Download - Lecture 2: Performance Measurement
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Lecture 2:Lecture 2:
Performance Performance MeasurementMeasurement
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Performance Evaluation
The primary duty of software developers is to create functionally correct programs
Performance evaluation is a part of software development for well-performing programs
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Performance Analysis Cycle
Have an optimization phase just like testing and debugging phase
Code Development
Measure
Modify / Tune
Analyze
Usage
Functionally complete and correct program
Complete, correct and well-performing program
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Goals of Performance Analysis
The goal of performance analysis is to provide quantitative information about the performance of a computer system
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Goals of Performance Analysis Compare alternatives
• When purchasing a new computer system, to provide quantitative information Determine the impact of a feature
• In designing a new system or upgrading, to provide before-and-after comparison System tuning
• To find the best parameters that produce the best overall performance Identify relative performance
• To quantify the performance relative to previous generations Performance debugging
• To identify the performance problems and correct them Set expectations
• To determine the expected capabilities of the next generation
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Performance EvaluationPerformance Evaluation steps:
1. Measurement / Prediction• What to measure? How to measure?• Modeling for prediction
• Simulation • Analytical Modeling
2. Analysis & Reporting• Performance metrics
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Performance Measurement
Interval Timers
• Hardware Timers
• Software Timers
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Performance MeasurementHardware Timers
• Counter value is read from a memory location
• Time is calculated as
Clock Counter
Tc
n bits to processor memory bus
Time = (x2 - x1) x Tc
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Performance MeasurementSoftware Timers
• Interrupt-based
• When interrupt occurs, interrupt-service routine increments the timer value which is read by a program
• Time is calculated as
Clock Prescaling Counter
Tc
to processor interrupt input
T’c
Time = (x2 - x1) x T’c
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Performance Measurement
Timer Rollover
Occurs when an n-bit counter undergoes a transition from its maximum value 2n – 1 to zero
There is a trade-off between roll over time and accuracy
T’c 32-bit 64-bit10 ns 42 s 5850 years
1 s 1.2 hour 0.5 million years
1 ms 49 days 0.5 x 109 years
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TimersSolution:
1. Use 64-bit integer (over half a million year)
2. Timer returns two values: • One represents seconds • One represents microseconds since the last secondWith 32-bit, the roll over is over 100 years
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Performance Measurement
Interval Timers
T0 Read current timeEvent being timed ();T1 Read current time
Time for the event is: T1-T0
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Performance MeasurementTimer Overhead
Initiate read_time
Current time is read
Event begins
Event ends; Initiate read_time
Current time is read
T1
T2
T3
T4
Measured time: Tm = T2 + T3 + T4
Desired measurement: Te = Tm – (T2 + T4) = Tm – (T1 + T2) since T1 = T4
Timer overhead: Tovhd = T1 + T2
Te should be 100-1000 times greater than Tovhd .
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Performance MeasurementTimer Resolution
Resolution is the smallest change that can be detected by an interval timer.
nT’c < Te < (n+1)T’c
If Tc is large relative to the event being measured, it may be impossible to measure the duration of the event.
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Performance MeasurementMeasuring Short Intervals
Te < Tc
Tc
Te
Tc
Te
1
0
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Performance MeasurementMeasuring Short Intervals
Solution: Repeat measurements n times.
Average execution time: T’e = (m x Tc) / nm: number of 1s measured
Average execution time: T’e = (Tt / n ) – hTt : total execution time of n
repetitionsh: repetition overhead
Tc
Te
Tt
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Performance Measurement Time
• Elapsed time / wall-clock time / response time• Latency to complete a task, including disk access,
memory access, I/O, operating system overhead, and everything (includes time consumed by other programs in a time-sharing system)
• CPU time• The time CPU is computing, not including I/O time or
waiting time• User time / user CPU time
• CPU time spent in the program• System time / system CPU time
• CPU time spent in the operating system performing tasks requested by the program
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Performance Measurement UNIX time command
90.7u 12.9s 2:39 65%
Drawbacks:• Resolution is in milliseconds• Different sections of the code can not be timed
User time
System time
Elapsed time Percentage of elapsed time
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Timers
Timer is a function, subroutine or program that can be used to return the amount of time spent in a section of code.
t0 = timer(); …< code segment > …t1 = timer();time = t1 – t0;
zero = 0.0;t0 = timer(&zero); …< code segment > …t1 = timer(&t0);time = t1;
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Timers
Read Wadleigh, Crawford pg 130-136 for:time, clock, gettimeofday, etc.
