U. Glaeser

FIGURE 6.21 A schematic for a GAs global-history predictor. N global history bits are concatenated with M bits
from the branch address to form the PHT index.
refers to a predictor that explicitly allocates groups of branches to particular BHT entries, and is only feasible
with extensive profiling or compiler support and hence has received little study. Skadron, Martonosi, and
Clark [37] added a fourth type, M, to this naming scheme to describe predictors that track a combination
of global and local history. The second letter, A or S, indicates whether the PHT is adaptive, using a finite
state machine based on saturating counters, or fixed, using statically assigned directions (a profiling pass
might determine the best PHT value for each entry); almost all predictors proposed or under study,
however, are A—adaptive. The third letter, g, s, or p, indicates the PHT organization. The PHT might
be indexed purely by history (g); or indexed using some concatenated branch address bits, making it setassociative
(s); or the predictor might have a separate PHT for each branch (p, for per-branch). This last
choice eliminates aliasing among branches but is prohibitively large for all but small history sizes, and
is therefore mainly of theoretical interest. A pure global-history predictor like that in Fig. 6.20 is, therefore,
a GAg predictor and a pure local-history predictor like that in Fig. 6.19 is a PAg predictor. If either of
these concatenate some address bits into the index, like the global-history predictor in Fig. 6.21, they
become GAs or PAs predictors. Note that the GAs predictor has also sometimes been referred to as gselect
[40]. Finally, a predictor that uses both global and per-branch history, such as the bi-mode predictor,
would be an MAg or MAs predictor [37]. As for specifying the specific configuration of a predictor—how
many bits, how many entries, etc.—so many notations are involved that it is better to just be explicit.
An alternative anti-aliasing approach is to XOR the history string and address string together; this
approach, introduced by McFarling [40], is called gshare. This avoids the need to use a shorter history
string—both strings can be as long as the index. Recent data by Sechrest, Lee, and Mudge [41], however,
suggest that gshare confers little benefit over GAs.
Two-level prediction can seem like magic, especially global-history prediction. But it operates on the
same principle as compression; a predictable sequence is also compressible. Indeed, two-level prediction
is a simplified version of a Markov model, the same principle that underlies the prediction by partial
matching (PPM) compression scheme [42].
Hybrid Prediction
Because some branches do benefit from global history and others do not, McFarling [40] proposed hybrid
branch prediction. Several different organizations have been proposed [43–45], but the common idea is
to operate two different predictors in parallel, and for each branch select which predictor’s output to
actually use in making the prediction. The selector is itself a predictor and can be any of the structures
described above, but the selector tracks predictor successes rather than branch outcomes. For each branch,
the selector attempts to learn which predictor component is more effective. Figure 6.22 shows a highlevel
schematic of a hybrid predictor’s organization. Note that both predictor components and the selector
can all be accessed in parallel to minimize lookup time.
The Compaq Alpha 21264 [25] uses a hybrid predictor comprised of 12-bit GAg and 10-bit PAg
predictors. The PAg component has a 1 K-entry BHT and, because it uses only history bits in indexing
© 2002 by CRC Press LLC
GBHR
baddr
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