Loading...

PPT – HIGH PERFORMANCE COMPUTING: MODELS, METHODS, PowerPoint presentation | free to view - id: 71f9ef-NjY1Z

The Adobe Flash plugin is needed to view this content

HIGH PERFORMANCE COMPUTING MODELS, METHODS,

MEANSBENCHMARKING

- Prof. Thomas Sterling
- Department of Computer Science
- Louisiana State University
- January 27, 2011

Topics

- Definitions, properties and applications
- Early benchmarks
- Linpack
- Other parallel benchmarks
- Organized benchmarking
- Presentation and interpretation of results
- Summary

Topics

- Definitions, properties and applications
- Early benchmarks
- Linpack
- Other parallel benchmarks
- Organized benchmarking
- Presentation and interpretation of results
- Summary

Basic Performance Metrics

- Time related
- Execution time seconds
- wall clock time
- system and user time
- Latency
- Response time
- Rate related
- Rate of computation
- floating point operations per second flops
- integer operations per second ops
- Data transfer (I/O) rate bytes/second

- Effectiveness
- Efficiency
- Sustained perf/peak perf
- Memory consumption bytes
- Productivity utility/(second)
- Performance measures
- Sustained Performance
- Peak Performance
- Benchmark sustained perf
- HPL Rmax

What Is a Benchmark?

Benchmark a standardized problem or test that

serves as a basis for evaluation or comparison

(as of computer system performance)

Merriam-Webster

- The term benchmark also commonly applies to

specially-designed programs used in benchmarking - A benchmark should
- be domain specific (the more general the

benchmark, the less useful it is for anything in

particular) - be a distillation of the essential attributes of

a workload - avoid using single metric to express the overall

performance - Computational benchmark kinds
- synthetic specially-created programs that impose

the load on the specific component in the system - application derived from a real-world

application program

Purpose of Benchmarking

- Provide a tool, enabling quantitative comparisons
- Comparison of variations within the same system
- Comparison of distinct systems
- Driving progress
- enable better engineering by defining measurable

and repeatable objectives - Establishing of performance agenda
- measure release-to-release or version-to-version

progress - set goals to meet
- be understandable and useful also to the people

not having the expertise in the field (managers,

etc.)

Properties of a Good Benchmark

- Relevance meaningful within the target domain
- Understandability
- Good metric(s) linear, orthogonal, monotonic
- Scalability applicable to a broad spectrum of

hardware/architecture - Coverage does not over-constrain the typical

environment (does not require any special

conditions) - Acceptance embraced by users and vendors
- Has to enable comparative evaluation

Adapted from Standard Benchmarks for Database

Systems by Charles Levine, SIGMOD 97

Topics

- Definitions, properties and applications
- Early benchmarks
- Linpack
- Other parallel benchmarks
- Organized benchmarking
- Presentation and interpretation of results
- Summary

Early Benchmarks

- Whetstone
- Floating point intensive
- Dhrystone
- Integer and character string oriented
- Livermore Fortran Kernels
- Livermore Loops
- Collection of short kernels
- NAS kernel
- 7 Fortran test kernels for aerospace computation
- The sources of the benchmarks listed above are

available from http//www.netlib.org/benchmark

Whetstone

- Originally written in Algol 60 in 1972 at the

National Physics Laboratory (UK) - Named after Whetstone Algol translator-interpreter

on the KDF9 computer - Measures primarily floating point performance in

WIPS Whetstone Instructions Per Second - Raised also the issue of efficiency of different

programming languages - The original Algol code was translated to C and

Fortran (single and double precision support),

PL/I, APL, Pascal, Basic, Simula and others

Dhrystone

- Synthetic benchmark developed in 1984 by Reinhold

Weicker - The name is a pun on Whetstone
- Measures integer and string operations

performance, expressed in number of iterations,

or Dhrystones, per second - Alternative unit D-MIPS, normalized to VAX

11/780 performance - Latest version released 2.1, includes

implementations in C, Ada and Pascal - Superseded by SPECint suite

Gordon Bell and VAX 11/780

Livermore Fortran Kernels (LFK)

