allen d. malony sameer s. shende robert bell {malony, sameer, bertie}@cs.uoregon.edu department of...

Allen D. Malony Sameer S. Shende Robert Bell {malony, sameer, bertie}@cs.uoregon.edu

Department of Computer and Information Science

Computational Science Institute

University of Oregon

The TAU Performance System

The TAU Performance System Cray Presentation, Berkeley, CA 8/5/032

Overview

Motivation and goals TAU architecture and toolkit

Instrumentation Measurement Analysis

Performance mapping Application case studies

… TAU Integration Work in progress Conclusions


Motivation

Tools for performance problem solving Empirical-based performance optimization process

Versatile performance technology Portable performance analysis methods

characterization

PerformanceTuning

PerformanceDiagnosis

PerformanceExperimentation

PerformanceObservation

hypotheses

properties

• Instrumentation• Measurement• Analysis• Visualization

PerformanceTechnology


Problems

Diverse performance observability requirements Multiple levels of software and hardware Different types and detail of performance data Alternative performance problem solving methods Multiple targets of software and system application

Demands more robust performance technology Broad scope of performance observation Flexible and configurable mechanisms Technology integration and extension Cross-platform portability Open, layered, and modular framework architecture


Complexity Challenges for Performance Tools

Computing system environment complexity Observation integration and optimization Access, accuracy, and granularity constraints Diverse/specialized observation capabilities/technology Restricted modes limit performance problem solving

Sophisticated software development environments Programming paradigms and performance models Performance data mapping to software abstractions Uniformity of performance abstraction across platforms Rich observation capabilities and flexible configuration Common performance problem solving methods


General Problems (Performance Technology)

How do we create robust and ubiquitous performance technology for the analysis and tuning of parallel and distributed software and systems in the presence of (evolving) complexity challenges?

How do we apply performance technology effectively for the variety and diversity of performance problems

that arise in the context of complex parallel and distributed computer systems?


Computation Model for Performance Technology

How to address dual performance technology goals? Robust capabilities + widely available methods Contend with problems of system diversity Flexible tool composition/configuration/integration

Approaches Restrict computation types / performance problems

machines, languages, instrumentation technique, … limited performance technology coverage and application

Base technology on abstract computation model general architecture and software execution features map features/methods to existing complex system types develop capabilities that can be adapted and optimized


General Complex System Computation Model

Node: physically distinct shared memory machine Message passing node interconnection network

Context: distinct virtual memory space within node Thread: execution threads (user/system) in context

memory memory

Node Node Node

VMspace

Context

SMP

Threads

node memory

…

…

Interconnection Network Inter-node messagecommunication

*

*

physicalview

modelview


TAU Performance System

Tuning and Analysis Utilities Performance system framework for scalable parallel and

distributed high-performance computing Targets a general complex system computation model

nodes / contexts / threads Multi-level: system / software / parallelism Measurement and analysis abstraction

Integrated toolkit for performance instrumentation, measurement, analysis, and visualization Portable performance profiling and tracing facility Open software approach with technology integration

University of Oregon , Forschungszentrum Jülich, LANL


Definitions – Profiling

Profiling Recording of summary information during execution

execution time, # calls, hardware statistics, … Reflects performance behavior of program entities

functions, loops, basic blocks user-defined “semantic” entities

Very good for low-cost performance assessment Helps to expose performance bottlenecks and hotspots Implemented through

sampling: periodic OS interrupts or hardware counter traps instrumentation: direct insertion of measurement code


Definitions – Tracing

Tracing Recording of information about significant points (events)

during program execution entering/exiting code region (function, loop, block, …) thread/process interactions (e.g., send/receive message)

Save information in event record timestamp CPU identifier, thread identifier Event type and event-specific information

Event trace is a time-sequenced stream of event records Can be used to reconstruct dynamic program behavior Typically requires code instrumentation


TAU Performance System Architecture

EPILOG

Paraver


TAU Performance Systems Goals

Multi-level performance instrumentation Multi-language automatic source instrumentation

Flexible and configurable performance measurement Widely-ported parallel performance profiling system

Computer system architectures and operating systems Different programming languages and compilers

Support for multiple parallel programming paradigms Multi-threading, message passing, mixed-mode, hybrid

Support for performance mapping Support for object-oriented and generic programming Integration in complex software systems and applications


How To Use TAU?