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TimersMeasuring Timer Resolution
main() { . . .zero = 0.0;t0 = timer(&zero);t1 = 0.0;j=0;while (t1 == 0.0) {
j++;zero=0.0;t0 = timer(&zero);foo(j);t1 = timer(&t0);
}printf (“It took %d iterations for a nonzero time\n”, j); if (j==1) printf (“timer resolution <= %13.7f seconds\n”, t1);else printf (“timer resolution is %13.7f seconds\n”, t1);
}foo(n){ . . .
i=0;for (j=0; j<n; j++)
i++;return(i);
}
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TimersMeasuring Timer Resolution
Using clock():
Using times():
Using getrusage():
It took 682 iterations for a nonzero timetimer resolution is 0.0200000 seconds
It took 720 iterations for a nonzero timetimer resolution is 0.0200000 seconds
It took 7374 iterations for a nonzero timetimer resolution is 0.0002700 seconds
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TimersSpin Loops
For codes that take less time to run than the resolution of the timer First call to a function may require an inordinate amount of time. Therefore the minimum of all times may be desired.
main() { . . .zero = 0.0;t2 = 100000.0;for (j=0; j<n; j++) {
t0 = timer(&zero);foo(j);t1 = timer(&t0); t2 = min(t2, t1);
}t2 = t2 / n;printf (“Minimum time is %13.7f seconds\n”, t2);
}foo(n){ . . .
< code segment >}
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Profilers A profiler automatically insert timing calls into applications to
generate calls into applications
It is used to identify the portions of the program that consumes the largest fraction of the total execution time.
It may also be used to find system-level bottlenecks in a multitasking system.
Profilers may alter the timing of a program’s execution
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Profilers Data collection techniques
• Sampling-based • This type of profilers use a predefined clock; every multiple of this clock tick
the program is interrupted and the state information is recorded. • They give the statistical profile of the program behavior. • They may miss some important events.
• Event-based• Events are defined (e.g. entry into a subroutine) and data about these events
are collected.• The collected information shows the exact execution frequencies. • It has substantial amount of run-time overhead and memory requirement.
Information kept• Trace-based: The compiler keeps all information it collects.• Reductionist: Only statistical information is collected.
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Performance EvaluationPerformance Evaluation steps:
1. Measurement / Prediction• What to measure? How to measure?• Modeling for prediction
• Simulation • Analytical Modeling
• Queuing Theory
2. Analysis & Reporting• Performance metrics
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Predicting Performance
Performance of simple kernels can be predicted to a high degree
Theoretical performance and peak performance must be close
It is preferred that the measured performance is over 80% of the theoretical peak performance
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Performance EvaluationPerformance Evaluation steps:
1. Measurement / Prediction• What to measure? How to measure?• Modeling for prediction
• Simulation • Analytical Modeling
• Queuing Theory
2. Analysis & Reporting• Performance metrics
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Performance Metrics
Measurable characteristics of a computer system: • Count of an event• Duration of a time interval
• Size of a parameter
Rate: • Operations executed per second
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Performance MertricsClock Speed
Clock speed/frequency (f): the rate of clock pulses (ex: 1GHz)
Cycle time (Tc): time between two clock pulses (Tc = 1/f)
Tc
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Performance MertricsInstruction Execution Rate
Cycles per Instruction (CPI): is an average depends on the design of micro-architecture
(hardwired/microprogrammed, pipelined)
Number of instructions: is the number of instructions executed at runtime Depends on
• instruction set architecture (ISA) • compiler
CPI =
n
i 1ii )ICPI(
n
i 1iI
CPIi: number of cycles required for instruction i
Ii: number of executed instructions of type i
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Performance MetricsCPU Performance
CPU time of a program (T) = instructions x cycles x time program instruction cycle
CPI (cycles per instruction)
T = instruction count x CPI x 1 f
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Performance MetricsCPU Performance
Drawbacks:
In modern computers, no program runs without some operating system running on the hardware
Comparing performance between machines with different operating systems will be unfair
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Performance MetricsExecution time
• Elapsed time / wall-clock time / response time• Latency to complete a task, including disk access, memory
access, I/O, operating system overhead, and everything (includes time consumed by other programs in a time-sharing system)
• CPU time• The time CPU is computing, not including I/O time or
waiting time• User time / user CPU time
• CPU time spent in the program• System time / system CPU time
• CPU time spent in the operating system performing tasks requested by the program
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Performance MetricsPerformance Comparison
Relative performance
Performancex = 1 . Execution timeX
Performance Ratio = PerformanceX = Execution timeY
PerformanceY Execution timeX
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Performance MetricsRelative Performance
• If workload consists of more than one program, total execution time may be used.
