Active Projects And Grants for Qing Yi
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I'm currently the principle investigater of three grants, with student support
available from each grant, and the Co-PI of one grant (no student report).
My main research focus is using POET , a transformation
scripting language, to build software development tools for improving both
software productivity and performance. Here are some links for the POET project.
I'm also working collaboratively with the following colleagues on several related projects.
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NSF CAREER award CCF-0747357
- Title: Multilayer Code Synthesis For Correctness and Performance
- Total amount : $399,953
REU Supplemental award (Aug,2009) : $16,000
- Period : 08/01/08 - 07/31/13
- Priciple investigator: Qing Yi
- co-investigators: none
- Students supported: Anitha Ancha(MS), Emilio Mercado(BS), Ali Scissons(BS)
Project Summary :
Correctness and performance are two of the most fundamental concerns in software development.
In particular, the increasing complexity of modern computing environment has made it extremely
difficult for software applications to be both correct and efficient. Software programs are
frequently found to be flawed, and existing technology has fallen behind in providing the
necessary programming language and tool support to ensure high quality software development.
This research develops programming language as well as compiler analysis and optimization
techniques to support the automated translation of software from high-level design to
low-level efficient implementations. This research develops a multilayer code synthesis
framework that systematically produces high-quality software by effectively combining
software verification techniques with program analysis and compiler optimization in a
three-phase translation process. First, starting from the software design phase, the
framework automatically translates formal software semantic specifications into
object-oriented or procedural implementations based on strategies selected by programmers.
Then, based on knowledge from the software-design phase, a sequence of domain-specific
optimizations is applied to the implementation to improve algorithm efficiency. Finally,
architecture-specific optimizations are applied to performance-critical routines, and the
optimized routines are empirically tuned as the application is ported to different machines.
Different design and programming languages may be used in each translation phase, and
software verification technology will be used to ensure the correctness of each translation.
The research focuses on scientific computing and system software applications, where both
correctness and performance are of critical concern. The integrated research is expected
to significantly improve both the trustworthiness and performance of existing software
development.
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NSF HECURA award CCF-0833203
- Title: Programmable Code Optimization and Empirical Tuning For High-end Computing
- Total amount : $462,000
- Period : 09/01/08 - 08/31/11
- Priciple investigator: Qing Yi
- co-investigators:
R. Clint Whaley and
Daniel Quinlan
- Students supported: Jichi Guo (PhD)
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.
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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 - 09/14/12
- Priciple investigator: Qing Yi
- co-investigators:
R. Clint Whaley and
Daniel Quinlan
- Students supported: TBA
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.
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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.
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