Instrumentation Application code and libraries Selective instrumentation

Install, compile, and link with TAU measurement library % configure; make clean install Multiple configurations for different measurements options Does not require change in instrumentation Selective measurement control

Execute “experiments” produce performance data Performance data generated at end or during execution

Use analysis tools to look at performance results


TAU Instrumentation Approach

Support for standard program events Routines Classes and templates Statement-level blocks

Support for user-defined events Begin/End events (“user-defined timers”) Atomic events Selection of event statistics

Support definition of “semantic” entities for mapping Support for event groups Instrumentation optimization


TAU Instrumentation

Flexible instrumentation mechanisms at multiple levels Source code

manual automatic

C, C++, F77/90 (Program Database Toolkit (PDT))OpenMP (directive rewriting (Opari))

Object code pre-instrumented libraries (e.g., MPI using PMPI) statically-linked and dynamically-linked fast breakpoints (compiler generated)

Executable code dynamic instrumentation (pre-execution) (DynInstAPI) virtual machine instrumentation (e.g., Java using JVMPI)


Multi-Level Instrumentation

Targets common measurement interface TAU API

Multiple instrumentation interfaces Simultaneously active

Information sharing between interfaces Utilizes instrumentation knowledge between levels

Selective instrumentation Available at each level Cross-level selection

Targets a common performance model Presents a unified view of execution

Consistent performance events


Program Database Toolkit (PDT)

Program code analysis framework develop source-based tools

High-level interface to source code information Integrated toolkit for source code parsing, database

creation, and database query Commercial grade front-end parsers Portable IL analyzer, database format, and access API Open software approach for tool development

Multiple source languages Implement automatic performance instrumentation tools

tau_instrumentor


PDT Architecture and Tools

Application/ Library

C / C++parser

Fortran 77/90parser

C / C++IL analyzer

Fortran 77/90IL analyzer

ProgramDatabase

Files

IL IL

DUCTAPE

PDBhtml

SILOON

CHASM

TAU_instr

Programdocumentation

Applicationcomponent glue

C++ / F90interoperability

Automatic sourceinstrumentation


PDT Components

Language front end Edison Design Group (EDG): C, C++, Java Mutek Solutions Ltd.: F77, F90

IL Analyzer Processes intermediate language (IL) tree from front-end Creates “program database” (PDB) formatted file

DUCTAPE (Bernd Mohr, FZJ/ZAM, Germany) C++ program Database Utilities and Conversion Tools

APplication Environment Processes and merges PDB files C++ library to access the PDB for PDT applications


Instrumentation Control

Selection of which performance events to observe Could depend on scope, type, level of interest Could depend on instrumentation overhead

How is selection supported in instrumentation system? No choice Include / exclude lists (TAU) Environment variables Static vs. dynamic

Controlling the instrumentation of small routines High relative measurement overhead Significant intrusion and possible perturbation


Selective Instrumentation% tau_instrumentor

Usage : tau_instrumentor <pdbfile> <sourcefile> [-o <outputfile>] [-noinline] [-g groupname] [-i headerfile] [-c|-c++|-fortran] [-f <instr_req_file> ]

For selective instrumentation, use –f option

% cat selective.dat

# Selective instrumentation: Specify an exclude/include list.

BEGIN_EXCLUDE_LIST

void quicksort(int *, int, int)

void sort_5elements(int *)

void interchange(int *, int *)

END_EXCLUDE_LIST

# If an include list is specified, the routines in the list will be the only

# routines that are instrumented.

# To specify an include list (a list of routines that will be instrumented)

# remove the leading # to uncomment the following lines

#BEGIN_INCLUDE_LIST

#int main(int, char **)

#int select_

#END_INCLUDE_LIST


Overhead Analysis for Automatic Selection

Analyze the performance data to determine events with high (relative) overhead performance measurements

Create a select list for excluding those events Rule grammar (used in tau_reduce tool)

[GroupName:] Field Operator Number GroupName indicates rule applies to events in group Field is a event metric attribute (from profile statistics)

numcalls, numsubs, percent, usec, cumusec, count, totalcount, stdev, usecs/call, counts/call

Operator is one of >, <, or = Number is any number Compound rules possible using “&” between simple rules


Example Rules

#Exclude all events that are members of TAU_USER #and use less than 1000 microseconds

TAU_USER:usec < 1000 #Exclude all events that have less than 100

#microseconds and are called only onceusec < 1000 & numcalls = 1

#Exclude all events that have less than 1000 usecs per #call OR have a (total inclusive) percent less than 5

usecs/call < 1000percent < 5

Scientific notation can be used


TAU Measurement

Performance information Performance events High-resolution timer library (real-time / virtual clocks) General software counter library (user-defined events) Hardware performance counters

PCL (Performance Counter Library) (ZAM, Germany) PAPI (Performance API) (UTK, Ptools Consortium) consistent, portable API

Organization Node, context, thread levels Profile groups for collective events (runtime selective) Performance data mapping between software levels


TAU Measurement Options

Parallel profiling Function-level, block-level, statement-level Supports user-defined events TAU parallel profile data stored during execution Hardware counts values Support for multiple counters Support for callpath profiling

Tracing All profile-level events Inter-process communication events Timestamp synchronization Trace merging and format conversion


