Transcript Let’s look at the single-cycle model analytically

Performance

Performance
– what is it: measures of performance

The CPU Performance Equation:
– Execution time as the measure
– what affects execution time
– examples

Choosing good benchmarks?
– choosing bad benchmarks?

Amdahl's Law
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Performance is Time

Time to do the task (Execution Time)
– execution time, response time, latency

Tasks per unit time (sec, minute, ...)
– throughput, bandwidth
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Performance as Response Time
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Performance is most often measured as
response time or execution time for some
task.
“X is n times faster than Y” means
Performance(X)
–––––––––––––– =
Performance(Y)

Execution Time(Y)
–––––––––––––––– = n
Execution Time(X)
Example
Execution time of program P
X is 5 sec; Y is 10 sec.

X is 2 times faster than Y.
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What time to measure?

Elapsed time, wall-clock time:
–
–
–
–
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CPU Time:
–
–
–
–
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actual time from start to completion
depends on CPU, system, I/O, etc.
often used in real benchmarks
only suitable choice when I/O is included
measure/analyze CPU performance only
may be suitable when machine is timeshared
possibly both user and system component
User CPU time is our focus for first part of course
Elapsed time = CPU time + Idle time
– usually and assuming time is accurately accounted for
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Metrics of performance

Different performance metrics are appropriate
at different levels:
Application
Programming
Language
Compiler
ISA
Datapath
Control
Function Units
Transistors
Frames per second
Operations per second
(millions) of Instructions per second – MIPS
(millions) of (F.P.) operations per second –
MFLOP/s
Cycles per second (clock rate)
Cycles per Instruction
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Relating Processor Metrics

CPU execution time per program
= CPU clock cycles/program X Clock cycle time
= CPU clock cycles/program ÷ Clock rate (frequency)
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CPU clock cycles/program
= Instructions/program X Clock cycles Per Instruction
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Clock cycles Per Instruction (CPI) is an average
measurement, it depends on :
– ISA, the implementation, and the program measured
– CPI = CPU clock cycles/program ÷ Instructions/program
– Also, Instructions per clock cycle or IPC = 1 / CPI

CPU execution time = Instructions X CPI X Clock cycle
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Let’s look at the single-cycle model
analytically
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Static timing analysis

Memories
Register
Adders
ALU
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Use topological sort!
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10 ns
5 ns
10 ns
10 ns
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Zero ext.
35 ns delay
10 ns
Branch
logic
5 ns
10 ns
0
A
ALU
4
B
+
Sgn/Ze
extend
31
+
10 ns
lw $2 const($3)
10 ns
10 ns
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But that path goes through
the data memory!
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What if this is not a load/store?
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How about an instruction that does nothing?
“NOP”
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Zero ext.
10 ns delay
10 ns
Branch
logic
5 ns
10 ns
0
A
ALU
4
B
+
Sgn/Ze
extend
31
+
10 ns
Nop
10 ns
10 ns
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Zero ext.
25 ns delay
10 ns
Branch
logic
5 ns
10 ns
0
A
ALU
4
B
+
Sgn/Ze
extend
31
+
10 ns
Add $ra $rb $rc
10 ns
10 ns
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Zero ext.
20 ns delay
10 ns
Branch
logic
5 ns
10 ns
0
A
ALU
4
B
+
Sgn/Ze
extend
31
+
10 ns
B label
10 ns
10 ns
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35 ns for load/store
but
10 ns for NOP !?
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Amdahl’s Law:
“Make the common case fast”
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Amdahl's Law
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Handy for evaluating impact of a change not tied to
CPU performance equation
Insight: No improvement of a feature enhances
performance by more than the use of the feature.
Suppose that enhancement E accelerates fraction F
of a program by a factor S (remainder of the task is
unaffected):
ExecTimeE = (1 – F(1 – 1/S)) X ExecTimewithout
E
F
1-F
F/S
1-F
S=
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What if we don’t need the ALU?
A branch instruction?
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BUT!

