Superscalar
Download
1 / 31

Kasi L.K. Anbumony Department of Electrical and Computer Engineering Auburn University Auburn, AL 36849 - PowerPoint PPT Presentation


  • 412 Views
  • Uploaded on

Superscalar Processors. Kasi L.K. Anbumony Department of Electrical and Computer Engineering Auburn University Auburn, AL 36849. Outline. Pipelining: Motivation Pipeline Hazards Advanced Pipelining Instruction Level Parallelism (ILP) Multiple Issue (MIPS Superscalar)

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about 'Kasi L.K. Anbumony Department of Electrical and Computer Engineering Auburn University Auburn, AL 36849' - Philip


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
Slide1 l.jpg

SuperscalarProcessors

Kasi L.K. Anbumony

Department of Electrical and Computer Engineering

Auburn University

Auburn, AL 36849


Outline l.jpg
Outline

  • Pipelining: Motivation

  • Pipeline Hazards

  • Advanced Pipelining

    • Instruction Level Parallelism (ILP)

    • Multiple Issue (MIPS Superscalar)

  • Static Multiple Issue (SW centric)

  • Dynamic Multiple Issue (HW centric)

  • Superscalar Processor

  • Conclusion


Pipelining motivation l.jpg
Pipelining: Motivation

  • Multiple instructions are overlapped in execution. To exploit the Instruction level parallelism(ILP)

  • One of technique to make the processors fast

  • Some terms:

  • Stages

  • Task Order

  • Throughput

  • In pipeline the stages occur concurrently (or) parallely

  • Possible as long as we have separate resources for each stage


Sequential laundry non pipelined l.jpg

6 PM

Midnight

7

8

9

11

10

Time

A

B

C

D

Sequential Laundry: Non-pipelined

  • Sequential laundry takes 6 hours for 4 loads

  • If they learned pipelining, how long would laundry take?

30

40

20

30

40

20

30

40

20

30

40

20

T

a

s

k

O

r

d

e

r


Pipelined laundry start work asap l.jpg

40

40

40

30

40

20

A

B

C

D

Pipelined Laundry:Start work ASAP

  • Pipelined laundry takes 3.5 hours for 4 loads

6 PM

Midnight

7

8

9

11

10

Time

T

a

s

k

O

r

d

e

r


Pipelining lessons l.jpg

40

40

40

30

40

20

A

B

C

D

Pipelining: Lessons

6 PM

7

8

9

  • Improvement in throughput of entire workload without improving any time to complete a single load

  • Pipeline rate limited by slowest pipeline stage

  • Multiple tasks operating simultaneously

  • Potential speedup = Number pipe stages

  • Unbalanced lengths of pipe stages reduces speedup

  • Time to “fill” pipeline and time to “drain” it reduces speedup

Time

T

a

s

k

O

r

d

e

r


Comparison example l.jpg
Comparison: Example

Consider a non-pipelined machine with 5 execution steps of lengths 200 ps, 100 ps, 200 ps, 200 ps, and 100 ps. Due to clock skew and setup, pipelining adds 5 ps of overhead to each instruction stage. Ignoring latency impact, how much speedup in the instruction execution rate will we gain from a pipeline?


Sequential vs pipelined execution l.jpg

800

800

800

200

100

200

200

100

200

100

200

200

100

200

100

200

200

100

200

100

200

200

100

Sequential vs. Pipelined Execution

Sequential Execution

Pipelined Execution

200

100

200

200

100

200

100

200

200

100


Speed up equation for pipelining l.jpg
Speed Up Equation for Pipelining

Speedup from pipelining =

=

=

Ideal CPIpipelined = CPIunpipelined /Pipeline depth

Speedup =



It s not that easy for computers limitation l.jpg
It’s Not That Easy for Computers: Limitation

  • Limits to pipelining: Hazards prevent next instruction from executing during its designated clock cycle

    • Structural hazards: Hardware cannot support this combination of instructions that has to be executed in the same clock cycle (washer+dryer)

    • Data hazards: Instruction depends on result of prior instruction still in pipeline (one sock missing)

    • Control hazards: Pipelining of branches & other instructions. Common solution is to stall the pipeline until the hazard “bubbles” through the pipeline


Instruction level parallelism l.jpg
Instruction Level Parallelism

  • Longer pipeline

  • Laundry analogy: Divide our washer into three machines that perform the wash, rinse and spin steps of a traditional machine

  • To get the full speedup,we need to rebalance the remaining steps so that they are of the same length

  • Amount of parallelism exploited is higher, since there are more operations being overlapped


Advanced pipelining techniques l.jpg
Advanced Pipelining: Techniques

  • Motivation:

    To further exploit the Instruction Level Parallelism (ILP)

  • Multiple Issue

    Replicate the internal components of the computer so that it can launch multiple instructions in every pipeline stage

  • Dynamic Pipeline scheduling (or) Dynamic Pipelining (or) Dynamic Multiple issue by hardware to avoid pipeline hazards


Multiple issue superscalar l.jpg
Multiple Issue: Superscalar

  • Launch multiple instructions in parallel

  • A Superscalar laundry would replace our household washer and dryer with say , three washers and three dryers. Also followed by 3 assistants to fold and put away thee times as much laundry in the same amount of time.

  • Downside extra work needed to keep all the machines busy and transferring load to next pipeline stage.