• If there are more than one machine to be compared, one of them must be selected as a reference.
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Performance MetricsThroughput
• Total amount of work done in a given time• Measured in tasks per time unit• Can be used for
• Operating system performance • Pipeline performance• Multiprocessor performance
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Performance Metrics MIPS (Million instructions per second)
• Includes both integer and floating point performance
• Number of instructions in a program varies between different computers
• Number of instructions varies between different programs on the same computer
MIPS = Instruction count = Clock rate Execution time x 106 CPI x 106
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Performance Metrics MFLOPS
(Million floating point operations per second)
• Give performance of only floating-point operations
• Different mixes of integer and floating-point operations may have different execution times:• Integer and floating-point units work independently• Instruction and data caches provide instruction and data
concurrently
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Performance Metrics Utilization
Speciality ratio
• 1 general purpose
Utilization = Busy time . Total time
Speciality ratio = Maximum performance . Minimum performance
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Performance Metrics Asymptotic and Half performance
• r – asymptotic performance
• n1/2 – half performanceT = r (n + n1/2)
r = 1/tn1/2 = t0/t
Slope = r-1
t0
-n1/2n1/2
2t0
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Performance MetricsSpeedup
• Express how much faster is system 2 than system 1• Calculated directly from execution time
Performancex = 1 = 1 Execution timeX TX
Speedup2,1 = Performance2 = T1
Performance1 T2
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Performance MetricsRelative Change
• It expresses the performance of system 2 relative to system 1
Performancex = 1 = 1 Execution timeX TX
Relative change2,1 = Performance2 - Performance1 = T1 - T2 = Speedup2,1 - 1 Performance1 T2
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Performance MetricsStatistical Analysis
• Used to compare performance
• Workload consists of many programs
• Depends on the nature of the data as well as distribution of the test results
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Performance MetricsIndices of Central Tendency
Used to summarize multiple measurements
• Mean
• Median
• Mode
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Performance Metrics• Mean (average)
Gives equal weight to all measurements
Arithmetic mean = xi , 1 ≤ i ≤ n n
Measurement Execution time
X1 10
X2 20
X3 15
X4 18
X5 16
Mean 15.8
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Performance Metrics• Median
1. Order all n measurements2. The middle value is the median. If n is even, median is the mean of the middle 2 values
Using Median instead of Mean reduces the skewing effect of the outliers.
Measurement Execution time
X1 10
X2 20
X3 15
X4 18
X5 16
X6 200
Mean 46.5
Measurement Execution time
X1 10
X3 15
X5 16
X4 18
X2 20
X6 200
254 XX Median =
= 17
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Performance Metrics• ModeMode is the value that occurs most frequently
• If all values occur once, there is no mode• If there are several samples that all have the same value, there would be several
modes
Measurement Execution time
X1 10
X2 20
X3 36
X4 20
X5 20
X6 20
Mode = 20
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Mean, Median, Mode
Mean Incorporates information from the entire measured values Sensitive to outliers
Median and Mode Less sensitive to outliers Do not effectively use all information
ex
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Performance MetricsArithmetic mean (average)
• May be misleading if the data are skewed or scattered
Arithmetic mean = xi , 1 ≤ i ≤ n n
MA MB MC
Prog1 50 100 500
Prog2 400 800 800
Prog3 5550 5100 4700
Average 2000 2000 2000
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Performance MetricsWeighted average
• weight is the frequency of each program in daily processing
• Results may change with a different set of execution frequencies
Weighted average = ∑ wi . xi 1 ≤ i ≤ n
weight MA MB MC
Prog1 60% 50 100 500
Prog2 30% 400 800 800
Prog3 10% 5550 5100 4700
Average 705 810 1010
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Performance MetricsGeometric mean
• Results are stated in relation to the performance of a reference machine
Geometric mean = ( xi )1/n , 1 ≤ i ≤ n
MA Normalized to MA
MB
(reference)
Normalized to MB
MC Normalized to MC
Prog1 50 2 100 1 500 0.2
Prog2 400 2 800 1 800 1
Prog3 5550 0.92 5100 1 4700 1.085
Average 1.54 1 0.60
• Results are consistent no matter which system is chosen as reference
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Performance MetricsHarmonic mean
• Used to compare performance results that are expressed as a rate (e.g. operations per second, throughput, etc.)
