Hello, A new project on the LCRC cluster has been requested. Please forward the information on to the LCRC Allocation sub-committee. Applicant's name: Prasanna Balaprakash Applicant's institution: ANL Applicant's division: MCS Project Name: perfopt Project title: Optimization Algorithms for Empirical Performance Associated funding: DOE ASCR Other Systems: Surveyor, ANL: 100000 core hours NERSC: 100000 core hours Science: Our scientific objectives are: i) To advance the state-of-the-art in empirical performance tuning and adaptation of scientific applications on extreme scale architectures and thereby significantly increasing scientific productivity ii) To improve our understanding of large-scale empirical performance tuning iii) To develop novel mathematical optimization algorithms that exploits the characteristics of the search problem in performance tuning Project description: The increasing complexity, heterogeneity, and rapid evolution of modern computer architectures present obstacles for achieving high performance of scientific codes. Even after algorithmic improvements --- seeking to improve scalability or minimize communication, for example --- are made, performance can vary greatly from machine to machine. Empirical performance tuning addresses this issue by selecting code variants based on their measured performance on the target machine. A major bottleneck in empirical performance tuning is the computation time associated with testing a large number of possible code variants, which grows exponentially with the number of tuning parameters. While typically parameters are tuned by hand, recent studies have shown that optimization algorithms can find high quality configurations quickly. Our goal is to advance the state-of-the-art in empirical performance tuning and adaptation of scientific applications on extreme scale architectures and thereby significantly increasing scientific productivity. To achieve this goal, we will systematically evaluate the strengths and limitations of optimization algorithms in empirical performance tuning. This study will improve our understanding of large-scale empirical performance tuning and help us to identify open issues that need to be addressed which will require developing novel, flexible optimization approaches. A main aspect of this line of research will be the systematic integration and experimental analysis on multiple problem classes on different architectures. Currently, we are developing novel numerical optimization algorithms that exploits the characteristics of the search problem in performance tuning. We will investigate the effectiveness of the optimization algorithms and will conduct an experimental study of some popular optimization algorithms. An important element needed for empirical performance tuning research is a collection of test problems that allow performance engineering and mathematical optimization researchers to conduct rigorous algorithmic developments and experimental studies. We developed a collection of extensible and portable search problems in automatic performance tuning (SPAPT). Each problem in SPAPT is a well defined mathematical optimization problem comprises a representative kernel from a scientific application, parameterized tuning directives, values for each parameter, input sizes, and an initial configuration for optimization algorithms. We will conduct experiments to show performance impacts of problem characteristics such as choice of performance objectives, noise, effect of cache misses, and input sizes. On Fusion, we plan to conduct experiments on scalability, parallelization strategies (such as OpenMP, MPI, and their hybrids), genera, and architecture-specific code optimization techniques. Another benefit Fusion brings to this research is the ability to test the portability of empirical performance tuning techniques on Intel-based clusters. Eventually, we hope to deploy our techniques for improving the performance of scientific applications running on Fusion. We estimate 100,000 core hours for our experimental study: evaluation of the strengths and limitations of empirical performance tuning = 15,000 core hours; experimental analysis on multiple problem classes on different architectures = 30,000 core hours; empirical analysis for new approaches = 30,000 core hours; tests on parallel performance tuning approaches = 25,000 core hours; We will strongly involve in research dissemination activities such as writing scientific articles and giving seminars at Argonne and in confe rences. Concerning the percentage of serial jobs, in the the first and second quarter, our jobs will be 80% serial. For the third and fourth quarter, we will have 30%. Project URL: http://www.mcs.anl.gov/research/project_detail.php?id=90 Requested allocation: 100000 Q1: 20000 Q2: 30000 Q3: 25000 Q4: 25000 Justification: The requester has used undetermined amount hours of their initial startup project. In addition to approving an initial amount, please specify a Category and Subcategory for this project. For a list of the current selection of approved categories, please see: https://wiki.lcrc.anl.gov/wiki/Processes/Categories Once the Allocation committee has approved the project, please go to the Project Management page to create it: https://accounts.lcrc.anl.gov/projects.php Thank You, The LCRC Accounts System