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University of California, Riverside

CSM Supercomputing Laboratory



The Supercomputing Laboratory (SuperLab) at the University of California, Riverside conducts research and development in the broad area of high performance computing and big data analytics. The mission of the laboratory is to develop techniques, design algorithms, and build software tools to improve the performance, reliability, and energy efficiency of large scale computations and big data applications. Specific topics of interest include (but are not limited to):

  • algorithm-based fault tolerance (ABFT)
  • matrix computations
  • big data analytics
  • energy efficient computing
  • power-aware algorithms and software
  • checkpointing techniques in parallel and distributed environments
  • the interplay between fault tolerance and energy efficiency
  • cluster and cloud computing
  • extreme scale computing
  • message passing interface (MPI)
  • high performance computing on GPUs with CUDA and OpenCL
  • improving performance using Intel MICs
  • parallel programming with MPI in Fortran/C/C++
  • parallel algorithm design and analysis
  • real number error/erasure correcting codes
  • numerical linear algebra algorithms and software
  • scientific computing
  • petascale earthquake simulations
  • high performance reservoir simulations

SuperLab members have access to a wide range of high performance computing platforms including the current world's fastest supercomputer Sunway TaihuLight at National Supercomputing Center in Wuxi, the current world's second fastest supercomputer Tianhe-2 at the China National Supercomputing Center in Guangzhou, and the current world's third fastest supercomputer Titan at at the U.S. Department of Energy Oak Ridge National Laboratory.

SuperLab has received research funds and gifts from National Science Foundation, Department of Energy, Abu Dhabi National Oil Company, CMG Reservoir Simulation Foundation, NVIDIA, and Microsoft Corporation.






 


Latest News:
  • 2017-06-15: Two full papers accepted by SC'17. The acceptance rate is 18.6% (61/327). Congratulations, Hongbo Li and Xin Liang!

  • 2017-04-10: Xin Liang and Jieyang Chen received summer internship offers from Los Alamos National Laboratory. Dingwen Tao received a summer internship offer from Argonne National Laboratory

  • 2017-04-04: Panruo Wu received a tenure-track Assistant Professor job offer from the University of Houston

  • 2016-11-11: One full paper is accepted by PPoPP'17

  • 2016-06-15: One full paper is accepted by SC'16

  • 2016-03-12: Three full papers accepted by HPDC'16

  • 2015-11-06: Li Tan defended his PhD dissertation and accepted a job offer from Los Alamos National Laboratory