• Slowest rates have the greatest influence on the resultIt identifies areas where performance can be improved
Harmonic mean = n , 1 ≤ i ≤ n ∑ 1/xi
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Performance Metrics
Characteristics of a good performance metric
• If the time values are averaged, then the resulting mean value must be directly proportional to the total time.
• If the rate values are averaged, then the resulting mean value must be inversely proportional to the total time.
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Performance MetricsEx• n benchmark programs• Ti is the execution time of program i• F floating-point operations in each program• Mi = F / Ti is the execution rate of program i (MFLOP/s)
Arithmetic mean
• Inappropriate for summarizing rates
TA = TA is directly proportional to the total execution time
n
1iiT
n1
MA = =
n
1iiM
n1
n
1i iT1
nF MA is inversely proportional to
the total execution time
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Performance MetricsHarmonic mean
• Inappropriate for summarizing execution times• Appropriate for summarizing rates
TH = TH is not directly proportional to the total execution time
n
1ii1/T
n
MH = =MH is inversely proportional to the total execution time
n
1ii1/M
n
n
1iiT
n F
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Performance MetricsEx
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Performance MetricsGeometric mean
• Inappropriate for summarizing execution times• Inappropriate for summarizing rates
TG = TG is not directly proportional to the total execution time
MH = =MH is not inversely proportional to the total execution time
n
n
1iiT
n
n
1iiM
n
n
1iiF/T
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Performance MetricsGeometric mean
• Produces a consistent ordering of the systems but it is the wrong ordering
M1 M2 M3Prog1 417 244 134
Prog2 83 70 70
Prog3 66 153 135
Prog4 39499 33527 66000
Prog5 772 368 369
Geometric mean (Normalized wrt S1) 1.0 0.86 0.84
Geometric mean (Normalized wrt S2) 1.17 1.0 0.99
Rank 3 2 1
Total time 40787 34362 66798
Arithmetic mean 8157 6872 13342
Rank 2 1 3
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Performance MetricsHistogram• Used to display the distribution of a set of measured
values (variability)
• First find the minimum and maximum values. Then divide the range into b subranges, called cells.
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Histogram
Message size (kbytes)
Network A Network B
0 < xi ≤ 5 11 39
5 < xi ≤ 10 27 25
10 < xi ≤ 15 41 18
15 < xi ≤ 20 32 5
20 < xi ≤ 25 21 19
25 < xi ≤ 30 12 42
30 < xi ≤ 35 4 0
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Performance MetricsIndex of Dispersion
Index of dispersion is used to compare the spread of measurements around the mean value
• Range is the simplest metric for an index of dispersion
• Range is sensitive to a few extreme values
)(min)(maxmax iiii xxR
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Performance MetricsIndex of Dispersion
• Maximum of the absolute values of the difference of each measurement from the mean
• It is also sensitive to extreme values
|)(|maxmax xxii
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Performance MetricsIndex of Dispersion
• Sample variance is the simplest metric for an index of dispersion
)1()(
2
1 1
22
nnxxn
sn
i
n
ii
Requires 2 passes through the data to calculate first x and then s2
Requires 1 pass
1)(
2
12
nxx
sn
i i
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Performance MetricsIndex of Dispersion
• Standard deviation
• Coefficient of variance (COV): normalizes standard deviation wrt the mean
1)(
2
12
nxx
ssn
i i
xsCOV /
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Performance Evaluation Methods
Benchmarking Monitoring Analytical Modeling Queuing Theory
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Benchmarking
Benchmark is a program that is run on a computer to measure its performance and compare it with other machines
Best benchmark is the users’ workload – the mixture of programs and operating system commands that users run on a machine. Not practical
Standard benchmarks
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BenchmarkingTypes of Benchmarks
Synthetic benchmarks
Toy benchmarks
Microbenchmarks
Program Kernels
Real Applications
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BenchmarkingSynthetic benchmarks
Artificially created benchmark programs that represent the average frequency of operations (instruction mix) of a large set of programs
• Whetstone benchmark • Dhrystone benchmark• Rhealstone benchmark
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BenchmarkingSynthetic benchmarks
• Whetstone benchmark • First written in Algol60 in 1972, today Fortran, C/C++,
Java versions are available• Represents the workload of numerical applications• Measures floating point arithmetic performance• Unit is Millions of Whetstone instructions per second
(MWIPS)• Shortcommings:
• Does not represent constructs in modern languages, such as pointers, etc.
