ADVANCED COMPUTER ARCHITECTURE (ACA)–Unit 1 - Instruction-Level Parallelism and Dynamic Exploitation

Anna University

Department of Computer Science Engineering



Subject Code : CS2354

Unit 1

Instruction-Level Parallelism and Dynamic Exploitation

What is meant by Instruction Level Parallelism

Instruction-Level Parallelism: Concepts and Challenges:

Instruction-level parallelism (ILP) is the potential overlap the execution of instructions using pipeline concept to improve performance of the system. The various techniques that are used to increase amount of parallelism are reduces the impact of data and control hazards and increases processor ability to exploit parallelism

There are two approaches to exploiting ILP.

1. Static Technique – Software Dependent

2. Dynamic Technique – Hardware Dependent

table1The simplest and most common way to increase the amount of parallelism is loop-level parallelism. Here is a simple example of a loop, which adds two 1000 -element arrays, that is completely parallel:

for (i=1;i<=1000; i=i+1) x[i] = x[i] + y[i];

CPI (Cycles per Instruction) for a pipelined processor is the sum of the base CPI and all contributions from stalls:

Pipeline CPI = Ideal pipeline CPI + Structural stalls + Data hazard stalls + Control stalls

The ideal pipeline CPI is a measure of the maximum performance attainable by the implementation. By reducing each of the terms of the right-hand side, we minimize the overall pipeline CPI and thus increase the IPC (Instructions per Clock).

Various types of Dependences in ILP. Data Dependence and Hazards:

To exploit instruction-level parallelism, determine which instructions can be executed in parallel. If two instructions are parallel, they can execute simultaneously in a pipeline without causing any stalls. If two instructions are dependent they are not parallel and must be executed in order.

There are three different types of dependences: data dependences (also called true data dependences), name dependences, and control dependences.

Data Dependences:

An instruction j is data dependent on instruction i if either of the following holds:

• 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.

The second condition simply states that one instruction is dependent on another if there exists a chain of dependences of the first type between the two instructions. This dependence chain can be as long as the entire program.

For example, consider the following code sequence that increments a vector of values in memory (starting at 0(R1) and with the last element at 8(R2)) by a scalar in register F2:

Loop: L.D F0,0(R1) ; F0=array element ADD.D F4,F0,F2 ; add scalar in F2 S.D F4,0(R1) ;store result DADDUI R1,R1,#-8 ;decrement pointer 8 bytes (/e BNE R1,R2,LOOP ; branch R1!=zero

The dependence implies that there would be a chain of one or more data hazards between the two instructions. Executing the instructions simultaneously will cause a processor with pipeline interlocks to detect a hazard and stall, thereby reducing or eliminating the overlap. Depend ences are a property of programs.

Whether a given dependence results in an actual hazard being detected and whether that hazard actually causes a stall are properties of the pipeline organization. This difference is critical to understanding how instruction-level parallelism can be exploited.

The presence of the dependence indicates the potential for a hazard, but the actual hazard and the length of any stall is a property of the pipeline. The importance of the data dependences is that a dependence

(1) indicates the possibility of a hazard,

(2) Determines the order in which results must be calculated, and

(3) Sets an upper bound on how much parallelism can possibly be exploited.

Name Dependences

The name dependence occurs when two instructions use the same register or memory location, called a name, but there is no flow of data between the instructions associated with that name.

There are two types of name dependences between an instruction i that precedes instruction j in program order:

• An antidependence between instruction i and instruction j occurs when instruction j writes a register or memory location that instruction i reads. The original ordering must be preserved to ensure that i reads the correct value.

• An output dependence occurs when instruction i and instruction j write the same register or memory location. The ordering between the instructions must be preserved to ensure that the value finally written corresponds to instruction j.

Both anti-dependences and output dependences are name dependences, as opposed to true data dependences, since there is no value being transmitted between the instructions. Since a name dependence is not a true dependence, instructions involved in a name dependence can execute simultaneously or be reordered, if the name (register number or memory location) used in the instructions is changed so the instructions do not conflict.

This renaming can be more easily done for register operands, where it is called register renaming. Register renaming can be done either statically by a compiler or dynamically by the hardware. Before describing dependences arising from branches, let’s examine the relationship between dependences and pipeline data hazards.

