Instructor: | Prof. Milo Martin |
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Due: | Thursday, Oct 28 at Noon (at the start of class) |

In this assignment you will explore the effectiveness of branch direction prediction (taken vs not taken) on an actual program. Your task is to use the trace format (see Trace Format document) to evaluate the effectiveness of a few simple branch prediction schemes.

To do this, you'll write a program that reads in the trace and simulates different branch prediction schemes and different predictor sizes. You can write the program in whatever language you like; although we are going to ask for a printout of your source code, the end result of this assignment are the experimental results (and not the program source code).

In this assignment we want to focus only on **conditional** branches.
In the trace format, conditional branches:

- Reads the flags register (that is, conditionRegister == 'R'), and
- Is either taken or not taken (that is, TNnotBranch != '-').

Ignore all the lines in the trace that fail to meet both the above criteria.

You'll be predicting the taken/not-taken status of each branch. You'll use the program counter from the trace to make the prediction and the taken/not-taken field to determine if the prediction was correct or not.

To help you debug your predictors, we've provided some annotated prediction results for the first couple hundred branches for various predictors (see below). Comparing your results to these annotated outputs should help ensure your predictor is working properly.

Before looking at dynamic branch prediction, we are going to look at simple "static" branch prediction policies of "always predict taken" and "always predict not taken". Write a program to read in the trace and calculate the mis-prediction rate (that is, the percentage of conditional branches that were mis-predicted) for these two simple schemes.

Which of these two policies is more accurate (has few mis-predictions)?

Based on common programming idioms, what might explain the above result?

The simplest dynamic branch direction predictor is an array of 2^{n} two-bit
saturating counters. Each counter includes one of four values: *strongly taken*
(T), *weakly taken* (t), *weakly not taken* (n), and *strongly not taken* (N).

**Prediction**. To make a prediction, the predictor selects a counter
from the table using using the lower-order *n* bits of the instruction's
address (its program counter value). The direction prediction is made
based on the value of the counter.

Note

The easiest way to calculate the index is to modulo (%) the address by the number of entries in the predictor table. However, as the table size is always a power of two, it is almost as easy to use shifts and bit-wise and/or operations to calculate the index.

In the past some students have converted the integer address to a string, extracted a sub-string, and then converted the string back into an integer. Don't do this; it is unnecessary, slow, and ugly.

**Training**. After *each* branch (correctly predicted or not), the hardware
increments or decrements the corresponding counter to bias the counter toward
the actual branch outcome (the outcome given in the trace file). As these are
two bit *saturating* counters, decrementing a minimum counter or incrementing a
maxed out counter should have no impact.

**Initialization**. Although initialization doesn't effect the results
in any significant way, your code should initialize the predictor to
"strongly not taken" so that your results match the example traces that
we've given you.

Analyze the impact of predictor size on prediction accuracy. Write a program
to simulate the bimodal predictor. Use your program to simulate varying sizes
of bimodal predictor. Generate data for bimodal predictors with 2^{2}, 2^{3},
2^{4}, 2^{5} ... 2^{20} counters. These sizes correspond to predictor index
size of 2 bits, 3 bits, 4 bits, 5 bits, ... 20 bits. Generate a line plot of
the data using MS Excel or some other graphing program. On the y-axis, plot
"percentage of branches mis-predicted" (a metric in which smaller is better).
On the x-axis plot the log of the predictor size (basically, the number of
index bits). By plotting the predictor size in terms of number of index bits,
the x-axis in essence becomes a log scale, which is what we want for this
graph.

Answer the following questions base on the data you collected:

Given a large enough predictor, what is the best mis-prediction rate obtainable by the bimodal predictor?

How large must the predictor be to reduce the number of mis-predictions by approximately half as compared to the better of "always taken" and "always not taken"? Give the predictor size both in terms of number of counters as well as bytes.