• Does not consider cache effects
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BenchmarkingSynthetic benchmarks
• Dhrystone benchmark• First written in Ada in1984, today • Represents the workload of C version is available• Statistics are collected on system software, such as operating
system, compilers, editors and a few numerical programs• Measures integer and string performance, no floating-point
operations• Unit is the number of program iteration completions per second • Shortcommings:
• Does not represent real life programs• Compiler optimization overstates system performance• Small code that may fit in the instruction cache
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BenchmarkingSynthetic benchmarks
• Rhealstone benchmark• Multi-tasking real-time systems • Factors are:
• Task switching time• Pre-emption time• Interrupt latency time• Semaphore shuffling time• Dead-lock breaking time• Datagram throughput time
• Metric is Rhealstones per second
6
∑ wi . (1/ ti) i=1
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BenchmarkingToy benchmarks
10-100 lines of code that the result is known before running the toy program
• Quick sort • Sieve of Eratosthenes
Finds prime numbers http://upload.wikimedia.org/wikipedia/commons/8/8c/New_Animation_Sieve_of_Eratosthenes.gif
func sieve( var N ) var PrimeArray as array of size N initialize PrimeArray to all true for i from 2 to N for each j from i + 1 to N, where i divides j
set PrimeArray( j ) = false
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BenchmarkingMicrobenchmarks
Small, specially designed programs used to test some specific function of a system (eg. Floating-point execution, I/O subsystem, processor-memory interface, etc.)
• Provide values for important parameters of a system• Characterize the maximum performance if the overall
performance is limited by that single component
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BenchmarkingKernels
Key pieces of codes from real applications.
• LINPACK and BLAS
• Livermore Loops
• NAS
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BenchmarkingKernels
• LINPACK and BLAS Libraries• LINPACK – linear algebra package
• Measures floating-point computing power• Solves system of linear equations Ax=b with Gaussian
elimination• Metric is MFLOP/s• DAXPY - most time consuming routine• Used as the measure for TOP500 list
• BLAS – Basic linear algebra subprograms• LINPACK makes use of BLAS library
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BenchmarkingKernels
• LINPACK and BLAS Libraries
• SAXPY – Scalar Alpha X Plus Y• Y = X + Y, where X and Y are vectors, is a scalar• SAXPY for single and DAXPY for double precision • Generic implementation:
for (int i = m; i < n; i++) { y[i] = a * x[i] + y[i];
}
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BenchmarkingKernels
• Livermore Loops• Developed at LLNL• Originally in Fortran, now also in C• 24 numerical application kernels, such as:
• hydrodynamics fragment, • incomplete Cholesky conjugate gradient, • inner product, • banded linear systems solution, tridiagonal linear systems solution, • general linear recurrence equations, • first sum, first difference, • 2-D particle in a cell, 1-D particle in a cell, • Monte Carlo search, • location of a first array minimum, etc.
• Metrics are arithmetic, geometric and harmonic mean of CPU rate
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BenchmarkingKernels
• NAS Parallel Benchmarks• Developed at NASA Advanced Supercomputing division• Paper-and-pencil benchmarks• 11 benchmarks, such as:
• Discrete Poisson equation,• Conjugate gradient• Fast Fourier Transform• Bucket sort• Embarrassingly parallel• Nonlinear PDE solution• Data traffic, etc.
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BenchmarkingReal Applications
Programs that are run by many users• C compiler• Text processing software• Frequently used user applications• Modified scripts used to measure particular aspects of
system performance, such as interactive behavior, multiuser behavior
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BenchmarkingBenchmark Suites
Desktop Benchmarks• SPEC benchmark suite
Server Benchmarks • SPEC benchmark suite• TPC
Embedded Benchmarks• EEMBC
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BenchmarkingSPEC Benchmark Suite
Desktop Benchmarks• CPU-intensive
• SPEC CPU2000• 11 integer (CINT2000) and 14 floating-point (CFP2000) benchmarks• Real application programs:
• C compiler• Finite element modeling• Fluid dynamics, etc.