TAU Measurement System Configuration

configure [OPTIONS] {-c++=<CC>, -cc=<cc>} Specify C++ and C compilers {-pthread, -sproc , -smarts} Use pthread, SGI sproc, smarts threads -openmp Use OpenMP threads -opari=<dir> Specify location of Opari OpenMP tool {-papi ,-pcl=<dir> Specify location of PAPI or PCL -pdt=<dir> Specify location of PDT {-mpiinc=<d>, mpilib=<d>} Specify MPI library instrumentation -TRACE Generate TAU event traces -PROFILE Generate TAU profiles -PROFILECALLPATH Generate Callpath profiles (1-level) -MULTIPLECOUNTERS Use more than one hardware counter -CPUTIME Use usertime+system time -PAPIWALLCLOCK Use PAPI to access wallclock time -PAPIVIRTUAL Use PAPI for virtual (user) time …


TAU Measurement API

Initialization and runtime configuration TAU_PROFILE_INIT(argc, argv);

TAU_PROFILE_SET_NODE(myNode);TAU_PROFILE_SET_CONTEXT(myContext);TAU_PROFILE_EXIT(message);TAU_REGISTIER_THREAD();

Function and class methods TAU_PROFILE(name, type, group);

Template TAU_TYPE_STRING(variable, type);

TAU_PROFILE(name, type, group);CT(variable);

User-defined timing TAU_PROFILE_TIMER(timer, name, type, group);

TAU_PROFILE_START(timer);TAU_PROFILE_STOP(timer);


TAU Measurement API (continued)

User-defined events TAU_REGISTER_EVENT(variable, event_name);

TAU_EVENT(variable, value);TAU_PROFILE_STMT(statement);

Mapping TAU_MAPPING(statement, key);

TAU_MAPPING_OBJECT(funcIdVar);TAU_MAPPING_LINK(funcIdVar, key);

TAU_MAPPING_PROFILE (funcIdVar);TAU_MAPPING_PROFILE_TIMER(timer, funcIdVar);TAU_MAPPING_PROFILE_START(timer);TAU_MAPPING_PROFILE_STOP(timer);

Reporting TAU_REPORT_STATISTICS();

TAU_REPORT_THREAD_STATISTICS();


Grouping Performance Data in TAU

Profile Groups A group of related routines forms a profile group Statically defined

TAU_DEFAULT, TAU_USER[1-5], TAU_MESSAGE, TAU_IO, …

Dynamically defined group name based on string, such as “adlib” or “particles” runtime lookup in a map to get unique group identifier uses tau_instrumentor to instrument

Ability to change group names at runtime Group-based instrumentation and measurement control


TAU Group Instrumentation Control API

Enabling Profile Groups TAU_ENABLE_INSTRUMENTATION(); TAU_ENABLE_GROUP(TAU_GROUP); TAU_ENABLE_GROUP_NAME(“group name”); TAU_ENABLE_ALL_GROUPS();

Disabling Profile Groups TAU_DISABLE_INSTRUMENTATION(); TAU_DISABLE_GROUP(TAU_GROUP); TAU_DISABLE_GROUP_NAME(); TAU_DISABLE_ALL_GROUPS();

Obtaining Profile Group Identifier Runtime Switching of Profile Groups


TAU Pre-execution Control

Dynamic groups defined at file scope Group names and group associations runtime modifiable Controlling groups at pre-execution time

--profile <group1+group2+…+groupN> option

% tau_instrumentor app.pdb app.cpp \–o app.i.cpp –g “particles”

% mpirun –np 4 application \–profile particles+field+mesh+io

Examples: POOMA (LANL) uses static groups VTF (Caltech) uses dynamic group in Python-based

execution instrumentation control


Configuring TAU Measurement Library

Profiling with wallclock time (on a quad PIII Linux machine) % configure -mpiinc=/usr/local/packages/mpich/include

-mpilib=/usr/local/packages/mpich/lib -pdt=/usr/pkg/pdtoolkit/ -useropt=-O2 -LINUXTIMERS

Tracing % configure -mpiinc=/usr/local/packages/mpich/include

-mpilib=/usr/local/packages/mpich/lib -pdt=/usr/pkg/pdtoolkit -useropt=-O2 -LINUXTIMERS

Profiling with PAPI % configure -mpiinc=/usr/local/packages/mpich/include

-mpilib=/usr/local/packages/mpich/lib -pdt=/usr/pkg/pdtoolkit/ -useropt=-O2 -papi=/usr/local/packages/papi % setenv PAPI_EVENT PAPI_FP_INS % setenv PAPI_EVENT PAPI_L1_DCM


Compiling with TAU Makefiles Include TAU Stub Makefile (<arch>/lib) in the user’s Makefile Variables:

TAU_CXX Specify the C++ compiler used by TAU TAU_CC, TAU_F90 Specify the C, F90 compilers TAU_DEFS Defines used by TAU. Add to CFLAGS TAU_LDFLAGS Linker options. Add to LDFLAGS TAU_INCLUDE Header files include path. Add to CFLAGS TAU_LIBS Statically linked TAU library. Add to LIBS TAU_SHLIBS Dynamically linked TAU library TAU_MPI_LIBS TAU’s MPI wrapper library for C/C++ TAU_MPI_FLIBS TAU’s MPI wrapper library for F90 TAU_FORTRANLIBS Must be linked in with C++ linker for F90. TAU_DISABLE TAU’s dummy F90 stub library