  • Superscalar is defined as executing more than one instruction per clock cycle


Performance metrics cpi ipc l.jpg
Performance Metrics: CPI & IPC

  • Instruction execution rate exceed the clock rate

  • Example: 6GHz, 4-way multiple-issue microprocessor can execute at a peak rate of 24 billion instructions per second and have a best case of CPI of 0.25

  • Instructions per clock cycle (IPC) (for the above case: 4)

  • Assume a 5 stage pipeline such a processor would have 20 instructions in execution at any given time.


Multiple issue processor decision strategy l.jpg
Multiple issue processor: Decision Strategy

  • Static Multiple Issue

  • Decisions are made at compile time before execution

  • Software based

  • Compiler scheduling

  • VLIW(Very Long Instruction Word)

  • Dynamic Multiple Issue

  • Decisions are made at run/execution time by the processor

  • Dynamic scheduling

  • Hardware based


Static multiple issue processor l.jpg
Static Multiple Issue Processor

  • Issue Packet: Set of instructions which can be paired to form one large instruction with multiple operations (VLIW)

  • Relies on Compiler to take on responsibilities for handling data and control hazards

  • Some of the compiler’s responsibilities may be static branch prediction and code scheduling


Getting cpi 1 static 2 issue pipeline l.jpg
Getting CPI < 1:Static 2 Issue pipeline

  • Superscalar MIPS: 2 instructions, 1 ALU & 1 LOAD instruction

    – Fetch 64-bits/clock cycle; ALU on left, Load on right

    – Can only issue 2nd instruction if 1st instruction issues

    Type Pipe Stages

    ALU instruction IF ID EX MEM WB

    Load instruction IF ID EX MEM WB

    ALU instruction IF ID EX MEM WB

    Load instruction IF ID EX MEM WB

    ALU instruction IF ID EX MEM WB

    Load instruction IF ID EX MEM WB


Static multiple issue datapath l.jpg
Static Multiple Issue: Datapath

ALU/bx xion

ALU

Reg.

file

IM

lw/sw xion

ALU


Example multiple issue code scheduling l.jpg
Example: Multiple Issue code scheduling

  • Loop: lw $t0, 0($s1)

    addu $t0, $t0, $s2

    sw $t0, 0 ($s1)

    addi $s1, $s1, -4

    bne $s1,$zero, Loop

  • After reordering the instructions based on dependencies, we get a CPI=0.8 (or) IPC=1.25


Loop unrolling 4 iterations l.jpg
Loop Unrolling: 4 Iterations

  • Multiple copies of the loop body are made , thus more ILP by overlapping instructions from different iterations

  • CPI=8/14=0.57


Dynamic multiple issue processors l.jpg
Dynamic Multiple-Issue Processors

  • Instructions are issue in order and the processor decides whether zero,one (or) more instructions can issue in a given clock cycle

  • Again achieving good performance requires the compiler to schedule instructions to move dependencies apart and thereby improving the instruction issue rate


Dynamic scheduling definition l.jpg
Dynamic Scheduling: Definition

  • Dynamic pipeline scheduling goes past stalls to find later instructions to execute while waiting for the stall to be resolved

  • Chooses which instruction to execute next by reordering the instructions to avoid stalls (dynamic issue decisions)

  • lw $t0, 20($s2)

    addu $t1, $t0, $s2

    sub $s4, $s4, $t3

    slti $t5, $s4, 20

    bne $s1,$zero, Loop


Hw schemes why l.jpg
HW Schemes: Why?

  • Why in HW at run time?

    • Works when can’t know real dependence at compile time

    • Compiler simpler

    • Code for one machine runs well on another


Dynamic pipeline scheduling model l.jpg
Dynamic Pipeline Scheduling: Model

Inst. Fetch & decode unit

In order

Res. station

Res. station

Res. station

………..

Out order

FP

lw/sw

Integer

………..

Reorder buffer

In order

Commit unit


Hw units working l.jpg
HW Units: Working

  • Inst fetch/decode unit fetches instructions,decodes them and sends each instruction to a corresponding functional unit of the execute stage

  • 5-10 functional units with buffers called reservation stations that holds the operands and operation

  • As soon as buffer contains all the operands , functional unit executes, the result is calculated

  • It is for the commit unit to decide when it is safe to put the result into the register file (or) for store into memory


Dynamic scheduling in order completion l.jpg
Dynamic scheduling: in-order completion

  • To make programs behave as if they run on a non-pipelined computer, the instruction fetch and decode unit is required to issue instructions in order, and the commit unit is required to write results to registers and memory in program execution order (in-order completion)

  • Hence an exception occurs, the computer can point to the last instruction executed and the only registers updated will be all those written by the instructions before exception


Dynamic scheduling speculation l.jpg
Dynamic scheduling: Speculation

  • Speculative execution: Dynamic scheduling can be combined with branch prediction, so after a mispredicted branch , commit unit be able to discard all the results in the execution unit

  • Dynamic scheduling can also be combined with Superscalar execution, so each unit may be committing 4 to 6 instructions per cycle



Conclusion several steps ilp exploitation l.jpg
Conclusion: Several Steps ILP Exploitation


References l.jpg
References

  • Computer Organization & Design, Patterson & Hennessy, 2 & 3 Edition

  • http://www.cs.berkeley.edu/~pattrsn/152F97/index_lectures.html

  • http://www.cse.lehigh.edu/~mschulte/ece401-01/

  • http://paul.rutgers.edu/courses/cs505/S03/

  • http://engineering.dartmouth.edu/~engs116/lectures/engs%20116%20lecture%204-05f.ppt (Pipelining)


ad