• Graphics intensive• SPECviewperf
• Measures rendering performance using OpenGL• SPECapc
• Pro/Engineer – 3D rendering with solid models• Solid/Works – 3D CAD/CAM design tool, CPU-intensive and I/O intensive tests• Unigraphics – solid modeling for an aircraft design
Server Benchmarks • SPECWeb – for web servers• SPECSFS – for NFS performance, throughput-oriented
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BenchmarkingTPC Benchmark Suite
Server Benchmark Transaction processing (TP) benchmarks Real applications
• TPC-C: simulates a complex query environment• TPC-H: ad hoc decision support• TPC-R: business decision support system where users run a
standard set of queries• TPC-W: business-oriented transactional web server
Measures performance in transactions per second. Throughput performance is measured only when response time limit is met.
Allows cost-performance comparisons
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BenchmarkingEEMBC Benchmarks
for embedded computing systems
34 benchmarks from 5 different application classes:• Automotive/industrial• Consumer• Networking• Office automation• Telecommunications
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BenchmarkingBenchmarking Strategies
Fixed-computation benchmarks
Fixed-time benchmarks
Variable-computation and variable-time benchmarks
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BenchmarkingBenchmarking Strategies
Fixed-computation benchmarks
Fixed-time benchmarks
Variable-computation and variable-time benchmarks
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BenchmarkingFixed-Computation benchmarks
W: fixed workload (number of instructions, number of floating-point operations, etc)
T: measured execution time
R: speed
Compare
TWR
1
2
2
1
2
1
//
TT
TWTW
RRSpeedup
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BenchmarkingFixed-Computation benchmarks
Amdahl’s Law
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BenchmarkingFixed-Time benchmarks
On a faster system, a larger workload can be processed in the same amount of time
T: fixed execution timeW: workload R: speed
Compare
TWR
2
1
2
1
2
1
//
WW
TWTW
RRSizeup
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BenchmarkingFixed-Time benchmarks
Scaled Speedup
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BenchmarkingVariable-Computation and Variable-Time
benchmarks
In this type of benchmark, quality of the solution is improved.
Q: quality of the solutionT: execution time
Quality improvements per second:TQ
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Quality of MeasurementCharacteristics of a measurement tool (timer)
Accuracy: Absolute difference of a measured value and the corresponding standard reference value (such as the duration of a second).
Precision: Reliability of the measurements made with the tool. Highly precise measurements are tightly clustered around a single value.
Resolution: Smallest incremental change that can be detected. Ex: interval between clock ticks
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Quality of Measurement
accuracy
precision
mean value true value
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Quality of Measurement
The uncertainties in the measurements are called errors or noise
Sources of errors: Accuracy, precision, resolution of the measurement tool Time required to read and store the current time value Time-sharing among multiple programs Processing of interrupts Cache misses, page faults
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Quality of Measurement
Types of errors:
Systematic errors• Are the result of some experimental mistake• Usually constant across all measurementsEx: temperature may effect clock period
Random errors• Unpredictable, nondeterministic• Effect the precision of measurementEx: timer resolution ±T , effects measurements with equal probability
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Quality of MeasurementExperimental measurements follow Gaussian (normal) distribution
Ex:x measured value±E random errorTwo sources of errors, each having 50% probability
Pg 48
Actual value of x is measured half of the time.
Error 1 Error 2 Measured value Probability
-E -E x-2E 1/4
-E +E x 1/4
+E -E x 1/4
+E +E x+2E 1/4
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Confidence IntervalsUsed to find a range of values that has a given probability of
including the actual value.
Case 1: number of measurements is large (n≥30)
{x1, x2, … xn} - Samples Gaussian distribution – mean – standard deviation
Confidence interval: [ c1, c2 ]Confidence level: (1-)×100 Pr[ c1 ≤ x ≤ c2 ] = 1-
Pr[ x < c1 ] = Pr[ x > c2] = /2
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Confidence Intervals
Case 1: number of measurements is large (n≥30)
Confidence interval: [ c1, c2 ]
nszxc 2/11
nszxc 2/12
x
s
- Sample mean
- Standard deviation
is obtained from the precomputed table
2/1 z
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Confidence Intervals
Case 2: number of measurements is small (n<30)
Sample variances s2 can vary significantly.
t distribution:
nstxc n 1;2/11
x
s
- Sample mean
- Standard deviation
is obtained from the precomputed table
1;2/1 nt
nstxc n 1;2/12
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Confidence Intervals
Ex: number of measurements is large (n<30)
Pg 51
90% confidence interval means that there is a 90% chance that the actual mean is within that interval.
