Transcript Programming in Programming in UPC

Performance Model & Tools Summary
Hung-Hsun Su
UPC Group, HCS lab
2/5/2004
Models
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Amdahl’s law, Scaled-speedup, LogP, cLogP, BSP
Parametric micro-level (PM, 1994)
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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)
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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
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“Analytical Modeling of Parallel Programs”
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Execution time, Total Parallel Overhead, Speedup, Efficiency, Cost
Isoefficiency function
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Wire, switch delays, message size, communication latency (contention not considered)
Closed queueing network model
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Generalized Amdahl’s law model
Lost Cycles Analysis
Agarwal network model
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Define coupling (interaction) between kernels that tries to improve the accuracy
Overhead Model
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Minimum execution time and cost-optimal execution time
Asymptotic Analysis
Analyzing performance using kernel performance
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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
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
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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 http://parallel.vub.ac.be/research/parallel_performance/
EPPA
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Information Retained
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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
EPPA
PROPHET*
*All information regarding PROPHET taken from http://www.par.univie.ac.at/project/prophet/
PROPHET
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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 http://www.par.univie.ac.at/project/scalea/
SCALEA
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Profile/Trace Analysis
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Overhead analysis
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Inclusive/Exclusive Analysis
Load balancing Analysis
Metric Ratio Analysis
Execution Summary
Region to Overhead
Overhead to region
Analysis functions
AKSUM*
*All information regarding AKSUM taken from http://www.par.univie.ac.at/project/aksum/
AKSUM
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Automatic performance bottleneck analysis tool
Performance properties are normalized
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Performance property name
Threshold
Reference code region
Prediction Tools
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P3T
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Carnival
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performance estimator for HPF programs closely integrated with
VFCS
The core part of P3T is centered around a set of parallel program
parameters (transfer time, number of transfers, computation time,
etc.
attempt to automate the cause-and-effect inference process for
performance phenomena
Network Weather Service
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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
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Autopilot
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aims at dynamically optimizing the performance of parallel
applications.
Kappa-PI
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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
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APART - IST Working Group on Automatic Performance Analysis:
Real Tools http://www.fz-juelich.de/apart-2/
Parallel Tools Consortium http://www.ptools.org/
Interesting Ideas
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Tool that facilitate going from one system to another