- Developed at Lawrence Livermore National

Laboratory in 1970 - also known as Livermore Loops
- Consists of 24 separate kernels
- hydrodynamic codes, Cholesky conjugate gradient,

linear algebra, equation of state, integration,

predictors, first sum and difference, particle in

cell, Monte Carlo, linear recurrence, discrete

ordinate transport, Planckian distribution and

others - include careful and careless coding practices
- Produces 72 timing results using 3 different

DO-loop lengths for each kernel - Produces Megaflops values for each kernel and

range statistics of the results - Can be used as performance, compiler accuracy

(checksums stored in code) or hardware endurance

test

NAS Kernel

- Developed at the Numerical Aerodynamic Simulation

Projects Office at NASA Ames - Focuses on vector floating point performance
- Consists of 7 test kernels in Fortran (approx.

1000 lines of code) - matrix multiply
- complex 2-D FFT
- Cholesky decomposition
- block tri-diagonal matrix solver
- vortex method setup with Gaussian elimination
- vortex creation with boundary conditions
- parallel inverse of three matrix pentadiagonals
- Reports performance in Mflops (64-bit precision)

Topics

- Definitions, properties and applications
- Early benchmarks
- Linpack
- Other parallel benchmarks
- Organized benchmarking
- Presentation and interpretation of results
- Summary

Linpack Overview

- Introduced by Jack Dongarra in 1979
- Based on LINPACK linear algebra package developed

by J. Dongarra, J. Bunch, C. Moler and P. Stewart

(now superseded by the LAPACK library) - Solves a dense, regular system of linear

equations, using matrices initialized with

pseudo-random numbers - Provides an estimate of systems effective

floating-point performance - Does not reflect the overall performance of the

machine!

Linpack Benchmark Variants

- Linpack Fortran (single processor)
- N100
- N1000, TPP, best effort
- Linpacks Highly Parallel Computing benchmark

(HPL) - Java Linpack

Fortran Linpack (I)

- N100 case
- Provides results listed in Table 1 of Linpack

Benchmark Report - Absolutely no changes to the code can be made

(not even in comments!) - Matrix generated by the program must be used to

run this case - An external timing function (SECOND) has to be

supplied - Only compiler-induced optimizations allowed
- Measures performance of two routines
- DGEFA LU decomposition with partial pivoting
- DGESL solves system of linear equations using

result from DGEFA - Complexity O(n2) for DGESL, O(n3) for DGEFA

Fortran Linpack (II)

- N1000 case, Toward Peak Performance (TPP), Best

Effort - Provides results listed in Table 1 of Linpack

Benchmark Report - The user can choose any linear equation to be

solved - Allows a complete replacement of the

factorization/solver code by the user - No restriction on the implementation language for