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Confidence Intervals
90% c1= 6.5 c2= 9.495% c1= 6.1 c2= 9.7 99% c1= 5.3 c2=10.6
Wider interval Less precise knowledge about the mean
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Confidence IntervalsDetermining the Number of measurements Needed
nszxxec 2/11 )1(
))1(,)1((),( 21 xexecc
22/1
xeszn
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Confidence IntervalsDetermining the Number of measurements Needed
Estimating s:1. Make small number of measurements.2. Estimate standard deviation s.3. Calculate n.4. Make n measurements.
22/1
xeszn
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Confidence Intervals
Ex:
Pg 53
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Confidence IntervalsConfidence Intervals for Proportions
When we are interested in the number of times events occur.
Bimonial distribution: If np≥10 it approximates Gaussian distribution with mean p and
variance p(1-p)/n
nppzpc )1(
2/11
n
m
- Total events recorded
- Number of times desired outcome occurs
is the sample proportion nmp /
nppzpc )1(
2/12
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Confidence IntervalsConfidence Intervals for Proportions
Determining the number of measurements needed:
2
22/1
)()1()(
epppzn
nppzppe )1()1( 2/1
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Confidence Intervals
Ex:
Pg 55
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Comparing Alternatives
Three different cases:
Before-and-after comparison
Comparison of non-corresponding (impaired) measurements
Comparisons involving proportions
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Comparing AlternativesBefore-and-after comparison
Used to determine whether some change made to a system has statistically significant impact on its performance.
1. Find a confidence interval for the mean of the differences of the paired observations
2. If this interval includes 0, then measured differences are not statistically significant.
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Comparing AlternativesBefore-and-after comparison
Before measurements: b1, … bn After measurements: a1, … an
Differences: d1= a1, - b1 d2= a2, - b2 …
nszdc d
2/11
nszdc d
2/12
d
ds
- Arithmetic mean
- Standard deviation
n ≥ 30
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Comparing AlternativesBefore-and-after comparison
Ex: pg 65
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Comparing AlternativesNon-corresponding Measurements
There is no direct corresponding between pairs of measurements.
1. First system: n1 measurements, find x1 and s1 2. Second system: n2 measurements, find x2 and s2 3. Calculate the difference of means and standard deviation of
the difference of means
4. If confidence interval includes 0, then no significant difference
21 xxx 2
22
1
21
ns
nssx
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Comparing AlternativesNon-corresponding Measurements
xszxc 2/11
xszxc 2/12 n1 ≥ 30 and n2 ≥ 30
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Comparing AlternativesNon-corresponding Measurements
xn stxcdf;2/11
n1 < 30 or n2 < 30
xn stxcdf;2/12
)1()1( 2
2
22
2
1
2
12
1
2
2
22
1
21
nns
nns
ns
ns
ndf
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Comparing AlternativesNon-corresponding Measurements
Ex: pg 67
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Comparing AlternativesComparing Proportions
m1 is the number of times the event occurs in system 1 out of a total of n1 events measured.
If m1>10 and m2>10 the it approximates normal distribution with
means and
variance and
111 / nmp 222 / nmp
1p 2p
111 /)1( npp 222 /)1( npp
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Comparing AlternativesComparing Proportions
Confidence intervals
where
Standard deviation2
22
1
11 )1()1(npp
nppsp
21 ppp
pszpc 2/11
pszpc 2/12
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Comparing AlternativesComparing more than Two Alternatives
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Comparing AlternativesComparing more than Two Alternatives
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Comparing AlternativesComparing more than Two Alternatives
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Comparing AlternativesComparing more than Two Alternatives
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TimersRoll Over
Suppose a timer returns 32-bit integer data and measures microseconds.
It rolls over after 232 microseconds (= 1.2 hours) Timers that measure milliseconds and use 32-bit data roll
over after 232 milliseconds (= 49 days)
There is a trade-off between roll over time and accuracy.
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Performance Evaluation
Performance Evaluation steps:
1. Measurement / Prediction• What to measure? How to measure?• Modeling for prediction
2. Analysis & Reporting• Performance metrics