TAU Analysis

Parallel profile analysis Pprof

parallel profiler with text-based display Racy

graphical interface to pprof (Tcl/Tk) paraprof

Java implementation of Racy

Trace analysis and visualization Trace merging and clock adjustment (if necessary) Trace format conversion (ALOG, SDDF, VTF, Paraver) Trace visualization using Vampir (Pallas)


Pprof Command

pprof [-c|-b|-m|-t|-e|-i] [-r] [-s] [-n num] [-f file] [-l] [nodes] -c Sort according to number of calls -b Sort according to number of subroutines called -m Sort according to msecs (exclusive time total) -t Sort according to total msecs (inclusive time total) -e Sort according to exclusive time per call -i Sort according to inclusive time per call -v Sort according to standard deviation (exclusive

usec) -r Reverse sorting order -s Print only summary profile information -n num Print only first number of functions -f file Specify full path and filename without node ids -l nodes List all functions and exit (prints only info about all

contexts/threads of given node numbers)


Pprof Output (NAS Parallel Benchmark – LU)

Intel QuadPIII Xeon

F90 + MPICH

Profile - Node - Context - Thread

Events - code - MPI


Paraprof (NAS Parallel Benchmark – LU)

n: nodec: contextt: thread

Global profiles Routine profile across all nodes

Event legend

Individual profile


Paraprof Profile Browser


Paraprof Profile Browser Main Window


Paraprof Profile Browser Node Window


Paraprof Profile Browser (Derived Metrics)


Paraprof Profile Browser Routine Window


TAU + PAPI (NAS Parallel Benchmark – LU )

Floating point operations

Re-link to alternate library

Can use multiple counter support


TAU + Vampir (NAS Parallel Benchmark – LU)

Timeline display Callgraph display

Parallelism display

Communications display


tau_reduce Example

tau_reduce implements overhead reduction in TAU Consider klargest example

Find kth largest element in a N elements Compare two methods: quicksort, select_kth_largest

Un-instrumented testcase: i = 2324, N = 1000000 quicksort: (wall clock) = 0.188511 secs select_kth_largest: (wall clock) = 0.149594 secs Total: (PIII/1.2GHz time) = 0.340u 0.020s 0:00.37

Execute with all routines instrumented Execute with rule-based selective instrumentation

usec>1000 & numcalls>400000 & usecs/call<30 & percent>25


Simple sorting example on one processor

NODE 0;CONTEXT 0;THREAD 0:

---------------------------------------------------------------------------------------

%Time Exclusive Inclusive #Call #Subrs Inclusive Name

msec msec usec/call

---------------------------------------------------------------------------------------

100.0 13 4,982 1 4 4982030 int main

93.5 3,223 4,659 4.20241E+06 1.40268E+07 1 void quicksort

62.9 0.00481 3,134 5 5 626839 int kth_largest_qs

36.4 137 1,813 28 450057 64769 int select_kth_largest

33.6 150 1,675 449978 449978 4 void sort_5elements

28.8 1,435 1,435 1.02744E+07 0 0 void interchange

0.4 20 20 1 0 20668 void setup

0.0 0.0118 0.0118 49 0 0 int ceil

Before selective instrumentation reduction

NODE 0;CONTEXT 0;THREAD 0:---------------------------------------------------------------------------------------%Time Exclusive Inclusive #Call #Subrs Inclusive Name msec total msec usec/call---------------------------------------------------------------------------------------100.0 14 383 1 4 383333 int main 50.9 195 195 5 0 39017 int kth_largest_qs 40.0 153 153 28 79 5478 int select_kth_largest 5.4 20 20 1 0 20611 void setup 0.0 0.02 0.02 49 0 0 int ceil

After selective instrumentation reduction


TAU Performance System Status

Computing platforms IBM SP / Power4, SGI Origin 2K/3K, ASCI Red, Cray

T3E / SV-1 (X-1 planned), HP (Compaq) SC (Tru64), HP Superdome (HP-UX), Sun, Hitachi SR8000, NEX SX-5 (SX-6 underway), Linux clusters (IA-32/64, Alpha, PPC, PA-RISC, Power), Apple (OS X), Windows

Programming languages C, C++, Fortran 77, F90, HPF, Java, OpenMP, Python

Communication libraries MPI, PVM, Nexus, shmem, Tulip, ACLMPL, MPIJava

Thread libraries pthreads, SGI sproc, Java,Windows, OpenMP, SMARTS


TAU Performance System Status (continued)

Compilers Intel KAI (KCC, KAP/Pro), PGI, GNU, Fujitsu, Sun,

Microsoft, SGI, Cray, IBM, Compaq, Hitachi, NEC, Intel Application libraries (selected)

