Back-End Optimizations in Software Creation pdf417 2d barcode in Software Back-End Optimizations

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5.3 Back-End Optimizations generate, create pdf417 2d barcode none for software projects QR Codes In this chapter an barcode pdf417 for None d in the previous one we have examined three optimizations based on speculation, which we can rank in order of importance as: 1. Branch prediction. 2.

Load-bypassing stores. 3. Prediction of load latency.

Clearly, branch prediction is a must. It is present, quite often in very sophisticated ways, in all standard microprocessors. Without it, most of the performance advantages of deeply pipelined superscalar processors would evaporate.

Load speculation is also important in that loads are often producers of values for subsequent instructions and delaying their execution might be quite detrimental to overall. Back-End: Instruction Scheduling, Memory Access Instructions, and Clusters performance. Howev Software PDF-417 2d barcode er, not all loads are of equal importance, as we shall see further in Section 5.3.

2. Most high-performance microprocessors today have some form of load speculation. Finally, guessing that the load latency will be that of a rst-level cache hit is common for the wakeup and select operations.

We shall see in the next chapter ways to tolerate or hide load latency in the cache hierarchy. In this section, we brie y present some optimizations that have been proposed as means to increase performance or to reduce power consumption, goals that are often con icting. Although none of these optimizations are standard fare, they might be useful in future microprocessor generations.

Inasmuch as none of them have been implemented in silicon except in a restricted fashion for the last one, we only brie y present the concepts, assess potential bene ts, and point to implementation dif culties. 5.3.

1 Value Prediction As we saw as early as 2, three types of hazards can slow down pipelined processors: r Structural hazards, such as a paucity of functional units to exploit fully instruction-level parallelism, or limited capacity for structures such as instruction windows, reorder buffer, or physical registers. The increase in capacity is not always an answer, and we discuss this further in Section 5.3.

3. r Control hazards, such as those induced by transfer of control instructions. Branch prediction is the main mechanism by which the effect of these hazards is minimized.

r Data hazards, such as data dependencies and memory dependencies. Outof-order processors remove non-RAW dependencies with the use of register renaming. Forwarding and bypassing take care of interdependent instructions that can be scheduled suf ciently far apart without creating bubbles in the pipeline.

Load speculation permits one to lessen the number of situations where false memory dependencies could slow down the ow of instructions. However, we have not been able to deal with true data dependencies whereby a RAW dependency cannot be hidden because there is not enough work for the processor to do before the dependent instruction needs to execute. In the rst simple pipeline of 2, this happened when a load was followed by an instruction requiring the result of the load.

In more complex processors, this particular example, along with simple generalizations of such a producer consumer paradigm, is still a source of performance (IPC) degradation. A possible remedy for this true dependence is to predict the value of the result that is needed. How realistic is it to envision such value prediction If we follow the model of a branch predictor (cf.

Figure 4.1) and apply it to value prediction, we have to de ne the event for which prediction will occur its scope as well as the predictor s structure and feedback mechanism. Again, as in other forms of prediction, our only way to predict the future is to rely on the recent past.

It is therefore necessary to assess. 5.3 Back-End Optimizations Alpha AXP 100.0 Va Software barcode pdf417 lue locality (%) 80.0 60.

0 40.0 20.0 0.

0. Value locality (%). Figure 5.4. Value Software pdf417 locality for load instructions.

The white bars are strict last values; the black bars are for the recurrence of one of the last 16 values (data from Lipasti and Shen [LS96]).. 100.0 80.0 60.

0 40.0 20.0 0.

0. 27 1 c co cjp c1 m Software PDF-417 2d barcode eg pr do ess eq duc nt ga ott w gp k er hy gre f dr p o m 2d pe g pe qu rl ic sw sk m c to 25 m 6 ca t xl v is p cc 1-. if (parts of) prog rams exhibit some value locality, for example, the repeated occurrence of a previously seen value in a storage location, or the repetition of a known pattern such as consecutive values that differ from each other by a common stride. Clearly, looking at the whole addressing space is not practical, and realistically value locality must be limited to the architectural registers and the instructions that write into these registers. Figure 5.

4 shows the value locality for a variety of benchmarks when the scope is limited to load instructions and the value locality is de ned as the fraction of the time the value written in a register was the same as the last one (left bars) or the same as one of the last 16 instances (right bars). As can be seen, in some benchmarks the value locality is quite high, over 40% even with a strict last value, whereas in the remaining three it is quite low (see for example the next to last benchmark tomcatv). If the same experiment is repeated for all instructions (that is, unlimited scope), the value locality diminishes signi cantly.

Leaving aside for a moment how values are predicted, let us assume a predictor structure that holds predicted values for some selected instructions. The hardware structure for the predictor can take forms similar to those for branch prediction, including a con dence counter for whether the prediction should be taken or not. The value predictor can be checked in some stage of the front-end.

If there is a predicted value, say for instruction i, it can be stored in the renamed result register, and for subsequent instructions, it looks as if instruction i had executed normally. Now of course, not only does the value-predicted instruction i need to be executed to be sure that the prediction is correct, but the result of instruction i must be compared with the predicted value. A suggested implementation is to store the prediction in the reservation station when the instruction is issued.

At the end of execution of instruction i, its result is compared with the predicted value. If the. cc 12 co cjp71 m e pdf417 for None g pr e do ss d eq uc nt o ga tt w gp k er f hy gre dr p o m 2d pe g pe qu rl ic k sw s m c to 25 m 6 ca t xl v is p.
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