Performance Model & Tools Summary Hung-Hsun Su UPC Group, HCS lab 2/5/2004

Models Amdahl ’ s law, Scaled-speedup, LogP, cLogP, BSP Parametric micro-level (PM, 1994)  Predict execution time, identify bottleneck, compare machines  Incorporate precise details of interprocessor communication, memory operations, auxiliary instructions and effects of communication and computation schedules  Derive analytical formulas  experimental measurement of sample cases  estimate misc. overhead  refine formula  predict execution time using formula ZPL (1998)  Model incorporated into language design  Scalar performance, concurrency and interprocessor communication  Identify interacting regions to determine how the data/processor is mapped  Once mapping is know, the cost is calculated by  Also try to compare alternative solutions through formula

Models “Analytical Modeling of Parallel Programs”  Execution time, Total Parallel Overhead, Speedup, Efficiency, Cost  Isoefficiency function Determines the ease with which it can achieve speedups increasing in proportion to number of processors (small  highly scalable) Determine if system is “cost-optimal” if [(Num. Proc) * Tp] vs Ts is proportional to each other Calculation of lower bound is use to determine the degree of concurrency  Minimum execution time and cost-optimal execution time  Asymptotic Analysis Analyzing performance using kernel performance  Define coupling (interaction) between kernels that tries to improve the accuracy Overhead Model  Generalized Amdahl’s law model  Lost Cycles Analysis Agarwal network model  Wire, switch delays, message size, communication latency (contention not considered) Closed queueing network model  Task graph that gives the synchronization constraints and use a closed queuing model to describe contention delay  Predict mean response time and resource utilization Anita W. Tam Model  Application – establishes a relationship between message generation rate and communication latency  Network Model – provide average message latency as function of message generation rate of nodes together with other system parameters

EPPA* *All information regarding EPPA taken from

EPPA Information Retained  The different phases of the program, like useful computation, partitioning (Cost of each phase, its impact on the performance)  The experiment parameters, like #processors, work size, hardware, … (Multiple experiment analysis: measurements in function of parameters)  The #quantums processed and communicated in each phase (Time of the phases in function of #quantums)  The #operations that are computed in each phase#operations per quantum (Time of phases in function of #basic operations) Does not use hardware counters, give first-order analysis

EPPA

PROPHET* *All information regarding PROPHET taken from

PROPHET prediction of the performance behavior of parallel and distributed applications on cluster and grid architectures Based on a UML model of an application and a simulator for a target architecture, one can predict the execution behavior of the application model

SCALEA* *All information regarding SCALEA taken from

SCALEA Profile/Trace Analysis  Inclusive/Exclusive Analysis  Load balancing Analysis  Metric Ratio Analysis  Execution Summary Overhead analysis  Region to Overhead  Overhead to region Analysis functions

AKSUM* *All information regarding AKSUM taken from

AKSUM Automatic performance bottleneck analysis tool Performance properties are normalized  Performance property name  Threshold  Reference code region

Prediction Tools P 3 T  performance estimator for HPF programs closely integrated with VFCS  The core part of P 3 T is centered around a set of parallel program parameters (transfer time, number of transfers, computation time, etc. Carnival  attempt to automate the cause-and-effect inference process for performance phenomena Network Weather Service  uses numerical models and monitored readings of current conditions to dynamically forecast the performance that various network and computational resources can deliver over a given time frame

Knowledge-based Tools Autopilot  aims at dynamically optimizing the performance of parallel applications. Kappa-PI  knowledge-based performance analyzer for parallel MPI and PVM programs. The basic principle of the tool is to analyse the efficiency of an application and provide the programmer with some indications about the most important performance problem found in the execution

Organizations APART - IST Working Group on Automatic Performance Analysis: Real Tools Parallel Tools Consortium

Interesting Ideas Tool that facilitate going from one system to another