At what point does the performance of the predictor pretty much max out? That is, how large does the predictor need to be before it basically captures almost all of the benefit of a much larger predictor.

From the previous data, you can see that neither bimodal or gshare is always
best. To remedy this situation, we can use a hybrid predictor that tries to
capture the best of both style of predicts. A tournament predictor consists of
*three* tables. The first and second tables are just normal bimodal and gshare
predictors. The third table is a "chooser" table that predicts whether the
bimodal or gshare predictor will be more accurate.

The basic idea is that some branches do better with a bimodal predictor and
some do better with a gshare predictor. So, the chooser is a table of two-bit
saturating counters indexed by the low-order bits of the PC that determines
*which* of the other two table's prediction to return. For the each entry in
the chooser, the two-bit counter encodes: *strongly prefer bimodal* (B),
*weakly prefer bimodal* (b), *weakly prefer gshare* (g), and *strongly prefer
gshare* (G).

**Prediction**. Access the chooser table using the low-order bits of the
branch's program counter address. In parallel, the bimodal and gshare tables
are accessed just as normal predictors, and each generates an independent
prediction. Based on the result of the lookup in the chooser table, the final
prediction is either the prediction from the bimodal predictor (if the choose
counter indicates a preference for bimodal) or the prediction from the gshare
predictor (otherwise).

**Training**. Both the gshare and bimodal predictors are trained on *every*
branch using their normal training algorithm. The choose predictor is trained
toward which of the two predictors was more accurate on that specific
prediction:

- Case #1: The two predictors make the
*same*prediction. In this case, either both predictors were correct or both predictors were wrong. In both cases, the chooser table isn't updated (as we didn't really learn anything). - Case #2: The two predictors made
*different*predictions (thus, one of the predictors was correct and the other incorrect). In this case, the chooser counter is trained (incremented or decremented by one) toward preferring that of the predictor that was correct. For example, if the gshare predictor was correct (and thus the bimodal predictor was incorrect), the chooser counter is adjusted by one toward preferring gshare.

As stated above, the bimodal and gshare tables are trained on *every* branch,
totally independent of the chooser table.

**Initialization**. The chooser table is initialized to *strongly prefer
bimodal*. The gshare and bimodal tables are initialized as normal.

Add support to your simulator for the tournament predictor by adding support for the chooser table. If your code was written in a modular way, you should be able to fully re-use your gshare and bimodal code (and thus avoid re-writing or replicating code).

Let's compare the tournament predictor's accuracy versus the data from its two
constituent predictors (using the data from the previous question). For now,
let's compare a 2^{n}-counter bimodal (or gshare) versus a tournament predictor
with *three* 2^{n}-counter tables. (We'll make the comparison more fair in
terms of a "same number of bits" comparison in a moment.) As above for the
gshare predictor, the gshare component of the tournament predictor should use a
history length equal to the number of index bits (log of the table size). The
graph will have three lines total.

How does the tournament predictor's accuracy compare to bimodal and gshare? Is the tournament predictor successful in meeting its goal?

Does the tournament predictor improve the overall peak (best-case) accuracy? If so, why? If not, what are the benefits of the tournament predictor?

In the previous question, the tournament predictor was given the unfair
advantage of having three times the storage. In this question, run another set
of experimental data in which all the predictors at the same location on the
x-axis have the same storage capacity. To do this, compare a 2^{n}-counter
predictor to a tournament predictor with the following three table sizes:

- Chooser table: 2
^{n-2}counters- Bimodal table: 2
^{n-2}counters- Gshare table: 2
^{n-1}counters

As 2^{n-2} + 2^{n-2} + 2^{n-1} is equal to 2^{n}, this becomes a fair "same
size" comparison.

Add a line to the graph from the previous question with this new "fair tournament" data. The graph now has four lines: bimodal, gshare, tournament, tournament-fair.

Compare the old and new tournament predictor data. What was the impact of moving to the smaller (fairer) table sizes?