the solver - The solution must conform to prescribed accuracy

and the matrix used must be the same as the

matrix used by the netlib driver

Linpack Fortran Performance on Different Platforms

Computer N100 MFlops N1000, TPP MFlops Theoretical Peak MFlops

Intel Pentium Woodcrest (1core, 3 GHz) 3018 6542 12000

NEC SX-8/8 (8 proc., 2 GHz) - 75140 128000

NEC SX-8/8 (1 proc., 2 GHz) 2177 14960 16000

HP ProLiant BL20p G3 (4 cores, 3.8 GHz Intel Xeon) - 8185 14800

HP ProLiant BL20p G3 (1 core 3.8 GHz Intel Xeon) 1852 4851 7400

IBM eServer p5-575 (8 POWER5 proc., 1.9 GHz) - 34570 60800

IBM eServer p5-575 (1 POWER5 proc., 1.9 GHz) 1776 5872 7600

SGI Altix 3700 Bx2 (1 Itanium2 proc., 1.6 GHz) 1765 5953 6400

HP ProLiant BL45p (4 cores AMD Opteron 854, 2.8 GHz) - 12860 22400

HP ProLiant BL45p (1 core AMD Opteron 854, 2.8 GHz) 1717 4191 5600

Fujitsu VPP5000/1 (1 proc., 3.33ns) 1156 8784 9600

Cray T932 (32 proc., 2.2ns) 1129 (1 proc.) 29360 57600

HP AlphaServer GS1280 7/1300 (8 Alpha proc., 1.3GHz) - 14260 20800

HP AlphaServer GS1280 7/1300 (1 Alpha proc., 1.3GHz) 1122 2132 2600

HP 9000 rp8420-32 (8 PA-8800 proc., 1000MHz) - 14150 32000

HP 9000 rp8420-32 (1 PA-8800 proc., 1000MHz) 843 2905 4000

Data excerpted from the 11-30-2006 LINPACK

Benchmark Report at http//www.netlib.org/benchmar

k/performance.ps

Fortran Linpack Demo

- gt ./linpack
- Please send the results of this run to
- Jack J. Dongarra
- Computer Science Department
- University of Tennessee
- Knoxville, Tennessee 37996-1300
- Fax 865-974-8296
- Internet dongarra_at_cs.utk.edu
- This is version 29.5.04.
- norm. resid resid machep

x(1) x(n) - 1.25501937E00 1.39332990E-14 2.22044605E-16

1.00000000E00 1.00000000E00 - times are reported for matrices of order

100 - dgefa dgesl total mflops

unit ratio b(1)

Total time (dgefadgesl)

Timing unit (obsolete)

First element of right hand side vector

Time spent in solver (dgesl)

Fraction of Cray-1S execution time (obsolete)

Sustained floating point rate

Time spent in matrix factorization routine (dgefa)

Two different dimensions used to test the effect

of array placement in memory

Reference http//www.netlib.org/utk/people/JackDo

ngarra/faq-linpack.html

Linpacks Highly Parallel Computing Benchmark

(HPL)

- Measures the performance of distributed memory

machines - Used in the Linpack Benchmark Report (Table 3)

and to determine the order of machines on the

Top500 list - The portable version (written in C)
- External dependencies for Linpack installation
- MPI-1.1 functionality for inter-node

communication - BLAS or VSIPL library for simple vector

operations such as scaled vector addition (DAXPY

y axy) and inner dot product (DDOT a Sxiyi) - Ground rules
- allows a complete user replacement of the LU

factorization and solver steps (the accuracy must

satisfy given bound) - same matrix as in the driver program
- no restrictions on problem size

HPL Algorithm

- Data distribution 2-D block-cyclic
- Algorithm elements
- right-looking variant of LU factorization with

row partial pivoting featuring multiple

look-ahead depths - recursive panel factorization with pivot search

and column broadcast combined - various virtual panel broadcast topologies
- bandwidth reducing swap-broadcast algorithm
- backward substitution with look-ahead depth of

one - Floating point operation count 2/3n3n2

HPL Algorithm Elements

Execution flow for single parameter set

Matrix Generation

Panel Factorization

Panel Broadcast

Look-ahead

Update

All columns of A processed?

N

Y

Backward Substitution

Six broadcast algorithms available

Solution Check

http//www.netlib.org/benchmark/hpl/algorithm.html

HPL Linpack Metrics

- The HPL implementation of the benchmark is run

for different problem sizes N on the entire

machine - For certain problem size Nmax, the cumulative

performance in Mflops (reflecting 64-bit addition

and multiplication operations) reaches its

maximum value denoted as Rmax - Another metric possible to obtain from the

benchmark is N1/2, the problem size for which the

half of the maximum performance (Rmax/2) is

achieved - The Rmax value is used to rank supercomputers in

Top500 list listed along with this number are

the theoretical peak double precision floating

point performance Rpeak of the machine and N1/2

Machine Parameters Influencing Linpack Performance

Parameter Linpack Fortran, N100 Linpack Fortran, N1000, TPP HPL

Processor speed Yes Yes Yes

Memory capacity No No (modern system) Yes (for Rmax)

Network latency/bandwidth No No Yes

Compiler flags Yes Yes Yes

Ten Fastest Supercomputers On Current Top500 List

Source http//www.top500.org/sublist

Java Linpack

- Intended mostly to measure the efficiency of Java

implementation rather than hardware floating

point performance - Solves a dense 500x500 system of linear equations

with one right-hand side, Axb - Matrix A is generated randomly
- Vector b is constructed, so that all component of

solution x are one - Uses Gaussian elimination with partial pivoting
- Reports Mflops, time to solution, Norm Res

(solution accuracy), relative machine precision

HPL Output Example

gt mpirun -np 4 xhpl

HPL

inpack 1.0a -- High-Performance Linpack

benchmark -- January 20, 2004 Written by A.

