Hello, A yearly allocation for the LCRC cluster has been requested with the following updated information: Submitter/PI: Prasanna Balaprakash Project Name: perfopt Division: MCS Project title: Optimization Algorithms for Empirical Performance Associated funding: DOE ASCR Other Systems: Mira, ANL: 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: There are different strategies for performance model construction. Analytical approaches for performance modeling are cheaper in the sense that they don't suffer from the profiling and model generation cost as in the case of machine learning techniques. But analytical models are often difficult to construct for complex programs and they require detailed understanding of the underlying hardware and implementation of communication functions. Machine learning techniques come into play when it is difficult to construct an analytical model for an application. Models generated using machine learning techniques may not be as highly precise as an analytical model but still they help to understand the performance of an application that in turn help in optimizations such as auto tuning. In this research, we first try to build analytical models for communication of various application kernels. We use architecture-independent parameters as described in well known communication models such as LogGP family to build the analytical models. If the constructed analytical model is accurate enough in predicting the communication time, it saves the huge overhead in gathering training data for the application of machine learning algorithms. We will test the accuracy of the developed analytical models in predicting the communication time of several application kernels from the CORAL benchmarks. If the analytical model is not accurate enough (the error in prediction goes above acceptable range), we switch to gathering profile data and then generate models using machine learning techniques on that data. This hybrid approach gives us a balance between the complexity of building analytical models and the overhead of running expensive machine learning algorithms on huge amount of collected profiling data. Also the machine learning models are limited in the sense that we can't do extrapolation. Fitted analytical models come into play in that case. On Blues and Bebop, we will conduct an experimental study of communication models. A major benefit Blues brings to this research is the ability to test the impact of MPI implementations and interconnect techniques on Intel-based clusters. Eventually, we hope to deploy our techniques for improving the performance of scientific applications running on Blues. We estimate 100,000 core hours for our experimental study: evaluation of the strengths and limitations of LogGP models = 25,000 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 = 20,000 core hours; We will strongly involve in research dissemination activities such as writing scientific articles and giving seminars at Argonne and in conferences. In the the first and second quarter, our jobs will be 80% parallel. For the third and fourth quarter, we will have 100%. Industry partnership: Project URL: Current FY Hours Used: undetermined amount New FY Requested allocation: 105000 Q1: 25000 Q2: 30000 Q3: 30000 Q4: 20000 Justification: Storage requirements: Thank You, The LCRC Accounts System