Once adjusted to be a fair-size comparison, does the tournament predictor succeed in its goal of being the best of bimodal and gshare?

Given a fixed transistor budget for a branch predictor (say, 4KBs), what predictor approach would you use?

Note

The idea of gshare and tournament (or "combining") branch predictors was proposed by Scott McFarling in his seminal paper published in 1993 entitled: Combining Branch Predictors. I encourage you to look at McFarling's paper. After having completed this assignment, I think you'll find the graphs and other results in this paper familiar.

We're provide you with three annotated results (one for each predictor type) from running the first few hundred branches from the trace.

bimodal3.output: bimodal with 2

^{3}counters:nNNNTNNN | b7fa3ae4 T | T correct 3

The columns are: table counter state, PC from input trace, branch outcome from input trace, prediction made, prediction result (correct/incorrect), running total of mis-predictions thus far.

gshare4-3.output: gshare with 2

^{4}counters and history length of three:NNnNNntNnNNNNNNN NTN | b7fa3ae4 T | T correct 5

The columns are: table counter state, history register, PC from input trace, branch outcome from input trace, prediction made, prediction result (correct/incorrect), running total of mis-predictions thus far.

tournament3-bimodal3-gshare4-4.output: tournament predictor with a chooser table with 2

^{3}counters, a bimodal with 2^{3}counters, and a gshare with 2^{4}counters with a history length of four:BBBBBBBB nNNNTNNN NNNNNnNNnNNNNNtN TNTN | b7fa3ae4 T | T correct 3

The columns are: chooser predictor table, bimodal predictor, gshare predictor table, gshare history register, PC from input trace, branch outcome from input trace, prediction made, prediction result (correct/incorrect), running total of mis-predictions thus far.

**Short answers**. Turn in your answers to the above questions. I strongly urge you to type up your answers.**Graphs**. Turn in printouts of the following three graphs. One per sheet of paper (to make them big enough to read). Label axes and give the lines descriptive names in a legend.**Graph A**[Questions #2, #3, and #5]: Include just one graph with all three lines: bimodal, gshare-8, and gshare-*n*.**Graph B**[Question #4]: Include the graph describe in question four.**Graph C**[Questions #6 and #7] This graph has four lines: bimodal, gshare-*n*, tournament, and tournament-fair.

**Source Code**. Print out your final source code that can simulate the various predictors.

**Runtime**. Running through the trace should take just a few seconds if implemented efficiently. Even if you run through the entire trace to generate each data point, it shouldn't take more than a few minutes to generate all the data you need for this assignment. If your code is taking much longer, you're probably not doing something right (ask me or the TAs about it). The most common culprit for greatly increased runtime in the past have been: (1) using string objects rather than using bit manipulation operations (shift, bit-wise and, bit-wise or, etc.) for extracting the index and tag bits or (2) allocating heap objects as part of processing each line in the trace.**Computer resources**. If you wish to write the code by ssh'ing into a Linux box, you can log into minus.seas.upenn.edu. These machines are multi-core multi-Ghz Intel chips, so they are quite fast. However, if your jobs run for too long (say 10 or 20 minutes) the system will kill off the job (to make sure that users don't use these machines for very long running jobs). Of course, you're also welcome to write and run the code on your personal computers or the lab machines.**Code reuse**. In the end, the total amount of code needed to perform these simulations isn't actually that much. Don't go crazy in making the code super modular, but conversely there is significant potential for code reuse that shouldn't be ignored.**Automation of data collection**. Put a little bit of though into how you're going to collect the various data for each question. If you're running your program multiple times by hand and then copying and pasting the individual results into Excel, you're doing something wrong (or at least super inefficiently). Your code should be able to perform a range of simulations in an automated fashion (either by building it into the main program of using scripts to call it with different parameters). You program should be able to split out data text files that can be imported into Excel (or whatever) to make graphs with minimal tedium.

- None, yet.