Petitet and R. Clint Whaley, Innovative

Computing Labs., UTK

An explanation of the input/output parameters

follows T/V Wall time / encoded variant. N

The order of the coefficient matrix A. NB

The partitioning blocking factor. P

The number of process rows. Q The number

of process columns. Time Time in seconds to

solve the linear system. Gflops Rate of

execution for solving the linear system. The

following parameter values will be used N

5000 NB 32 PMAP Row-major

process mapping P 2 1

4 Q 2 4 1 PFACT

Left NBMIN 2 NDIV 2 RFACT

Left BCAST 1ringM DEPTH 0

SWAP Mix (threshold 64) L1

transposed form U transposed form EQUIL

yes ALIGN 8 double precision

words -------------------------------------------

--------------------------------- - The matrix A

is randomly generated for each test. - The

following scaled residual checks will be

computed 1) Ax-b_oo / ( eps A_1

N ) 2) Ax-b_oo / ( eps A_1

x_1 ) 3) Ax-b_oo / ( eps A_oo

x_oo ) - The relative machine precision (eps)

is taken to be 1.110223e-16 -

Computational tests pass if scaled residuals are

less than 16.0

T/V N

NB P Q Time

Gflops -------------------------------------------

--------------------------------- WR01L2L2

5000 32 2 2 7.14

1.168e01 ---------------------------------------

------------------------------------- Ax-b_oo

/ ( eps A_1 N )

0.0400275 ...... PASSED Ax-b_oo / ( eps

A_1 x_1 ) 0.0264242 ......

PASSED Ax-b_oo / ( eps A_oo x_oo

) 0.0051580 ...... PASSED

T/V N NB P

Q Time

Gflops -------------------------------------------

--------------------------------- WR01L2L2

5000 32 1 4 7.00

1.192e01 ---------------------------------------

------------------------------------- Ax-b_oo

/ ( eps A_1 N )

0.0335428 ...... PASSED Ax-b_oo / ( eps

A_1 x_1 ) 0.0221433 ......

PASSED Ax-b_oo / ( eps A_oo x_oo

) 0.0043224 ...... PASSED

T/V N NB P

Q Time

Gflops -------------------------------------------

--------------------------------- WR01L2L2

5000 32 4 1 7.00

1.191e01 ---------------------------------------

------------------------------------- Ax-b_oo

/ ( eps A_1 N )

0.0426255 ...... PASSED Ax-b_oo / ( eps

A_1 x_1 ) 0.0281393 ......

PASSED Ax-b_oo / ( eps A_oo x_oo

) 0.0054928 ...... PASSED

Finished 3 tests with the

following results 3 tests

completed and passed residual checks,

0 tests completed and failed residual checks,

0 tests skipped because of illegal

input values. ------------------------------------

---------------------------------------- End of

Tests.

For configuration issues, consult

http//www.netlib.org/benchmark/hpl/faqs.html

Topics

- Definitions, properties and applications
- Early benchmarks
- Linpack
- Other parallel benchmarks
- Organized benchmarking
- Presentation and interpretation of results
- Summary

Other Parallel Benchmarks

- High Performance Computing Challenge (HPCC)

benchmarks - Devised and sponsored to enrich the benchmarking

parameter set - NAS Parallel Benchmarks (NPB)
- Powerful set of metrics
- Reflects computational fluid dynamics
- NPBIO-MPI
- Stresses external I/O system

