Publication: MASCOT: Predicting memory dependencies and
opportunities for speculative memory bypassing
Authors
Mose, Karl H. ; Kim, Sebastian S. ; Ros Bardisa, Alberto ; Jones, Timothy M. ; Mullins, Robert D.
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Publisher
IEEE Computer Society
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DOI
https://doi.org/10.1109/HPCA61900.2025.00016
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info:eu-repo/semantics/article
Description
© 2025 IEEE. This manuscript version is made available under the CC-BY 4.0 license http://creativecommons.org/licenses/by/4.0/
This document is the Accepted Manuscript version of a Published Work that appeared in final form in 2025 IEEE International. To access the final edited and published work see https://doi.org/10.1109/HPCA61900.2025.00016
Abstract
Memory-dependence prediction (MDP) increases
instruction-level parallelism (ILP) by allowing load instructions
to be issued even when addresses in the store queue are unknown.
The predictor determines whether a load will alias with a
prior store, delaying issue when a dependence is predicted.
Speculative memory bypassing (SMB) further enhances ILP by
short-circuiting a predicted dependence to forward the value
written by a store to a load that is predicted to depend on
it, without their addresses necessarily being known. This breaks
data dependencies on the load and store addresses, allowing loads
to obtain their values much earlier than they normally would.
To obtain benefits, dependencies must be predicted with high
accuracy. Furthermore, the benefits are skewed, with false negatives being more costly for performance than false positives for
MDP, since the former requires squashing when the misprediction
is identified, whereas the latter only delays the issue of independent loads. For SMB, on the other hand, false positives are very
costly, as they require squashing, whereas false negatives have
little impact in the presence of an accurate memory dependence
predictor. Due to these differing requirements, the designs of
predictors for these mechanisms have diverged.
In this paper, we propose MASCOT, a novel predictor capable
of performing both MDP and SMB. MASCOT is inspired by
the TAGE predictor, widely used in branch prediction. Although
TAGE has proven effective as a universal predictor structure, we
demonstrate how prior TAGE-based MDP or SMB predictors
suffer from inaccuracy due to not learning patterns of nondependence. By learning the context for dependencies as well
as non-dependencies, MASCOT achieves sufficiently low false
negatives and false positives to perform MDP and SMB, while
at the same time uses less space than existing designs that only
perform MDP or SMB.
Our simulation results show that for SPEC CPU 2017, MASCOT used for MDP alone yields an IPC gain of 0.4 % over the
previous state-of-the-art predictor, on average, at the same size.
When used for both MDP and SMB, it yields an increase in
IPC of 1.9 % on average, with peak gains of 26 %. A compacted
version of MASCOT, MASCOT-OPT, achieves similar numbers
within 0.1 % while using just 10.1 KiB of space.
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