Past Projects And Grants of Qing Yi

NSF HECURA award CCF-0833203 (CCF-1261778 since Sep 2012)

  • Title: Programmable Code Optimization and Empirical Tuning For High-end Computing
  • Total amount: $462,000
  • Period: 09/01/08 - 08/31/14
  • Priciple investigator: Qing Yi
  • co-investigators: R. Clint Whaley and Daniel Quinlan
  • Students supported: Faizur Rahman(PhD,2010-), Jichi Guo (PhD, 2009-), Akshatha Bhat (MS, 2010-2012), Md Ziaul Haque (MS, 2011-2012)
  • Project Summary : The complexity of modern high-end computers has made it exceedingly difficult for scientific applications to effectively manage resources such as extreme-scale parallelism, single-chip multi-processors, and deep hierarchy of shared/distributed caches and memories. In particular, as machines and applications have both evolved to become complex and massively parallel, compilers have failed to automatically bridge the gap between complex software and diverse hardware platforms. Optimization models for parallel computing have lagged far behind those for serial applications, and conventional compilers are increasingly unable to accommodate emerging high-end architectures.

    This research develops a new optimization model that allows 1) developers to effectively interact with advanced optimizing compilers to provide both domain-specific knowledge and high-level optimization strategies (e.g., directions to enable new or choose amongst differing parallelization strategies); 2) computational specialists to easily define arbitrary domain-specific transformations to directly control performance optimizations to their code; 3) architecture-sensitive optimizations to be easily parameterized and empirically tuned to achieve portable high performance. The optimization model is supported with an integrated environment that contains two main components: ROSE, a C/C++/Fortran2003 source-to-source optimizing compiler developed at DOE/LLNL; and POET, a transformation language together with an empirical optimization engine developed at UTSA. This framework permits different levels of automation and programmer intervention, from fully-automated tuning to semi-automated development to fully programmable control. The research targets both the optimization needs of computational kernels and the more general requirements of whole program optimizations. The framework is integrated as an external development mechanism for the widely-adopted ATLAS library and is connected with empirical tuning research under DOE SciDAC program to improve the efficiency of large-scale scientific applications.

DOE/LLNL subcontract B602385

  • Title: Automatically Migrating Stencil Computations For Many-Core Architectures
  • Total amount $50,798
  • Period : 12/13/12 - 01/30/14
  • Priciple investigators: Qing Yi
  • co-investigators: none
  • Students supported: TBA
  • Project Summary : This research will develop program analysis and transformation techniques to automate the migration of existing scientific applications written either sequentially or using OpenMP to run efficiently on future many-core architectures. A special emphasis will be on the analysis, migration, and optimization of stencil computations, which are used extensively in scientific domains such as finite differential equations, finite element methods, and fluid dynamics simulations. The work will be developed within the ROSE C/C++/Fortran source-to-source compiler at LLNL and will utilize capabilities within the POET program transformation and tuning framework developed by Dr. Qing Yi to help support the optimization and tuning needs. Special emphasis will be placed on making sure that the developed techniques will have significant impact on improving the performance and power efficiency of DOE scientific applications.

DOE Office of Science award DE-SC0001770

  • Title: A Multi-Language Environment For Programmable Code Optimization and Empirical Tuning
  • Total amount : $360,000
  • Period : 09/15/09 - 09/14/13
  • Priciple investigator: Qing Yi
  • co-investigators: R. Clint Whaley , Daniel Quinlan and Apan Qasem
  • Students supported: Faizur Rahman(PhD, 2010-), Jichi Guo (PhD, 2009-)
  • Project Summary : We will build an integrated optimization environment for programmable code optimization and empirical tuning within the framework of existing languages. The environment will use ROSE, a source-to-source optimizing compiler at DOE/LLNL, and POET, an transformation scripting language at UTSA, to support the automated parameterization of source-to-source optimizations and the empirical tuning of applications in C, C++, and Fortran 2003. Our approach will permit different levels of possible automation and programmer intervention, from fully-automated tuning of whole applications to semi-automated development of domain-specific libraries. Such an environment will permit maximal impact on the performance optimization of existing and future software development, including both the optimization needs of computational kernels and the more general requirements of whole program optimizations. Our work will be integrated as an external development mechanism for the widely-adopted ATLAS library and will be connected with existing empirical tuning research under DOE SciDAC PERI program.

NSF CNS-0855247

  • Title: II-NEW: Enhanced Parallelization for High Performance Computing
  • Total amount : $227,178
  • Period : 08/01/09 - 07/31/12
  • Priciple investigator: Kleanthis Psarris
  • Co-investigators: Ali S. Tosun, Dakai Zhu, Qing Yi
  • Students supported: none
  • Project Summary : This project is for acquisition of a cluster for high performance computing.

Lawrence Livermore National Laboratory Sub-contract B574748

  • Title: Program Analysis and Optimization For The Empirical Tuning of Scientific Applications
  • Total amount : $51,710
  • Period : 04/01/08 - 10/31/09
  • Priciple investigator: Qing Yi
  • co-investigators: Collaborated work with Daniel Quinlan
  • Students supported: Brian Edwards (BS, 2007-2008)
  • Project Summary : Due to the increasing complexity of computer architectures, compilers have great difficulty statically predicting the dynamic behavior of applications on different computing platforms. Automated empirical tuning technology resolves this difficulty by experimentally applying different transformations to an input computation and then selecting the optimizations that produce the best-performing code. This contract will extend the ROSE compiler to effectively parameterize advanced optimizations so that differently transformed code can be dynamically generated and their performance empirically measured. Additionally, we will extend optimizations within ROSE to utilize results of advanced program analysis techniques to improve the effectiveness of these optimizations in understanding application semantics.

Lawrence Livermore National Laboratory Sub-contract B555671

  • Title: Semantics-driven optimization of scientific applications.
  • Total amount : $70,193
  • Period : 01/01/06 - 3/31/08
  • Priciple investigator: Qing Yi
  • co-investigators: Collaborated work with Daniel Quinlan
  • Students supported: Brian Edwards (BS, 2007-2008)

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