Blitz++, A++/P++, PETSc, SAMRAI, Overture, PAWS Application frameworks (selected)

POOMA, MC++, Conejo, Uintah, VTF, UPS, GrACE Performance projects using TAU

Aurora / SCALEA: ACPC, University of Vienna TAU full distribution (Version 2.12, web download)

TAU performance system toolkit and user’s guide Automatic software installation and examples


PDT Status

Program Database Toolkit (Version 2.2, web download) EDG C++ front end (Version 2.45.2) Mutek Fortran 90 front end (Version 2.4.1) C++ and Fortran 90 IL Analyzer DUCTAPE library Standard C++ system header files (KCC Version 4.0f)

PDT-constructed tools TAU instrumentor (C/C++/F90) Program analysis support for SILOON and CHASM

Platforms Same as for TAU with a few exceptions


Performance Mapping

High-level semantic abstractions Associate

performance measurements

Performance mapping performance

measurement system support to assign data correctly


New dynamic mapping scheme (SEAA) Contrast with ParaMap (Miller and Irvin) Entities defined at any level of abstraction Attribute entity with semantic information Entity-to-entity associations

Two association types (implemented in TAU API) Embedded – extends associated

object to store performancemeasurement entity

External – creates an external look-uptable using address of object as key tolocate performance measurement entity

Semantic Entities/Attributes/Associations

…


Hypothetical Mapping Example Particles distributed on surfaces of a cubeParticle* P[MAX]; /* Array of particles */

int GenerateParticles() {

/* distribute particles over all faces of the cube */

for (int face=0, last=0; face < 6; face++){

/* particles on this face */

int particles_on_this_face = num(face);

for (int i=last; i < particles_on_this_face; i++) {

/* particle properties are a function of face */ P[i] = ... f(face);

...

}

last+= particles_on_this_face;

}

}


Hypothetical Mapping Example (continued)

How much time is spent processing face i particles? What is the distribution of performance among faces?

int ProcessParticle(Particle *p) {

/* perform some computation on p */

}

int main() {

GenerateParticles();

/* create a list of particles */

for (int i = 0; i < N; i++)

/* iterates over the list */

ProcessParticle(P[i]);

}

…

engine

workpackets


No Performance Mapping versus Mapping

Typical performance tools report performance with respect to routines

Does not provide support for mapping

Performance tools with SEAA mapping can observe performance with respect to scientist’s programming and problem abstractions

TAU (no mapping) TAU (w/ mapping)


Performance Mapping in Callpath Profiling

Consider callgraph (callpath) profiling Measure time (metric) along an edge (path) of callgraph

Incident edge gives parent / child view Edge sequence (path) gives parent / descendant view

Callpath profiling when callgraph is unknown Must determine callgraph dynamically at runtime Map performance measurement to dynamic call path state

Callpath levels 0-level: current callgraph node 1-level: immediate parent (descendant) k-level: kth calling parent (call descendant)


1-Level Callpath Implementation in TAU

TAU maintains a performance event (routine) callstack Profiled routine (child) looks in callstack for parent

Previous profiled performance event is the parent A callpath profile structure created first time parent calls TAU records parent in a callgraph map for child String representing 1-level callpath used as its key

“a( )=>b( )” : name for time spent in “b” when called by “a” Map returns pointer to callpath profile structure

1-level callpath is profiled using this profiling data Build upon TAU’s performance mapping technology Measurement is independent of instrumentation Use –PROFILECALLPATH to configure TAU


Callpath Profiling Example (NAS LU v2.3)

% configure -PROFILECALLPATH -SGITIMERS -arch=sgi64-mpiinc=/usr/include -mpilib=/usr/lib64 -useropt=-O2


Callpath Parallel Profile Display 0-level and 1-level callpath grouping

1-Level Callpath0-Level Callpath


Strategies for Empirical Performance Evaluation

Empirical performance evaluation as a series of performance experiments Experiment trials describing instrumentation and

measurement requirements Where/When/How axes of empirical performance space

where are performance measurements made in program when is performance instrumentation done how are performance measurement/instrumentation chosen

Strategies for achieving flexibility and portability goals Limited performance methods restrict evaluation scope Non-portable methods force use of different techniques Integration and combination of strategies


Case Study: SIMPLE Performance Analysis

SIMPLE hydrodynamics benchmark C code with MPI message communication Multiple instrumentation methods

source-to-source translation (PDT) MPI wrapper library level instrumentation (PMPI) pre-execution binary instrumentation (DyninstAPI)

Alternative measurement strategies statistical profiles of software actions statistical profiles of hardware actions (PCL, PAPI) program event tracing choice of time source

gettimeofday, high-res physical, CPU, process virtual


PDT automatically generates instrumentation code names events with full function signatures