HPC Challenge Benchmark

- Consists of 7 individual tests
- HPL (Linpack TPP) floating point rate of

execution of a solver of linear system of

equations - DGEMM floating point rate of execution of double

precision matrix-matrix multiplication - STREAM sustainable memory bandwidth (GB/s) and

the corresponding computation rate for simple

vector kernel - PTRANS (parallel matrix transpose) total

capacity of the network using pairwise

communicating processes - RandomAccess the rate of integer random updates

of memory (in GUPS Giga-Updates Per Second) - FFT floating point rate of execution of double

precision complex 1-D Discrete Fourier Transform - b_eff (effective bandwidth benchmark) latency

and bandwidth of a number of simultaneous

communication patterns

Comparison of HPCC Results on Selected

Supercomputers

- Notes
- all metrics shown are higher-better, except

for the Random Ring Latency - machine labels include machine name (optional),

manufacturer and system name, affiliation and (in

parentheses) - processor/network fabric type

NAS Parallel Benchmarks

- Derived from computational fluid dynamics (CFD)

applications - Consist of five kernels and three

pseudo-applications - Exist in several flavors
- NPB 1 original paper-and-pencil specification
- generally proprietary implementations by hardware

vendors - NPB 2 MPI-based sources distributed by NAS
- supplements NPB 1
- can be run with little or no tuning
- NPB 3 implementations in OpenMP, HPF and Java
- derived from NPB-serial version with improved

serial code - a set of multi-zone benchmarks was added
- test implementation efficiency of multi-level and

hybrid parallelization methods and tools (e.g.

OpenMP with MPI) - GridNPB 3 new suite of benchmarks, designed to

rate the performance of computational grids - includes only four benchmarks, derived from the

original NPB - written in Fortran and Java
- Globus as grid middleware

NPB 2 Overview

- Multiple problem classes (S, W, A, B, C, D)
- Tests written mainly in Fortran (IS in C)
- BT (block tri-diagonal solver with 5x5 block

size) - CG (conjugate gradient approximation to compute

the smallest eigenvalue of a sparse, symmetric

positive definite matrix) - EP (embarrassingly parallel evaluates an

integral by means of pseudorandom trials) - FT (3-D PDE solver using Fast Fourier Transforms)
- IS (large integer sort tests both integer

computation speed and network performance) - LU (a regular-sparse, 5x5 block lower and upper

triangular system solver) - MG (simplified multigrid kernel tests both short

and long distance data communication) - SP (solves multiple independent system of

non-diagonally dominant, scalar, pentadiagonal

equations) - Sources and reports available from

http//ww.nas.nasa.gov/Resources/Software/npb.html

NPBIO-MPI

- Attempts to address lack of I/O tests in NPB,

focusing primarily on file output - Based on BTIO (Block Tridiagonal Input Output)

effort, which extended BT (Block-tridiagonal)

benchmark with routines writing to storage five

double precision numbers for every mesh point - runs for 200 iterations, writing every five

iterations - after all time steps are finished, all data

belonging to a single time step must be stored in

the same file, sorted by vector components - timing must include all required data

rearrangements to achieve the specified data

layout - Supported access scenarios
- simple MPI-IO without collective buffering
- full MPI-IO collective buffering
- fortran Fortran 77 file operations
- epio where each process writes continuously its

part of the computational domain to a separate

file - Number of processes must be a square
- Problem sizes class A (643), class B (1023),

class C (1623) - Several possible results, depending on the

benchmarking goal effective flops, effective

output bandwidth or output overhead

Sample NPB 2 Results

Reference The NAS Parallel Benchmarks 2.1

Results by W. Saphir, A. Woo, and M. Yarrow

http//www.nas.nasa.gov/News/Techreports/1996/PDF/

nas-96-010.pdf

Topics

- Definitions, properties and applications
- Early benchmarks
- Linpack
- Other parallel benchmarks
- Organized benchmarking
- Presentation and interpretation of results
- Summary

