.inria.fr\/sbac2022\/wp-json\/wp\/v2\/media?parent=343"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}