Computer performance

Computer performance
Dr. Amit Kumar, Dept of CSE, JUET, Guna

Why Study Performance?
• Make intelligent design choices
• See through the marketing hype
• Key to understanding underlying computer organization
- Why is some hardware faster than others for
different programs?
- What factors of system performance are
hardware related? (e.g., Do we need a new
machine, or a new operating system?)

Computer performance is characterized by
the amount of useful work accomplished
by a computer system compared to the
time and resources used.

Depending on the context, good computer
performance may involve one or more of the
following:
• Short response time for a given piece of work
• High throughput (rate of processing work)
• Low utilization of computing resource(s)
• High availability of the computing system or
application
• Fast (or highly compact) data compression and
decompression
• High bandwidth / short data transmission time

Computer vs H/W Performance
• Latency/Response Time (clocks from input to corresponding
output)
—How long does it take for my program to run?
—How long must I wait after typing return for the result?
• Throughput (How many results per clock)
—How many results can be processed per second?
—What is the average execution rate of my program?
—How much work is getting done?
If we upgrade a machine with a new processor what do we
improve?
Response Time/Latency
If we add a new machine to the lab what do we increase?
Throughput Dr. Amit Kumar, Dept of CSE, JUET, Guna

Design Tradeoffs
• Maximum Performance: measured by the
numbers of instructions executed per
Second
• Minimum Cost: measured by the size of the
circuit.
• Best Performance/Price: measured by the ratio
of MIPS to size. In powersensitive applications
MIPS/Watt is important too.

Aspect of software quality
Computer software performance,
particularly software application
response time, is an aspect of
software quality that is important
in human–computer interactions.

Technical and non-technical definitions
The performance of any computer system can be
evaluated in measurable, technical terms, using one or
more of the metrics . This way the performance can be
• compared relative to other systems or the same system
before/after changes
• defined in absolute terms, e.g. for fulfilling a
contractual obligation
Whilst the above definition relates to a scientific,
technical approach, the following definition given by
Arnold Allen would be useful for a non-technical
audience:
The word performance in computer
performance means the same thing that performance
means in other contexts, that is, it means "How well is
the computer doing the work it is supposed to do?"

Performance Equation
The total amount of time (t) required to execute a
particular benchmark program is
t = N * C/f, or equivalently
P = I * f/N
where
• P = 1/t is "the performance" in terms of time-to-
execute
• N is the number of instructions actually executed
(the instruction path length).
• f is the clock frequency in cycles per second.
• C= is the average cycles per instruction (CPI) for
this benchmark.
• I= is the average instructions per cycle (IPC) for
this benchmark.Dr. Amit Kumar, Dept of CSE, JUET, Guna

An another performance equation- The
equation, which is fundamental to measuring
computer performance is :
where the time per program is the required CPU time.

CPU optimization is not the only way to increase system
performance. Memory and I/O also weigh heavily on
system throughput. The contribution of memory and I/O,
however, is not accounted for in the basic equation. For
increasing the overall performance of a system, we have
the following options:
• CPU optimization-Maximize the speed and efficiency of
operations performed by the CPU (the performance
equation addresses this optimization).
• Memory optimization-Maximize the efficiency of a code's
memory management.
• I/O optimization-Maximize the efficiency of input/output
operations.

Comparing the performance of two systems
In comparing the performance of two systems we
measure the time that it takes for each system to
perform the same amount of work. If the same
program is run on two systems, System A and
System B, System A is n times as fast as System B
if:

Comparing the performance of two systems
System A is x% faster than System B if:
These formulas are useful in comparing the
average performance of one system with the
average performance of another.

Book's Definition of Performance
• For some program running on machine X,
PerformanceX = Program Executions / TimeX (executions/sec)
"X is n times faster than Y"
PerformanceX / PerformanceY = n
• Problem:
Machine A runs a program in 20 seconds
Machine B runs the same program in 25 seconds
PerformanceA = 1/20 PerformanceB = 1/25
Machine A is (1/20)/(1/25) = 1.25 times faster than
Machine B

Clock Cycles
Instead of reporting execution time in seconds,
we often use cycle counts
Clock “ticks” indicate when to start activities (one abstraction):
clock rate (frequency) = cycles per second (1 Hz. = 1 cycle/sec)

Execution Time
• Elapsed Time/Wall Clock Time
counts everything (disk and memory accesses, I/O , etc.)
a useful number, but often not good for comparison purposes
• CPU time
Doesn’t include I/O or time spent running other programs can
be broken up into system time, and user time
• Our focus: user CPU time
Time spent executing actual instructions of “our” program

Computer Performance Measure

How to Improve Performance?

