11/14/2023 0 Comments Bill magro abacus technology![]() ![]() PERKS: a Locality-Optimized Execution Model for Iterative Memory-bound GPU Applications Session 5A (Parallel): Accelerator Programming I (2.00 pm - 3.40 pm), Location: Magnolia 10-11Ĭhair: Milind Kulkarni, Purdue University Accelerating BWA-MEM Read Mapping on GPUs Nikolopoulos, Virginia Tech Title: HPC is dead, long live HPC! The future of HPC in a post-exascale era. Session 3 (Plenary): Keynote Talk (8.45 am - 9.45 am), Location: Magnolia 10-11Ĭhair: Dimitrios S. Jingwen Leng Shanghai Jiao Tong University, Zhibin Yu Shuhai Lab, Huawei Cloud Computing Technologies Co., Ltd,ĭeze ZengĜhina University of Geosciences, Zhou Yu Shuhai Lab, Huawei Cloud Computing Technologies Co., Ltd, Yaoxuan Li Shanghai Jiao Tong University, PAC: Preference-Aware Co-location Scheduling on Heterogeneous NUMA Architectures To Improve Resource Utilization Jianfeng Zhan Institute of Computing Technology, Chinese Academy of Sciences & University of Chinese Academy of Sciences. Zihan Jiang Huawei Technologies Co., Ltd., Lei Wang Institute of Computing Technology, Chinese Academy of Sciences & University of Chinese Academy of Sciences, Wanling Gao Institute of Computing Technology, Chinese Academy of Sciences & University of Chinese Academy of Sciences,Īnzheng Li Institute of Computing Technology, Chinese Academy of Sciences & University of Chinese Academy of Sciences, Xu Wen Institute of Computing Technology, Chinese Academy of Sciences & University of Chinese Academy of Sciences, Talěen-Nun Lawrence Livermore National Laboratory,ĬMLCompiler: A Unified Compiler for Classical Machine Learning ![]() ![]() Performance Embeddings: A Similarity-based Transfer Tuning Approach to Performance Optimization PrasannaěalaprakashĚrgonne National Laboratory. Michael KruseĚrgonne National Laboratory, Session 2 (Plenary): Compilation and Scheduling (4.00 pm - 5.40 pm), Location: Magnolia 10-11Ĭhair: Chen Ding, University of Rochester Transfer-learning-based Autotuning using Gaussian Copulaīrice VideauĚrgonne National Laboratory, Yaocheng Xiang Huawei, Peking University, Jun Xiao Peking University, University of Amsterdam, Using Additive Modifications in LU Factorization Instead of PivotingįLORIA: A Fast and Featherlight Approach for Predicting Cache Performance ZizhongĜhen University of California, Riverside,įranckĜappello Argonne National Laboratory. Kai Zhao University of Alabama at Birmingham, Jinyang Liu University of California, Riverside, Session 1 (Plenary): Best Papers (2.20 pm - 3.40 pm), Location: Magnolia 10-11Ĭhair: Bill Magro, Google FAZ: A flexible auto-tuned modular error-bounded compression framework for scientific data ![]()
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