Similarly for all other routines in SIMPLE program

SIMPLE Source Instrumentation (Preprocessed)

int compute_heat_conduction(double theta_hat[X][Y], double deltat, double new_r[X][Y], double new_z[X][Y], double new_alpha[X][Y],

double new_rho[X][Y], double theta_l[X][Y],double Gamma_k[X][Y], double Gamma_l[X][Y])

{

TAU_PROFILE("int compute_heat_conduction(double (*)[259], double, double (*)[259],double (*)[259], double (*)[259], double (*)[259],

double (*)[259], double (*)[259], double (*)[259])"," ", TAU_USER);

...}


int MPI_Send(…)...{

int returnVal, typesize;TAU_PROFILE_TIMER(tautimer, "MPI_Send()", " ", TAU_MESSAGE);TAU_PROFILE_START(tautimer);if (dest != MPI_PROC_NULL) {

PMPI_Type_size(datatype, &typesize);

TAU_TRACE_SENDMSG(tag, dest, typesize*count);}returnVal = PMPI_Send(buf, count, datatype, dest, tag, comm);TAU_PROFILE_STOP(tautimer); return returnVal;

}

MPI Library Instrumentation (MPI_Send)

Uses MPI profiling interposition library (PMPI)


MPI Library Instrumentation (MPI_Recv)

int MPI_Recv(…)...{

int returnVal, size;TAU_PROFILE_TIMER(tautimer, "MPI_Recv()", " ", TAU_MESSAGE);TAU_PROFILE_START(tautimer);returnVal = PMPI_Recv(buf, count, datatype, src, tag, comm,

status);if (src != MPI_PROC_NULL && returnVal == MPI_SUCCESS) {

PMPI_Get_count( status, MPI_BYTE, &size );TAU_TRACE_RECVMSG(status->MPI_TAG, status->MPI_SOURCE,

size);}TAU_PROFILE_STOP(tautimer); return returnVal;

}


Multi-Level Instrumentation (Profiling)

four processes

Profile per process

global profile

event legend


Multi-Level Instrumentation (Tracing)

Relink with TAU library configured for tracing No modification of source instrumentation required!

TAU performance groups


Dynamic Instrumentation of SIMPLE

Uses DynInstAPI for runtime code patching Mutator loads measurement library, instruments mutatee

One mutator (tau_run) per executable image mpirun –np <n> tau.shell


Case Study: PETSc v2.1.3 (ANL)

Portable, Extensible Toolkit for Scientific Computation Scalable (parallel) PDE framework

Suite of data structures and routines (374,458 code lines) Solution of scientific applications modeled by PDEs

Parallel implementation MPI used for inter-process communication

TAU instrumentation PDT for C/C++ source instrumentation (100%, no manual) MPI wrapper interposition library instrumentation

Example Linear system of equations (Ax=b) (SLES) (ex2 test case) Non-linear system of equations (SNES) (ex19 test case)


PETSc ex2 (Profile - wallclock time)

Sorted with respect to exclusive time


PETSc ex2(Profile - overall and message counts) Observe

load balance

Track messages

Capture with user-defined events


PETSc ex2 (Profile - percentages and time)

View per threadperformance on individual routines


PETSc ex2 (Trace)


PETSc ex19

Non-linear solver (SNES) 2-D driven cavity code Uses velocity-vorticity formulation Finite difference discretization on a structured grid

Problem size and measurements 56x56 mesh size on quad Pentium III (550 Mhz, Linux) Executes for approximately one minute MPI wrapper interposition library PDT (tau_instrumentor) Selective instrumentation (tau_reduce)

three routines identified with high instrumentation overhead


PETSc ex19 (Profile - wallclock time)

Sorted by inclusive time

Sorted by exclusive time


PETSc ex19 (Profile - overall and percentages)


PETSc ex19 (Tracing)

Commonly seen communicaton behavior


PETSc ex19 (Tracing - callgraph)


PETSc ex19 (PAPI_FP_INS, PAPI_L1_DCM)

PAPI_FP_INS

PAPI_L1_DCM

Uses multiple counter profile measurement


Case Study: Mixed-mode Parallel Programs

Portable mixed-mode parallel programming Multi-threaded shared memory programming Inter-node message passing

Performance measurement Access to runtime system and communication events Associate communication and application events

2-Dimensional Stommel model of ocean circulation OpenMP for shared memory parallel programming MPI for cross-box message-based parallelism Jacobi iteration, 5-point stencil Timothy Kaiser (San Diego Supercomputing Center)


Stommel Instrumentation

OpenMP directive instrumentation (uses OPARI)

pomp_for_enter(&omp_rd_2);

#line 252 "stommel.c"

#pragma omp for schedule(static) reduction(+: diff) private(j) firstprivate (a1,a2,a3,a4,a5) nowait

for( i=i1;i<=i2;i++) {

for(j=j1;j<=j2;j++){

new_psi[i][j]=a1*psi[i+1][j] + a2*psi[i-1][j] + a3*psi[i][j+1]

