Broadcast snooping and directory protocols are, by far, the most common
coherence protocols in research and commercial systems. These protocols
represent two extremes of cache coherence protocol design with seemingly
incompatible goals. Directory protocols produce scalable systems by reducing
network bandwidth requirements at the cost of increasing latency. Snooping based
systems allow low latency at the cost of increased bandwidth. Recently, a
promising class of coherence protocols called Token Coherence has been shown
to outperform directory and snooping protocols by attempting to combine the best
characteristics of both protocols. The concept of token counting allows the
protocol to safely multicast requests on an unordered network. This avoids
indirection like a snooping system but allows the system to scale by eliminating
the need for an ordered network. Additionally, Token Coherence promises to be
easier to implement, requiring nothing more than reliable message delivery from
the network, and provides a simple set of rules to guarantee correctness.
Token Coherence was developed to improve the performance of multiprocessors
running "commercial" applications including web and database servers. The fact
that token coherence was designed with a specific class of applications in mind
raises questions about its ability to perform under different circumstances.
Without a more thorough investigation of the performance of Token Coherence, it
is unclear whether its success on commercial applications is representative of
its performance on other workloads. The goal of this thesis is to evaluate the
performance of Token Coherence using a subset of the Splash2 benchmark suite.
Also, variations of Token Coherence described in the literature but whose
effects on performance were not published are examined.
This work shows that Token Coherence is not dependent on the peculiarities of
commercial workloads and can improve the performance of scientific applications.
In fact, Token Coherence performs well despite that assumptions under which it
was conceived are not necessarily true on all applications. In addition, some
optimizations made to Token Coherence specifically for commercial workloads do
not have a significant positive benefit for scientific workloads.