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. Every instruction, except for those in the first basic block of the program, is control dependent on some set of branches, and, in general, these control dependences must be preserved to preserve program order. One of the simplest examples of a control dependence is the dependence of the statements in the “then” part of an if statement on the branch. For example, in the co de segment:

if p1 { S1;


if p2 { S2;


S1 is control dependent on p1, and S2is control dependent on p2 but not on p1. In general, there are two constraints imposed by control dependences:

1. An instruction that is control dependent on a branch cannot be moved before the branch so that its execution is no longer controlled by the branch. For example, we cannot take an instruction from the then-portion of an if-statement and move it before the if- statement.

2. An instruction that is not control dependent on a branch cannot be moved after the branch so that its execution is controlled by the branch. For example, we cannot take a statement before the if-statement and move it into the then-portion.

Control dependence is preserved by two properties in a simple pipeline, First, instructions execute in program order. This ordering ensures that an instruction that occurs before a branch is executed before the branch. Second, the detection of control or branch hazards ensures that an instruction that is control dependent on a branch is not executed until the branch direction is known.

Data Hazard and various hazards in ILP. Data Hazards

A hazard is created whenever there is a dependence between instructions, and they are close

enough that the overlap caused by pipelining, or other reordering of instructions, would change the order of access to the operand involved in the dependence.

Because of the dependence, preserve order that the instructions would execute in, if executed sequentially one at a time as determined by the original source program. The goal of both our software and hardware techniques is to exploit parallelism by preserving program order only where it affects the outcome of the program. Detecting and avoiding hazards ensures that necessary program order is preserved.

Data hazards may be classified as one of three types, depending on the order of read and write accesses in the instructions.

Consider two instructions i and j, with i occurring before j in program order. The possible data hazards are RAW (read after write) — j tries to read a source before i writes it, so j incorrectly gets the old value. This hazard is the most common type and corresponds to a true data dependence. Program order must be preserved to ensure that j receives the value from i. In the simple common five-stage static pipeline a load instruction followed by an integer ALU instruction that directly uses the load result will lead to a RAW hazard.

WAW (write after write) — j tries to write an operand before it is written by i. The writes end up being performed in the wrong order, leaving the value written by i rather than the value written by j in the destination. This hazard corresponds to an output dependence. WAW hazards are present only in pipelines that write in more than one pipe stage or allow an instruction to

proceed even when a previous instruction is stalled. The classic five-stage integer pipeline writes a register only in the WB stage and avoids this class of hazards.

WAR (write after read) — j tries to write a destination before it is read by i, so i incorrectly gets the new value. This hazard arises from an antidependence. WAR hazards cannot occur in most static issue pipelines even deeper pipelines or floating point pipelines because all reads are early (in ID) and all writes are late (in WB). A WAR hazard occurs either when there are some instructions that write results early in the instruction pipeline, and other instructions that read a source late in the pipeline or when instructions are reordered.

Dynamic Scheduling

Overcoming Data Hazards with Dynamic Scheduling:

The Dynamic Scheduling is used handle some cases when dependences are unknown at a compile time. In which the hardware rearranges the instruction execution to reduce the stalls while maintaining data flow and exception behavior.

It also allows code that was compiled with one pipeline in mind to run efficiently on a different pipeline. Although a dynamically scheduled processor cannot change the data flow, it tries to avoid stalling when dependences, which could generate hazards, are present.

Dynamic Scheduling:

A major limitation of the simple pipelining techniques is that they all use in -order instruction issue and execution: Instructions are issued in program order and if an instruct ion is stalled in the pipeline, no later instructions can proceed. Thus, if there is a dependence between two closely spaced instructions in the pipeline, this will lead to a hazard and a stall. If there are multiple functional units, these units could lie idle. If instruction j depends on a long-running instruction i, currently in execution in the pipeline, then all instructions after j must be stalled until i is finished and j can execute. For example, consider this code:

DIV.D F0,F2,F4 ADD.D F10,F0,F8 SUB.D F12,F8,F14

Out-of-order execution introduces the possibility of WAR and WAW hazards, which do not exist in the five-stage integer pipeline and its logical extension to an in-order floating-point pipeline.

Out-of-order completion also creates major complications in handling exceptions. Dynamic scheduling with out-of-order completion must preserve exception behavior in the sense that exactly those exceptions that would arise if the program were executed in strict program order actually do arise.

Imprecise exceptions can occur because of two possibilities:

1. The pipeline may have already completed instructions that are later in program order than the instruction causing the exception, and

2. The pipeline may have not yet completed some instructions that are earlier in program order than the instruction causing the exception.