+ a4*psi[i][j-1] - a5*the_for[i][j];

diff=diff+fabs(new_psi[i][j]-psi[i][j]);

}

}

pomp_barrier_enter(&omp_rd_2);

#pragma omp barrier

pomp_barrier_exit(&omp_rd_2);

pomp_for_exit(&omp_rd_2);

#line 261 "stommel.c"


OpenMP + MPI Ocean Modeling (Trace)

Thread-paired message passing

Integrated OpenMP + MPI events


OpenMP + MPI Ocean Modeling (HW Profile)

% configure -papi=../packages/papi -openmp -c++=pgCC -cc=pgcc -mpiinc=../packages/mpich/include -mpilib=../packages/mpich/lib


FP instructions



Case Study: C++ and Performance Mapping

Object-oriented programming abstract data types, encapsulation, inheritance, …

Domain-specific abstractions Implemented by OO languages in form of class libraries

Generic programming mechanisms efficient coding abstractions, compile-time transformations

Creates a semantic gap between the transformed code and what the user expects (as describes in source code)

Need a mechanism to expose the nature of high-level abstract computation to the performance tools

Map low-level performance data to high-level semantics


C++ Template Instrumentation (Blitz++, PETE)

High-level objects Array classes Templates (Blitz++)

Optimizations Array processing Expressions (PETE)

Relate performance data to high-level statement

Complexity of template evaluation

ArrayexpressionsArrayexpressions


Standard Template Instrumentation Difficulties

Instantiated templates result in mangled identifiers Standard profiling techniques / tools are deficient

Integrated with proprietary compilers Specific systems platforms and programming models

Very long! Uninterpretable routine names


Blitz++ Library Instrumentation

Expression templates embed the form of the expression in a template name

Blitz++ describes structure of the expression template Present as pretty printed name to the profiling toolkit

Create performance event associated with expression type

+

B -

+

2.0 D

C

BinOp<Add, B, <BinOp<Subtract, C, <BinOp<Multiply, Scalar<2.0>, D>>>

Expression: B + C - 2.0 * D


Blitz++ Library Instrumentation (example)

#ifdef BZ_TAU_PROFILINGstatic string exprDescription;if (!exprDescription.length()) {exprDescription = "A";prettyPrintFormat format(_bz_true); // terse mode onformat.nextArrayOperandSymbol();T_update::prettyPrint(exprDescription);expr.prettyPrint(exprDescription, format);

}TAU_PROFILE(" ", exprDescription, TAU_BLITZ);#endif

exprDescription is the event name


TAU Instrumentation and Profiling for C++

Performance data presented with respect to high-level array expression types

Profile of expression types

Performance data presented with respect to high-level array expression types


Case Study: C-SAFE / Uintah

Center for Simulation of Accidental Fires & Explosions ASCI ASAP Level 1 center, University of Utah PSE for multi-model simulation high-energy explosion Coupled non-linear solvers, optimization, computational

steering, visualization, and experimental data verification Very large-scale simulations

Computer science problems: Coupling of multiple simulation codes Software engineering across diverse expert teams Achieving high performance on large-scale systems


Example C-SAFE Simulation Problems

∑Heptane fire simulation

Material stress simulation

Typical C-SAFE simulation with a billion degrees of freedom and non-linear time dynamics


Uintah Computational Framework (UCF)

Execution model based on software (macro) dataflow Exposes parallelism and hides data transport latency Computations expressed a directed acyclic graphs of tasks

consumes input and produces output (input to future task) input/outputs specified for each patch in a structured grid

Abstraction of global single-assignment memory DataWarehouse Directory mapping names to values (array structured) Write value once then communicate to awaiting tasks

Task graph gets mapped to processing resources Communications schedule approximates global optimal


Performance Technology Integration

Uintah present challenges to performance integration Software diversity and structure

UCF middleware, simulation code modules component-based hierarchy

Portability objectives cross-language and cross-platform multi-parallelism: thread, message passing, mixed

Scalability objectives High-level programming and execution abstractions

Requires flexible and robust performance technology Requires support for performance mapping


Task Execution in Uintah Parallel Scheduler

Profile methods and functions in scheduler and in MPI library

Need to map performance data!

Task execution time dominates (what task?)

MPI communication overheads (where?)

