Overview of reports

1. Overview of reports Adam Leko HCS Research Laboratory University of Florida

2. Programming report overview (report form) • Report on programming • CAMEL • Description of benchmark • Overview of code • Comparison of UPC code with MPI • Bench9 • Description of benchmark • Overview of code • Comparison of UPC code with MPI and SHMEM • Discussion of the effect of different optimizations on code • Convolution • Description of benchmark • Overview of code • Comparison of UPC code with MPI and SHMEM • Discussion of the effect of different optimizations on code

3. Report on Paradyn (slideset) • Overview of Paradyn • Description of instrumentation overview • Paradyn architecture overview • Dynamic instrumentation overview • Description of metrics and visualizations • PCL & MDL configuration files • Overview of W3 search model (why, where, when) & performance consulatant • Continuation of description based on site visit slides • Demo • Show performance consultant/W3 on sequential and MPI program • Evaluation of Paradyn & discussion • Finish evaluation from the site visit slides • Current limitations/caveats • What problems we might encounter if we try to adapt for SHMEM and UPC

4. Report on performance modeling • Paper outline • Abstract & Introduction • Overview of performance models • Reasons for examining performance models in the design of a PAT • Get a list of performance factors • Performance models can guide a PAT in giving advice or identifying bottlenecks • Overview & analysis of current performance models • Formal performance models • Petri nets & process algebras • Quick description; not a detailed discussion since formal performance models aren’t very useful for a PAT • Analytical performance models • Meant to give programmer “mental picture” of programming environment (LogP & variants, BSP, etc) • Predictive performance models • Meant to predict performance of actual applications on a range of architectures • Probably the most useful to us • For each performance model, • Give list of performance factors used to characterize model • Report accuracy for each model • Conclusion & recommendations • Give recommendation on how to best use a performance model in the context of our PAT

5. Performance model papers • Formal models • “Software Performance Modeling using PEPA nets,” S. Gilmore et all. • Analytical models • “Parallelism in Random Access Machines,” S. Fortune and J. Wyllie. • “LogP: Towards a Realistic Model of Parallel Computation,” Culler et all. • “A Bridging Model for Parallel Computation,” L. Valiant. • Related paper: “Using the BSP Cost Model for Optimal Parallel Neural Network Training,” R. Rogers and D. Skillicorn. • Related paper: “Theory, practice, and a tool for BSP performance prediction,” J. Hill et all. • “Modeling Performance of Parallel Programs,” W. Meira, Jr. [tech report on lost cycles analysis] • Related paper: “Parallel Performance Prediction Using Lost Cycles Analysis,” M. Crovella and T. LeBlanc. • Predictive models • “A Static Parameter based Performance Prediction Tool for Parallel Programs,” T. Fahringer and H. Zima. • “A Framework for Performance Modeling and Prection,” A. Snaveley et all. • “Predicting and Evaluating Distributed Communication Performance,” K. Cameron and R. Ge. • “PerPreT – A Performance Prediction Tool for Massively Parallel Systems,” J. Brehm. • “PACE: A Toolset to Investigate and Predict Performance in Parallel Systems,” D. Kerbyson et all. • Related paper: “Is Predictive Tracing Too Late for HPC Users?” D. Kerbyson et all. • “An Effective and Practical Performance Prediction Model for Parallel Computing on Non-dedicated Heterogeneous NOW,” Y. Yan and X. Zhang. • “Parametric Micro-level Performance Models for Parallel Computing,” Y. Kim et all. • “Estimating and Optimizing Performance for Parallel Programs,” T. Fahringer. • Related paper: “Automatic Performance Prediction to Support Parallelization of Fortran Programs for Massively Parallel Systems,” T. Fahringer et all. • Related paper: “Hierarchical Performance Prediction for Parallel Programs,” R. Blasko.

6. Performance model papers • Predictive models, continued • “Compiling Performance Models from Parallel Programs,” A. van Gemund. • Related paper: “Performance Prediction of Parallel Processing Systems: The PAMELA Methodology,” A. van Gemund. • “Analytical Performance Prediction on Multicomputers,” M. Clement. • “Studying the Performance Properties of Concurrent Programs by Simulation Experiments on Synthetic Programs,” R. Candlin. • “Performance Prediction of Large Parallel Applications Using Parallel Simualtions,” R. Bagrodia et all. • “Predictive Performance and Scalability Modeling of a Large-Scale Application,” D. Kerbyson et all. • “Parallel Program Performance Prediction Using Deterministic Task Graph Analysis,” V. Adve. • Related paper: “Analyzing the Behavior and Performance of Parallel Programs, “ V. Adve. • “Execution-Driven Tools for Parallel Simulation of Parallel Architectures and Applications,” D. Poulsen and R. Yew. • “Analytical Performance Prediction on Multicomputers,” M. Clement and M. Quinn. • “A Performance Evaluation of Cluster-based Architectures,” X. Qin and J. Baer. • “A Source Code Analyzer for Performance Prediction,” M. Kuhnemann et all. • “Interpretive Performance Prediction for High Performance Application Development,” M. Parashar and S. Hariri. • Related paper: “Compile-Time Performance Prediction of HPF/Fortran 90D,” M. Parashar and S. Hariri. • “Performance Prediction: A Case Study Using a Scalable Shared-Virtual-Memory Machine,” X. Sun and J. Zhu. • “Accurate Performance Prediction for Massively Parallel Systems and its Applications,” J. Simon and J. Wierum. • “A Performance Modeling System for Message-Passing Parallel Programs,” D. Grove and P. Coddington. • Related paper: “Performance Modeling of Message-Passing Parallel Programs,” D. Grove (PhD thesis). • Approaches to parallel performance prediction,” F. Howell (PhD thesis). • Related paper: “Reverse Profiling,” F. Howell.

7. Report on compiler optimizations • Paper outline • Abstract & Introduction • Overview of compiler optimizations • Reasons for examining compiler optimizations • Get a list of performance factors • Compiler optimizations can affect performance in positive or negative ways, so its important to • Overview & analysis of compiler optimizations • Concentrate on compiler optimizations directed at parallel programs (High-Performance Fortran) • Conclusion & recommendations • Give recommendation on how to best use a performance model in the context of our PAT • Future work (next semester) • Examine sequential compiler optimizations • Examine algorithm optimization techniques