Instruction Level Parallelism ILP

Advanced Computer Architecture CSE 8383

Spring 2004 2/19/2004Presented By: Sa’ad Al-HarbiSaeed Abu Nimeh

Outline What’s ILP ILP vs Parallel Processing Sequential execution vs ILP execution Limitations of ILP ILP Architectures

Sequential Architecture Dependence Architecture Independence Architecture

ILP Scheduling Open Problems References

What’s ILP Architectural technique that allows the

overlap of individual machine operations ( add, mul, load, store …)

Multiple operations will execute in parallel (simultaneously)

Goal: Speed Up the execution Example:

load R1 R2 add R3 R3, “1”

add R3 R3, “1” add R4 R3, R2add R4 R4, R2 store [R4] R0

Example: Sequential vs ILP Sequential execution (Without ILP)

Add r1, r2 r8 4 cyclesAdd r3, r4 r7 4 cycles 8 cycles

ILP execution (overlap execution)Add r1, r2 r8 Add r3, r4 r7

Total of 5 cycles

ILP vs Parallel Processing

ILP Overlap individual

machine operations (add, mul, load…) so that they execute in parallel

Transparent to the user

Goal: speed up execution

Parallel Processing Having separate

processors getting separate chunks of the program ( processors programmed to do so)

Nontransparent to the user

Goal: speed up and quality up

ILP Challenges

In order to achieve parallelism we should not have dependences among instructions which are executing in parallel: H/W terminology Data Hazards ( RAW,

WAR, WAW) S/W terminology Data Dependencies

Dependences and Hazards Dependences are a property of

programs If two instructions are data

dependent they can not execute simultaneously

A dependence results in a hazard and the hazard causes a stall

Data dependences may occur through registers or memory

Types of Dependencies

Name dependencies Output dependence Anti-dependence

Data True dependence Control Dependence Resource Dependence

Name dependences Output dependence

When instruction I and J write the same register or memory location. The ordering must be preserved to leave the correct value in the register

add r7,r4,r3div r7,r2,r8

Anti-dependenceWhen instruction j writes a register or memory location that instruction I reads

i: add r6,r5,r4j: sub r5,r8,r11

Data Dependences An instruction j is data

dependent on instruction i if either of the following hold:

instruction i produces a result that may be used by instruction j , or

instruction j is data dependent on instruction k, and instruction k is data dependent on instruction i

LOOP LD F0, 0(R1)

ADD F4, F0, F2

SD F4, 0(R1)

SUB R1, R1, -8

BNE R1, R2, LOOP

Control Dependences A control dependence determines the ordering of an

instruction i, with respect to a branch instruction so that the instruction i is executed in correct program order.

Example:If p1 {

S1;};

If p2 { S2;};

Two constraints imposed by control dependences:

1. An instruction that is control dependent on a branch cannot be moved before the branch

2. An instruction that is not control dependent on a branch cannot be moved after the branch

Resource dependences An instruction is resource-

dependent on a previously issued instruction if it requires a hardware resource which is still being used by a previously issued instruction. e.g.

div r1, r2, r3 div r4, r2, r5

ILP Architectures Computer Architecture: is a contract

(instruction format and the interpretation of the bits that constitute an instruction) between the class of programs that are written for the architecture and the set of processor implementations of that architecture.

In ILP Architectures: + information embedded in the program pertaining to available parallelism between instructions and operations in the program

ILP Architectures Classifications Sequential Architectures: the program is not

expected to convey any explicit information regarding parallelism. (Superscalar processors)

Dependence Architectures: the program explicitly indicates the dependences that exist between operations (Dataflow processors)

Independence Architectures: the program provides information as to which operations are independent of one another. (VLIW processors)

Sequential architecture and superscalar processors Program contains no explicit

information regarding dependencies that exist between instructions

Dependencies between instructions must be determined by the hardware It is only necessary to determine

dependencies with sequentially preceding instructions that have been issued but not yet completed

Compiler may re-order instructions to facilitate the hardware’s task of extracting parallelism

Superscalar Processors Superscalar processors attempt to

issue multiple instructions per cycle However, essential dependencies are

specified by sequential ordering so operations must be processed in sequential order

This proves to be a performance bottleneck that is very expensive to overcome

Dependence architecture and data flow processors The compiler (programmer) identifies the

parallelism in the program and communicates it to the hardware (specify the dependences between operations)

The hardware determines at run-time when each operation is independent from others and perform scheduling

Here, no scanning of the sequential program to determine dependences

Objective: execute the instruction at the earliest possible time (available input operands and functional units).

Dependence architectures Dataflow processors Dataflow processors are representative of

Dependence architectures Execute instruction at earliest possible time

subject to availability of input operands and functional units

Dependencies communicated by providing with each instruction a list of all successor instructions

As soon as all input operands of an instruction are available, the hardware fetches the instruction

The instruction is executed as soon as a functional unit is available

Few Dataflow processors currently exist

Dataflow strengths and limitations Dataflow processors use control

parallelism alone to fully utilize the FU. Dataflow processor is more successful

than others at looking far down the execution path to find control parallelism

When successful its better than speculative execution: Every instruction is executed is useful Processor does not have to deal with error

conditions, because of speculative operations

Independence architecture and VLIW processors By knowing which operations are independent,

the hardware needs no further checking to determine which instructions can be issued in the same cycle

The set of independent operations >> the set of dependent operations

Only a subset of independent operations are specified

The compiler may additionally specify on which functional unit and in which cycle an operation is executed

The hardware needs to make no run-time decisions

VLIW processors Operation vs instruction

Operation: is an unit of computation (add, load, branch = instruction in sequential ar.)