Example
Our favorite program runs in 10 seconds on computer A, which
has a 400 Mhz clock. We are trying to help a computer
designer build a new machine B, to run this program in 6
seconds. The designer can use new (or perhaps more
expensive) technology to substantially increase the clock rate,
but has informed us that this increase will affect the rest of
the CPU design, causing machine B to require 1.2 times as
many clock cycles as machine A for the same program. What
clock rate should we tell the designer to target?

CPI = Clocks per instruction

Performance metrics
Computer performance metrics include
availability, response time, channel capacity,
latency, completion time, service time,
bandwidth, throughput, relative efficiency,
scalability, performance per watt,
compression ratio, instruction path length and
speed up.

Technical performance metrics
(Benchmarks)
There are a wide variety of technical performance
metrics that indirectly affect overall computer
performance.
Because there are too many programs to test a
CPU's speed on all of them, benchmarks were
developed. The most famous benchmarks are the
SPECint and SPECfp benchmarks developed by
Standard Performance Evaluation Corporation
and the ConsumerMark benchmark developed by
the Embedded Microprocessor Benchmark
Consortium EEMBC.

Early Benchmarks
• Whetstone
– Floating point intensive, Originally written in Algol 60 in 1972 at the
National Physics Laboratory (UK)
– Measures primarily floating point performance in WIPS: Whetstone
Instructions Per Second
• Dhrystone
– Integer and character string oriented, Synthetic benchmark developed
in 1984 by Reinhold Weicker
– Measures integer and string operations performance, expressed in
number of iterations, or Dhrystones, per second
• Livermore Fortran Kernels
– “Livermore Loops”, Developed at Lawrence Livermore National
Laboratory in 1970
– Collection of short kernels
• NAS kernel
– 7 Fortran test kernels for aerospace computation, Developed at the
Numerical Aerodynamic Simulation Projects Office at NASA Ames
– Focuses on vector floating point performance

(Benchmarks)
Some important measurements include:
• Instructions per second – Most consumers pick a
computer architecture (normally Intel
architecture) to be able to run a large base of pre-
existing, pre-compiled software. Being relatively
uninformed on computer benchmarks, some of
them pick a particular CPU based on operating
frequency.
• FLOPS – The number of floating-point operations
per second is often important in selecting
computers for scientific computations.

(Benchmarks)
• Performance per watt – System designers
building parallel computers, such as Google,
pick CPUs based on their speed per watt of
power, because the cost of powering the CPU
outweighs the cost of the CPU itself.
• Some system designers building parallel
computers pick CPUs based on the speed per
dollar.

(Benchmarks)
• System designers building real-time
computing systems want to guarantee worst-
case response.
• Computer programmers who program directly
in assembly language want a CPU to support a
full-featured instruction set.
• Low power – For systems with limited power
sources (e.g. solar, batteries, human power).
• Small size or low weight - for portable
embedded systems, systems for spacecraft.Dr. Amit Kumar, Dept of CSE, JUET, Guna

(Benchmarks)
• Environmental impact – Minimizing environmental
impact of computers during manufacturing and
recycling as well as during use. Reducing waste,
reducing hazardous materials.
• Giga-updates per second - a measure of how
frequently the RAM can be updated
Occasionally a CPU designer can find a way to make
a CPU with better overall performance by
improving one of these technical performance
metrics without sacrificing any other (relevant)
technical performance metric—for example,
building the CPU out of better, faster transistors.

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 [%]
– Memory consumption [bytes]
– Productivity [utility/($*second)]
• Modifiers:
– Sustained
– Peak
– Theoretical peak Dr. Amit Kumar, Dept of CSE, JUET, Guna

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

Commonly Used Metrics
• Nominal capacity: maximum achievable under ideal conditions
– networks: nominal capacity = bandwidth
• Throughput: requests / unit time (must be high)
• Usable capacity: max throughput for given response time limit
(response time must be low)
• Efficiency: usable capacity / nominal capacity
• Utilization: fraction of time resource busy servicing requests
(normal is best)
• Idle time
• Reliability: probability of error, MTBE
• Availability: fraction of time system servicing requests
• Mean uptime: MTBF

Computer performance

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