The single cycle model has to accomodate
the slowest instruction

Even if it rarely occurs!
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How much work can our
structure perform?

For a program Q:

Time = Number of executed instruction *
Number of cycles per instruction *
Time per cycle
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T = Nq * CPI * Tc
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For the single cycle model....

CPI = 1 for all instructions
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Tc determined by the slowest instruction
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How to reduce T?
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T = Nq * CPI * Tc
Reduce Nq.
More powerful instructions!
More hardware, longer paths, cycle time
goes up (slower machine)
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“No free lunch”
Why designers are so well paid to optimize designs.
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How to reduce T?

T = Nq * CPI * Tc
Faster hardware
Technological limits
Cost increase not linearly related
Sales volume drops
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How to reduce T?

T = Nq * CPI * Tc
Make this a function of the instruction
For example:
NOP = 1 cycle
LW = 4 cycles
Chapter 5.4, the classical method
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How to reduce T?

T = Nq * CPI * Tc
Make this a function of the instruction
CPI goes up, but we can use an average,
not the worst case
Tc goes down, time to do the longes step,
not the entire instruction
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Example
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Branch:
Step 1: fetch
Step 2: New PC
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Add:
Step 1: fetch
Step 2: decode/ register fetch
Step 3: Compute and write back
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Example
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LW = 4 steps
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Cycletime = 1/4 old time
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T
LW
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=4
* 1/4 old time,
CPI
just as slow for the lw instruction
our worst case!
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But that’s not important if
LW is not common!
T = Nq * CPI * 1/4 old time
Averaged
over this many
instructions
1,3?
1,7?
Never = 4,0!
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We win because of quantitative statistical
properties of our programs!
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What value of CPI do we use?
1,3?
1,5?
1,7?
Easy: Use average program!
?
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There is no such thing!
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Artificial “average programs”
called “benchmarks”
Are they something to trust?
What about “peak performance values”
mips?
mflops?
We have a peak at CPI = 1....
...a program of only NO-OPS!
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Why Do Benchmarks?
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How we evaluate performance differences
– Across and within a single system (design & variations)
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What should benchmarks do?
– Represent a large class of important programs
– Behave like typical programs:
 improved benchmark performance => improved
performance broadly
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For better or worse, benchmarks shape a field
Good ones accelerate progress
Bad benchmarks hurt progress
– help real programs vs. sell machines/papers?
– Enhancements that help benchmarks may not help most
programs and v.v.
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Classes of Benchmarks
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(Toy) Benchmarks
– 10-100 line–e.g.,: sieve, puzzle, quicksort
– good first programming assignments
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Synthetic Benchmarks
– attempt to match average frequencies of real workloads
– e.g., Whetstone, dhrystone
– mostly good for nothing: too artificial
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Kernels
– Time critical excerpts of real programs
– e.g., Livermore loops, Linpack
– good for micro-performance studies
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Real programs
– e.g., gcc, spice, Verilog, Database, stock trading
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Successful Benchmark: SPEC Collection
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1987 RISC industry (workstations) mired in
“bench marketing”:
– (“That is an 8 MIPS machine, but they claim 10 MIPS!”)
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EE Times + 5 companies band together to
perform Systems Performance Evaluation
Committee (SPEC) in 1988:
– Sun, MIPS, HP, Apollo, DEC
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Create standard list of programs, inputs,
reporting rules:
– several real programs, including OS calls
– some I/O
– rules for running and reporting
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Multiple clock cycle designs:
State machines
Micro programming
chapter 5.4
“Computer Organization & Design”
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How to reduce T?
T = Nq * CPI * Tc
Reduce quotient cycles / instruction
reduce “cycles”
multiple clockcycle design
Increase “instruction”
execute more
than one instr.
per cycle!
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More than one
instruction per cycle?
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Parallelism
– Div/mult + floating point + integer
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Superscalarity
– Multiple issue etc.
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Pipelining
– Of general importance
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