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Limits of Instruction-LevelParallelism

Presentation by: Robert Duckles

CSE 520

Paper being presented:Limits of Instruction-Level Parallelism

David W. WallWRL Research Report, November 1993



What is ILP?

Instructions that do not have dependencies on each other;can be executed in any order.

r1 := 0[r9] r1 := 0[r9]r2 := 17 r2 := r1 + 174[r3] := r6 4[r2] := r6(has ILP) (no ILP)

Super-scalar machine – a machine that can issue multipleindependent instructions in the same clock cycle.



Definition of Parallelism

Parallelism = (Number of Instructions) / (Number of Cycles it takes to execute)

r1 := 0[r9] r1 := 0[r9]r2 := 17 r2 := r1 + 174[r3] := r6 4[r2] := r6

Parallelism = 3 Parallelism = 1



How much parallelism is there?

That depends how hard you want to look for it...

Ways to increase ILP: Register renaming Branch prediction Alias analysis Indirect-jump prediction



Low estimate for ILP

Programs are made up of ―basic blocks‖— uninterrupted

sequences of instructions with no branches.

On average, in typical applications, basic blocks are ~10instructions long.

Each basic block has parallelism of around 3.



High estimate for ILP

If you look beyond a basic block, at the entire scope of a program,studies have shown that an ―omniscient‖ scheduler can achieve

parallelism of > 1000 in some numerical applications.

―Omniscient‖ scheduling can be implemented by saving a trace of a program execution, and using an oracle to schedule it. Theoracle knows what will happen, and thus can create a perfectexecution schedule.

Practical, achievable ILP should be between 3 and 1000.



Types of dependencies

Types of dependencies:

* True dependency - given the computations involved, the dependency must exist* False dependency - dependency happens to exist as an artifact of the code generationengine. E.g., two independent values are allocated to the same register by the compiler.

r1 := 20[r4] r2 := r1 + r4... ...r2 := r1 + 1 r1 := r17 - 1

(a) true data dependency (b) anti-dependency

r1 := r2 * r3 if r17 = 0 goto L...

... r1 := r2 + r3...

r1 := 0[r7] L:(c) output dependency (d) control dependency



Register renaming

The compiler's register allocation algorithm can insert falsedependencies by assigning unrelated values to the sameregister. We can undo this damage by assigning each value to aunique register so that only true dependencies remain. However, machines have a finite number of registers, so wecan never guarantee perfect parallelism.



Register renaming



Alias analysis

We often have registers that point to a memory location or containa memory offset. Can two memory pointers point to the sameplace in memory? If so, there might be a dependency. We're not sure yet. We can try to inspect pointer values at runtime to see if they point

to overlapping memory.



Alias analysis



Limitations of branch prediction:

We can correctly predict around ~0.9 by counting which branches havebeen recently taken, and taking the most common one.



Indirect-jump prediction

If we jump to an address that is not known at compile time--for example, if a destinationaddress is calculated into a register at runtime.

This is often the case for "return" constructs, where the the calling function's address isstored on the stack. In this case, we can do indirect-jump prediction.



Latency

Multi-cycle instructions can greatly decrease parallelism



Window size

The window size is the maximum number ofinstructions that can appear in the pending cyclelist.



Overall results



Conclusions: the ILP Wall

Even with ―perfect‖ techniques, most real applications hit an ILP limit of around 20 With reasonable, practical methods, it's even worse—it's

very difficult to get an ILP above 10.



Relationship to Term Project

Our term project is about optimization techniques for AMD64Opteron/Athlon processors. Maximizing ILP is essential to getting the most performance

out of any processor. Branch prediction, register renaming, etc., are all particularlyrelevant optimizations

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