To allow out-of-order execution, we essentially split the ID pipe stage of our simple five -stage pipeline into two stages:

1 Issue—Decode instructions, check for structural hazards.

2 Read operands—Wait until no data hazards, then read operands.

In a dynamically scheduled pipeline, all instructions pass through the issue stage in order (in - order issue); however, they can be stalled or bypass each other in the second stage (read operands) and thus enter execution out of order.

Score-boarding is a technique for allowing instructions to execute out-of-order when there are sufficient resources and no data dependences; it is named after the CDC 6600 scoreboard, which developed this capability. We focus on a more sophisticated technique, called Tomasulo’s algorithm, that has several major enhancements over scoreboarding.

Tomasulo’s Approach

Dynamic Scheduling Using Tomasulo’s Approach :

This scheme was invented by RobertTomasulo, and was first used in the IBM 360/91. it uses register renaming to eliminate output and anti-dependencies, i.e. WAW and WAR hazards. Output and anti-dependencies are just name dependencies, there is no actual data dependence.

Tomasulo's algorithm implements register renaming through the use of what are called reservation stations. Reservation stations are buffers which fetch and store instruction operands as soon as they are available

In addition, pending instructions designate the reservation station that will provide their input. Finally, when successive writes to a register overlap in execution, only the last one is actually used to update the register. As instructions are issued, the register specifies for pending operands are renamed to the names of the reservation station, which provides register renaming.


the basic structure of a Tomasulo-based MIPS processor, including both the floating-point unit and the load/store unit.

Instructions are sent from the instruction unit into the instruction queue from which they are issued in FIFO order.

The reservation stations include the operation and the actual operands, as well as information used for detecting and resolving hazards. Load buffers have three functions: hold the components of the effective address until it is computed, track outstanding loads that are waiting on the memory, and hold the results of completed loads that are waiting for the CDB.

Similarly, store buffers have three functions: hold the components of the effective addr ess until it is computed, hold the destination memory addresses of outstanding stores that are waiting for the data value to store, and hold the address and value to store until the memory unit is available.

All results from either the FP units or the load unit are put on the CDB, which goes to the FP register file as well as to the reservation stations and store buffers. The FP adders implement addition and subtraction, and the FP multipliers do multiplication and division.

There are only three steps in Tomasulo’s Aprroach :

1. Issue—Get the next instruction from the head of the instruction queue. If there is a matching reservation station that is empty, issue the instruction to the station with the operand values (renames registers)

2. Execute(EX)— When all the operands are available, place into the corresponding

reservation stations for execution. If operands are not yet available, monitor the common data bus (CDB) while waiting for it to be computed.

3. Write result (WB)When the result is available, write it on the CDB and from there into the registers and into any reservation stations (including store buffers) waiting for this result. Stores also write data to memory during this step: When both the address and data value are available, they are sent to the memory unit and the store completes.

Each reservation station has six fields:

• Op—The operation to perform on source operands S1 and S2.

• Qj, Qk—The reservation stations that will produce the corresponding source operand; a value of zero indicates that the source operand is already available in Vj or Vk, or is unnecessary.

• Vj, Vk—The value of the source operands. Note that only one of the V field or the Q field is valid for each operand. For loads, the Vk field is used to the offset from the instruction.

• A–used to hold information for the memory address calculation for a load or store.

• Busy—Indicates that this reservation station and its accompanying functional unit are occupied.

Reduce Branch Costs with Dynamic Hardware Prediction

Basic Branch Prediction and Branch-Prediction Buffers

· The simplest dynamic branch-prediction scheme is a branch-pr ediction buffer or branch history table.

· A branch-prediction buffer is a small memory indexed by the lower portion of the address of the branch instruction.

· The memory contains a bit that says whether the branch was recently taken or not. if the prediction is correct—it may have been put there by another branch that has the same low-order address bits.

· The prediction is a hint that is assumed to be correct, and fetching begins in the predicted direction. If the hint turns out to be wrong, the prediction bit is inverted and stored back.

· The performance of the buffer depends on both how often the prediction is for the branch of interest and how accurate the prediction is when it matches.

· This simple one-bit prediction scheme has a performance shortcoming: Even if a branch is almost always taken, we will likely predict incorrectly twice, rather than once, when it is not taken.


