Distributed shared memory architectures and global performance state estimation
Citation:
Michael Manzke, 'Distributed shared memory architectures and global performance state estimation', [thesis], Trinity College (Dublin, Ireland). School of Computer Science & Statistics, 2006, pp 198Download Item:
Abstract:
Invasive and non-invasive methods may be applied to measure and analyse the performance
of hardware Distributed Shared Memory (DSM) systems. This thesis presents novel
solutions for both methods and discusses the architectural organisation of loosely and tightly
coupled systems. The work begins with a discussion of the design and implementation of
a non-invasive deep-trace instrument for high-speed interconnects and also deals with the
analysis of the trace-data. Analysis results are used to tune interconnect simulations.
This thesis then presents an innovative invasive approach to estimate and predict the
system-wide utilisation of computational resources in real-time. An algorithm that implements
a discrete minimum mean-square error filter is applied to fuse concurrent and sequential
observations of system event counts into a state-vector. Contemporary computer
components and subsystems make these event counts available through hardware Performance
Monitoring Counter (PMC) registers. The registers may be accessed by the system’s
software quasi-concurrently but the number of registers in individual components is usually
smaller than the number of events that can be monitored. This approach overcomes the
problem by modelling individual PMC readings as vector random processes and recursively
processes them one PMC set at a time into a common state-vector, thereby making larger
PMC sets observable than would otherwise be possible.
Finally this work looks at loosely and tightly coupled hardware DSM systems as targets
for the estimation algorithm. Particular attention is paid to the conceptual design of a
tightly coupled hybrid reconfigurable DSM graphics cluster.
Author: Manzke, Michael
Advisor:
Coghlan, BrianQualification name:
Doctor of Philosophy (Ph.D.)Publisher:
Trinity College (Dublin, Ireland). School of Computer Science & StatisticsNote:
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