Instruction: set of operations that are intended to be issued simultaneously

Compiler decides which operation to go to each instruction (scheduling)

All operations that are supposed to begin at the same time are packaged into a single VLIW instruction

VLIW strengths In hardware it is very simple:

consisting of a collection of function units (adders, multipliers, branch units, etc.) connected by a bus, plus some registers and caches

More silicon goes to the actual processing (rather than being spent on branch prediction, for example),

It should run fast, as the only limit is the latency of the function units themselves.

Programming a VLIW chip is very much like writing microcode

VLIW limitations The need for a powerful compiler, Increased code size arising from

aggressive scheduling policies, Larger memory bandwidth and register-

file bandwidth, Limitations due to the lock-step

operation, binary compatibility across implementations with varying number of functional units and latencies

Summary: ILP Architectures

Sequential Architecture

Dependence Architecture

Independence Architectures

Additional info required in the program

None Specification of dependences between operations

Minimally, a partial list of independences. A complete specification of when and where each operation to be executed

Typical kind of ILP processor

Superscalar Dataflow VLIW

Dependences analysis

Performed by HW Performed by compiler

Performed by compiler

Independences analysis

Performed by HW Performed by HW Performed by compiler

Scheduling Performed by HW Performed by HW Performed by compiler

Role of compiler Rearranges the code to make the analysis and scheduling HW more successful

Replaces some analysis HW

Replaces virtually all the analysis and scheduling HW

ILP Scheduling

Static Scheduling boosted by parallel code optimization

done by the compiler The processor

receives dependency-free and optimized code for parallel execution

Typical for VLIWs and a few pipelined processors (e.g. MIPS)

Dynamic Scheduling without static parallel

code optimization

done by the processor

The code is not optimized for parallel execution. The processor detects and resolves dependencies on its own

Early ILP processors (e.g. CDC 6600, IBM 360/91 etc.)

Dynamic Scheduling boosted by static parallel

code optimization

done by processor in conjunction with parallel optimizing compiler

The processor receives optimized code for parallel execution, but it detects and resolves dependencies on its own

Usual practice for pipelined and superscalar processors (e.g. RS6000)

ILP Scheduling: Trace scheduling An optimization technique that has

been widely used for VLIW, superscalar, and pipelined processors.

It selects a sequence of basic blocks as a trace and schedules the operations from the trace together.

Example:Instr1Instr2Branch xInstr3

Trace Scheduling Extract more ILP Increase machine fetch bandwidth

by storing logically consecutive blocks in physically contiguous cache location (possible to fetch multiple basic blocks in one cycle)

Trace scheduling can be implemented by hardware or software

Trace Scheduling in HW Hardware technique makes use of a large

amount of information in dynamic execution to format traces dynamically and schedule the instructions in trace more efficiently.

Since the dependency and memory access addresses have been solved in dynamic execution, instructions in trace can be reordered more easily and efficiently.

Example: trace cache approach

Trace scheduling in SW Supplement to machines without

hardware trace scheduling support. Formats traces based on static

profiled data, and schedules instructions using traditional compiler scheduling and optimization technique.

It faces some difficulties like code explosion and exception handling.

ILP open problems Pipelined scheduling : Optimized scheduling of pipelined

behavioral descriptions. Two simple type of pipelining (structural and functional).

Controller cost : Most scheduling algorithms do not consider the controller costs which is directly dependent on the controller style used during scheduling.

Area constraints : The resource constrained algorithms could have better interaction between scheduling and floorplanning.

Realism : Scheduling realistic design descriptions that contain

several special language constructs. Using more realistic libraries and cost functions. Scheduling algorithms must also be expanded to

incorporate different target architectures.

References Instruction-Level Parallel Processing: History, Overview and Perspective. B. Ramakrishna Rau,

Joseph A. Fisher. Journal of Supercomputing, Vol. 7, No. 1, Jan. 1993, pages 9-50.

Limits of Control Flow on Parallelism. Monica S. Lam, Robert P. Wilson. 19th ISCA, May 1992, pages 19-21.

Global Code Generation for Instruction-Level Parallelism: Trace Scheduling-2. Joseph A. Fisher. Technical Report, HPLabs HPL-93-43, Jun. 1993.

VLIW at IBM Research http://www.research.ibm.com/vliw

Intel and HP hope to speed CPUs with VLIW technology that's riskier than RISC, Dick Pountain http://www.byte.com/art/9604/sec8/art3.htm

Hardware and Software Trace Scheduling http://charlotte.ucsd.edu/users/yhu/paperlist/summary.html

ILP open problemshttp://www.ececs.uc.edu/~ddel/projects/dss/hls_paper/node9.html

Computer Architecture A Quantitative Approach, Hennessy & Patterson, 3rd edition, M Kaufmann