The two bits are used to encode the four states in the system. In a counter implementation, the counters are incremented when a branch is taken and decremented when it is not taken; the counters saturate at 00 or 11. One complication of the two-bit scheme is that it updates the prediction bits more often than a one-bit predictor, which only updates the prediction bit on a mispredict. Since we typically read the prediction bits on every cycle, a two-bit predictor will typically need both a read and a write access port.

The two-bit scheme is actually a specialization of a more general scheme that has an n-bit saturating counter for each entry in the prediction buffer. With an n-bit counter, the counter can n take on values between 0 and 2n1 : when the counter is greater than or equal to one half of its maximum value (2), the branch is predicted as taken; otherwise, it is predicted untaken.

To exploit more ILP, the accuracy of our branch prediction becomes critical, this problem in two ways: by increasing the size of the buffer and by increasing the accuracy of the scheme we use for each prediction.

Correlating Branch Predictors:

These two-bit predictor schemes use only the recent behavior of a single branch to predict the future behavior of that branch. It may be possible to improve the prediction accuracy if we also

look at the recent behavior of other branches rather than just the branch we are trying to predict. Consider a small code fragment from the SPEC92 benchmark

if (aa==2)


if (bb==2)


if (aa!=bb) {

Here is the MIPS code that we would typically generate for this code fragment assuming that aa and bb are assigned to registers R1 and R2:


BNEZ R3,L1 ;branch b1 (aa!=2) DADD R1,R0,R0 ;aa=0

L1: DSUBUI R3,R2,#2

BNEZ R3,L2 ;branch b2(bb!=2)

DADD R2,R0,R0 ; bb=0 L2: DSUBU R3,R1,R2 ;R3=aa-bb

BEQZ R3,L3 ;branch b3 (aa==bb)

Let’s label these branches b1, b2, and b3. The key observation is that the behavior of branch b3 is correlated with the behavior of branches b1 and b2. Clearly, if branches b1 and b2 are both not taken (i.e., the if conditions both evaluate to true and aa and bb are both assigned 0), then b3 will be taken, since aa and bb are clearly equal. A predictor that uses only the behavior of a single branch to predict the outcome of that branch can never capture this behavior.

Branch predictors that use the behavior of other branches to make a prediction are called correlating predictors or two-level predictors.

Tournament Predictors: Adaptively Combining Local and Global Predictors

The primary motivation for correlating branch predictors came from the observation that the standard 2-bit predictor using only local information failed on some important branches and that by adding global information, the performance could be improved.

Tournament predictors take this insight to the next level, by using multiple predictors, usually one based on global information and one based on local information, and combining them with a selector.

Hardware speculation

·Hardware-based speculation combines three key ideas: dynamic branch prediction to choose which instructions to execute, speculation to allow the execution of instructions before the control dependences are resolved and dynamic scheduling to deal with the scheduling of different combinations of basic blocks.

·Hardware-based speculation follows the predicted flow of data values to choose when to execute instructions. This method of executing programs is essentially a data-flow execution: operations execute as soon as their operands are available.

·The approach is implemented in a number of processors (PowerPC 603/604/G3/G4, MIPS R10000/R12000, Intel Pentium II/III/ 4, Alpha 21264, and AMD K5/K6/Athlon), is to implement speculative execution based on Tomasulo’s algorithm.

·The key idea behind implementing speculation is to allow instructions to execute out of order but to force them to commit in order and to prevent any irrevocable action until an instruction commits.

·In the simple single-issue five-stage pipeline we could ensure that instructions committed in order, and only after any exceptions for that instruction had been detected, simply by moving writes to the end of the pipeline.


Limitations of ILP The Hardware Model

An ideal processor is one where all artificial constraints on ILP are removed. The only limits on

ILP in such a processor are those imposed by the actual data flows either through registers or memory.

The assumptions made for an ideal or perfect processor are as follows:

1. Register renamingThere are an infinite number of virtual registers available and hence all WAW and WAR hazards are avoided and an unbounded number of instructions can begin execution simultaneously.

2. Branch prediction—Branch prediction is perfect. All conditional branches are predicted exactly.

3. Jump prediction—All jumps (including jump register used for return and computed jumps) are perfectly predicted. When combined with perfect branch prediction, this is equivalent to having a processor with perfect speculation and an unbounded buffer of instructions available for execution.

4. Memory-address alias analysis—All memory addresses are known exactly


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