Task execution time distribution


Uintah Task Performance Mapping Uintah partitions individual particles across processing

elements (processes or threads) Simulation tasks in task graph work on particles

Tasks have domain-specific character in the computation “interpolate particles to grid” in Material Point Method

Task instances generated for each partitioned particle set Execution scheduled with respect to task dependencies

How to attributed execution time among different tasks Assign semantic name (task type) to a task instance

SerialMPM::interpolateParticleToGrid Map TAU timer object to (abstract) task (semantic entity) Look up timer object using task type (semantic attribute)

Further partition along different domain-specific axes


Mapping Instrumentation in UCF (example)

Use TAU performance mapping APIvoid MPIScheduler::execute(const ProcessorGroup * pc,

DataWarehouseP & old_dw, DataWarehouseP & dw ) {

...TAU_MAPPING_CREATE(

task->getName(), "[MPIScheduler::execute()]", (TauGroup_t)(void*)task->getName(), task->getName(), 0);...TAU_MAPPING_OBJECT(tautimer)TAU_MAPPING_LINK(tautimer,(TauGroup_t)(void*)task->getName());

// EXTERNAL ASSOCIATION...TAU_MAPPING_PROFILE_TIMER(doitprofiler, tautimer, 0)TAU_MAPPING_PROFILE_START(doitprofiler,0);task->doit(pc);TAU_MAPPING_PROFILE_STOP(0);...

}


Task Performance Mapping (Profile)

Mapped task performance across processes

Performance mapping for different tasks


Work Packet – to – Task Mapping (Trace)

Work packet computation events colored by task type

Distinct phases of computation can be identifed based on task


Comparing Uintah Traces for Scalability Analysis

8 processes

8 processes

32 processes32 processes

32 processes


Online Performance Analysis for C-SAFE Apps

ApplicationPerformance

Steering PerformanceVisualizer

PerformanceAnalyzer

PerformanceData Reader

TAUPerformance

System

PerformanceData Integrator

SCIRun (Univ. of Utah)

// performancedata streams

// performancedata output

file system

• sample sequencing• reader synchronization

accumulatedsamples


2D Field Performance Visualization in SCIRun

SCIRun program


Uintah Computational Framework (UCF)

UCF analysis Scheduling MPI library Components

500 processes Online

and offlinevisualization

Performancesteering use SCIRun

support

The TAU Performance System SC2002 PERC Tutorial, Nov. 17, 2002102

Case Study: SAMRAI (LLNL)

Structured Adaptive Mesh Refinement Application Infrastructure (SAMRAI)

Programming C++ and MPI SPMD

Instrumentation PDT for automatic instrumentation of routines MPI interposition wrappers SAMRAI timers for interesting code segments

timers classified in groups (apps, mesh, …) timer groups are managed by TAU groups


SAMRAI (Profile)

Euler (2D)

return type routine name


SAMRAI Euler (Profile)


SAMRAI Euler (Trace)


Case Study: EVH1

Enhanced Virginia Hydrodynamics #1 (EVH1) "TeraScale Simulations of Neutrino-Driven Supernovae

and Their Nucleosynthesis" SciDAC project Configured to run a simulation of the Sedov-Taylor blast

wave solution in 2D spherical geometry Performance study found EVH1 communication bound

for more than 64 processors Predominant routine (>50% of execution time) at this

scale is MPI_ALLTOALL Used in matrix transpose-like operations


EVH1 Execution Profile


EVH1 Execution Trace

MPI_Alltoall is an execution bottleneck


TAU Integration (Selected)

SAMRAI (LLNL) Overture (LLNL) C-SAFE (ASCI ASAP) VTF (ASCI ASAP) SAGE (ASCI LANL) POOMA, POOMA-II (LANL, Code Sourcery) PETSc (ANL) CCA (DOE SciDAC) GrACE (Rutgers) Aurora / SCALEA (University of Vienna)


Work in Progress

Trace visualization Event traces with counters (Vampir 3.0 will visualize) EPILOG trace conversion

Runtime performance monitoring and analysis Online performance data access Performance analysis and visualization in SCIRun

Performance Database Framework XML parallel profile representation of TAU profiles PostgresSQL performance database

Next-generation PDT Performance analysis for component software (CCA)


Concluding Remarks

Complex software and parallel computing systems pose challenging performance analysis problems that require robust methodologies and tools

To build more sophisticated performance tools, existing proven performance technology must be utilized

Performance tools must be integrated with software and systems models and technology Performance engineered software Function consistently and coherently in software and

system environments TAU performance system offers robust performance

technology that can be broadly integrated … so USE IT!


Acknowledgements Department of Energy (DOE)

MICS office DOE 2000 ACTS contract “Performance Technology for Tera-class Parallel Computer

Systems: Evolution of the TAU Performance System” PERC SciDAC project affiliate

University of Utah DOE ASCI Level 1 sub-contract DOE ASCI Level 3 (LANL, LLNL)

NSF National Young Investigator (NYI) award Research Centre Juelich

John von Neumann Institute for Computing Dr. Bernd Mohr

Los Alamos National Laboratory


Information

TAU (http://www.acl.lanl.gov/tau) PDT (http://www.acl.lanl.gov/pdtoolkit) PAPI (http://icl.cs.utk.edu/projects/papi/) OPARI (http://www.fz-juelich.de/zam/kojak/)

allen d. malony sameer s. shende robert bell {malony, sameer, bertie}@cs.uoregon.edu department of...

Documents

tau performance system

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goals tau architecture

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