Benchmarking Organizations

- SPEC (Standard Performance Evaluation

Corporation) - Created to satisfy the need for realistic, fair

and standardized performance tests - Motto An ounce of honest data is worth more

than a pound of marketing hype - TPC (Transaction Processing Performance Council)
- Formed primarily due to lack of reliable database

benchmarks

SPEC Benchmark Suite Overview

- Standard Performance Evaluation Corporation

(SPEC) is a non-profit organization (financed by

its members over 60 leading computer and

software manufacturers) founded in 1988 - SPEC benchmarks are written in platform-neutral

language (typically C or Fortran) - The code may be compiled using arbitrary

compilers, but the sources may not be modified - many manufacturers are known to optimize their

compilers and/or systems to improve the SPEC

results - Benchmarks may be obtained by purchasing the

license from SPEC the results are published on

the SPEC website - Website http//www.spec.org

SPEC Suite Components

- SPEC CPU2006 combined performance of CPU, memory

and compiler - CINT2006 (aka. SPECint) integer arithmetic test

using compilers, interpreters, word processors,

chess programs, etc. - CFP2006 (aka. SPECfp) floating point test using

physical simulations, 3D graphics, image

processing, computational chemistry, etc. - SPECweb2005 PHP/JSP performance
- SPECviewperf OpenGL 3D graphic system

performance - SPECapc several popular 3D-intensive

applications - SPEC HPC2002 high-end parallel computing tests

using quantum chemistry application, weather

modeling, industrial oil deposits locator - SPEC OMP2001 OpenMP application performance
- SPECjvm98 performance of java client on a Java

VM - SPECjAppServer2004 multi-tier benchmark

measuring the performance of J2EE application

servers - SPECjbb2005 server-side Java performance
- SPEC MAIL2001 mail server performance (SMTP and

POP) - SPEC SFS97_R1 NFS server throughput and response

time - Planned SPEC MPI2006, SPECimap, SPECpower,

Virtualization

Sample Results SPEC CPU2006

System CINT2006 Speed CINT2006 Speed CFP2006 Speed CFP2006 Speed CINT2006 Rate CINT2006 Rate CFP2006 Rate CFP2006 Rate

System base peak base peak base peak base peak

Dell Precision 380 (Pentium EE965 3.73GHz, 2cores) 11.6 12.4 23.1 21.7

HP ProLiant DL380 G4 (Xeon 3.8GHz, 2 cores) 11.4 11.7 20.9 18.8

HP ProLiant DL585 (Opteron 854 2.8GHz, 2 cores) 11.2 12.7 12.1 13.0 22.3 25.2 24.1 25.9

Sun Blade 2500 (1 UltraSPARC IIIi, 1280MHz) 4.04 4.04

Sun Fire E25K (UltraSPARC IV 1500MHz, 144 cores) 759 904

HP Integrity rx6600 (Itanium2 1.6GHz/24MB, 2 cores) 14.5 15.7 17.3 18.1

HP Integrity rx6600 (Itanium2 1.6GHz/24MB, 8 cores) 94.7 102 69.1 71.4

HP Integrity Superdome (Itanium2 1.6GHz/24MB, 128 cores) 1534 1648 1422 1479

- Notes
- base metric requires that the same flags are

used when compiling all instances of the

benchmark (peak is less strict) - speed metric measures how fast a computer

executes single task, while rate determines

throughput with multiple tasks

TPC

- Governed by the Transaction Processing

Performance Council (http//www.tpc.org)

founded in 1985 - members include leading system and microprocessor

manufacturers, and commercial database developers - the council appoints professional affiliates and

auditors outside the member group to help fulfill

the TPCs mission and validate benchmark results - Current benchmark flavors
- TPC-C for transaction processing (de-facto

standard for On-Line Transaction Processing) - TPC-H for decision support systems
- TPC-App for web services
- Obsolete benchmarks
- TPC-A (performance of update-intensive databases)
- TPC-B (throughput of a system in transactions per

second) - TPC-D (decision support applications with long

running queries against complex data structures) - TPC-R (business reporting, decision support)
- TPC-W (transactional web e-Commerce benchmark)

Top Ten TPC-C Results

Topics

- Definitions, properties and applications
- Early benchmarks
- Linpack
- Other parallel benchmarks
- Organized benchmarking
- Presentation and interpretation of results
- Summary

Presentation of the Results

- Tables
- Graphs
- Bar graphs (a)
- Scatter plots (b)
- Line plots (c)
- Pie charts (d)
- Gantt charts (e)
- Kiviat graphs (f)
- Enhancements
- Error bars, boxes or confidence intervals
- Broken or offset scales (be careful!)
- Multiple curves per graph (but avoid overloading)
- Data labels, colors, etc.

