1. Advanced Pipelining
• Superpiplining: Increase the depth of the pipeline (deep pipline)
– to overlap more instructions
• Multiple issue: start more than one instruction each cycle
– To have CPI<1
• Loop unrolling : a technique to get better instr scheduling
– To expose more ILP
• “Superscalar” processors
– DEC Alpha 21264: 9 stage pipeline, 6 instruction issue
– dynamic multiple issue: processor dynamically chooses which
instructions to execute in a given cycle while trying to avoid hazard.
• VLIW: very long instruction word, static multiple issue
(relies more on compiler technology - packing instructions
and handling hazard)
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2. Advanced Pipelining
• Static multiple issue
– compiler decides multiple issue before execution
• Dynamic multiple issue
– processor decides multiple issue during execution
• Problems of multiple issue
– How to package instructions into issue slots
– How to deal with data and control hazard
• Speculation – the compiler or processor guesses the outcome
of an instruction to remove it as a dependence in executing
other instructions
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3. Static Multiple Issue
• Issue packet
– the set of instructions that issue together in a clock cycle
• SMI concept
– regard an issue packet as one large instruction with multiple operations
– Very Long Instruction Word (VLIW) or Explicitly Parallel Instruction
Computer (EPIC) by intel IA-64
• Assume two instrs may be issued per clock cycle:
– 1 for an integer ALU op or branch
– 1 for a load or store
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4. A Static Two-issue Datapath
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5. Static Multiple Issues
• Extra resources (issuing 2 instrs per cycle)
– Another 32bits from instruction memory
– need extra ports in the register file
– Another ALU handling address calculation for data transfer
• Without these extra resources ⇒ structural hazards
• More ambitious compiler or h/w scheduling technique
– loads have a latency of 1 clock cycle in simple five-stage pipeline
• In two-issue pipeline, the next two inst cannot use the load result
without stalling.
– ALU that has no use latency in simple five-stage pipeline
• Become 1-instr use latency (the result cannot be used in paired instr)
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6. Example: Multiple-issue Code Scheduling
• How would this loop be scheduled on a two-issue pipeline for MIPS?
Reorder the instrs to avoid as many pipeline stalls as possible.
Loop: lw $t0, 0($s1)
addu $t0, $t0, $s2
sw $t0, 0($s1)
addi $s1, $s1, -4
bne $s1, $zero, Loop
• Ans: 4 clocks per loop iteration
CPI = 4/5= 0.8
ALU or branch inst. Data transfer inst. Clock cycle
Loop: lw $t0, 0($s1) 1
addi $s1, $s1, -4 2
addu $t0, $t0, $s2 3
bne $s1, $zero,Loop sw $t0, 4($s1) 4
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7. Example: Loop Unrolling for Multiple-issue Pipelines
Loop unrolling:
• multiple copies of the loop body are made &
instrs from different iterations are scheduled together
• Register renaming - remove antidependence (name dependence)
Ex. Assume the loop index is a multiple of four
ALU or branch inst. Data transfer Clock
Loop: lw $t0, 0($s1) inst. cycle
addu $t0, $t0, $s2 Loop: addi $s1,$s1, -16 lw $t0, 0($s1) 1
sw $t0, 0($s1) lw $t1,12($s1) 2
addi $s1, $s1, -4 addu $t0, $t0, $s2 lw $t2,8($s1) 3
bne $s1, $zero, Loop 4
addu $t1, $t1, $s2 lw $t3,4($s1)
addu $t2, $t2, $s2 sw $t0,16($s1) 5
• Ans:
addu $t3, $t3, $s2 sw $t1,12($s1) 6
– 8/4 clocks per iteration
sw $t2,8($s1) 7
– CPI = 8/14=0.57 bne $s1, $zero, Loop 8
sw $t3,4($s1)
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8. The BIG Picture
• Both pipelining and multiple-issue execution
increase peak instr throughput.
• Longer pipelines and wider multiple-issue put even
more pressure on the compiler to deliver on the
performance potential of the hardware.
• Hardware designers must ensure correct execution
of all instr sequences.
• Compiler writers must understand the pipeline to
generate the appropriate code and then to achieve
best performance.
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9. Dynamic Pipeline Scheduling
• SuperScalar processor – the pipeline is divided into three
major units
1. an instr fetch and decode unit:
« fetches instrs, decodes them, & sends each instr to related
functional units
2. functional units (FUs):
« Reservation station: each FU has buffers
« Once the buffer contains all its operands and the functional
unit is ready to execute, the result is calculated.
3. a commit unit:
« decide when to put the result into the reg file or memory
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10. The Dynamically scheduled Pipeline
Instruction fetch In-order issue
and decode unit
Reservation Reservation … Reservation Reser vation
station station station station
Floating Load/ Out-of-order
Functional Integer Integer … Out-of-order execute
units point Store execution
In-order commit
Commit
unit
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11. The Dynamically scheduled Pipeline
• Motivations for dynamic scheduling:
– Not all stalls are predictable (e.g., cache miss). (Ch7)
– If dynamic branch prediction is used (it cannot know the
execution order of instruction at compile time)
– Pipeline latency and issue width change from one
implementation to another.
Dynamic scheduling allows to hide the multiple
versions of hardware implementations of the same
instruction set.
Old code will get benefit of a new implementation
without the need for recompilation.
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