(a)

(b)

(c)

(d)

(e)

(f)

Kiviat Graph Example

Source http//www.cse.clrc.ac.uk/disco/DLAB_BENCH

_WEB/hpcc/hpcc_kiviat.shtml

Mixed Graph Example

WRF OOCORE MILC

PARATEC HOMME BSSN_PUGH Whisky_Carpet

ADCIRC PETSc_FUN3D

Computation fraction

Floating point operations

Communication fraction

Load/store operations

Other operations

Characterization of NSF/CCT parallel applications

on POWER5 architecture (using data collected by

IPM)

Graph Dos and Donts

- Good graphs
- Require minimum effort from the reader
- Maximize information
- Maximize information-to-ink ratio
- Use commonly accepted practices
- Avoid ambiguity
- Poor graphs
- Have too many alternatives on a single chart
- Display too many y-variables on a single chart
- Use vague symbols in place of text
- Show extraneous information
- Select scale ranges improperly
- Use line chart instead of a bar graph

Reference Raj Jain, The Art of Computer Systems

Performance Analysis, Chapter 10

Common Mistakes in Benchmarking

From Chapter 9 of The Art of Computer Systems

Performance Analysis by Raj Jain

- Only average behavior represented in test

workload - Skewness of device demands ignored
- Loading level controlled inappropriately
- Caching effects ignored
- Buffering sizes not appropriate
- Inaccuracies due to sampling ignored
- Ignoring monitoring overhead
- Not validating measurements
- Not ensuring same initial conditions
- Not measuring transient performance
- Using device utilizations for performance

comparisons - Collecting too much data but doing very little

analysis

Misrepresentation of Performance Results on

Parallel Computers

- Quote only 32-bit performance results, not 64-bit

results - Present performance for an inner kernel,

representing it as the performance of the entire

application - Quietly employ assembly code and other low-level

constructs - Scale problem size with the number of processors,

but omit any mention of this fact - Quote performance results projected to the full

system - Compare your results with scalar, unoptimized

code run on another platform - When direct run time comparisons are required,

compare with an old code on an obsolete system - If MFLOPS rates must be quoted, base the

operation count on the parallel implementation,

not on the best sequential implementation - Quote performance in terms of processor

utilization, parallel speedups or MFLOPS per

dollar - Mutilate the algorithm used in the parallel

implementation to match the architecture - Measure parallel run times on a dedicated system,

but measure conventional run times in a busy

environment - If all else fails, show pretty pictures and

animated videos, and don't talk about performance

Reference David Bailey Twelve Ways to Fool the

Masses When Giving Performance Results on

Parallel Computers, Supercomputing Review, Aug

1991, pp.54-55, http//crd.lbl.gov/dhbailey/dhbpa

pers/twelve-ways.pdf

Topics

- Definitions, properties and applications
- Early benchmarks
- Linpack
- Other parallel benchmarks
- Organized benchmarking
- Presentation and interpretation of results
- Summary

Material For Test

- Basic performance metrics (slide 4)
- Definition of benchmark in own words purpose of

benchmarking properties of good benchmark

(slides 5, 6, 7) - Linpack what it is, what does it measure,

concepts and complexities (slides 15, 17, 18) - HPL (slides 21 and 24)
- Linpack compare and contrast (slide 25)
- General knowledge about HPCC,SPEC and NPB suites

(slides 30, 31, 34, 39) - Kiviat Graph (slide 46)
- Benchmark result interpretation (slides 49, 50)

(No Transcript)