2,538 research outputs found
Managing Large Enclaves in a Data Center
Live migration of an application or VM is a well-known technique for load
balancing, performance optimization, and resource management. To minimize the
total downtime during migration, two popular methods -- pre-copy or post-copy
-- are used in practice. These methods scale to large VMs and applications
since the downtime is independent of the memory footprint of an application.
However, in a secure, trusted execution environment (TEE) like Intel's scalable
SGX, the state-of-the-art still uses the decade-old stop-and-copy method, where
the total downtime is proportional to the application's memory footprint. This
is primarily due to the fact that TEEs like Intel SGX do not expose memory and
page table accesses to the OS, quite unlike unsecure applications. However,
with modern TEE solutions that efficiently support large applications, such as
Intel's Scalable SGX and AMD's Epyc, it is high time that TEE migration methods
also evolve to enable live migration of large TEE applications with minimal
downtime (stop-and-copy cannot be used any more). We present OptMig, an
end-to-end solution for live migrating large memory footprints in TEE-enabled
applications. Our approach does not require a developer to modify the
application; however, we need a short, separate compilation pass and
specialized software library support. Our optimizations reduce the total
downtime by 98% for a representative microbenchmark that uses 20GB of secure
memory and by 90 -- 96% for a suite of Intel SGX applications that have
multi-GB memory footprints
Rapid Tunneling and Percolation in the Landscape
Motivated by the possibility of a string landscape, we reexamine tunneling of
a scalar field across single/multiple barriers. Recent investigations have
suggested modifications to the usual picture of false vacuum decay that lead to
efficient and rapid tunneling in the landscape when certain conditions are met.
This can be due to stringy effects (e.g. tunneling via the DBI action), or by
effects arising due to the presence of multiple vacua (e.g. resonance
tunneling). In this paper we discuss both DBI tunneling and resonance
tunneling. We provide a QFT treatment of resonance tunneling using the
Schr\"odinger functional approach. We also show how DBI tunneling for
supercritical barriers can naturally lead to conditions suitable for resonance
tunneling. We argue using basic ideas from percolation theory that tunneling
can be rapid in a landscape where a typical vacuum has multiple decay channels
and discuss various cosmological implications. This rapidity vacuum decay can
happen even if there are no resonance/DBI tunneling enhancements, solely due to
the presence of a large number of decay channels. Finally, we consider various
ways of circumventing a recent no-go theorem for resonance tunneling in quantum
field theory.Comment: 47 pages, 16 figures. Acknowledgements adde
VarSim: A Fast Process Variation-aware Thermal Modeling Methodology Using Green's Functions
Despite temperature rise being a first-order design constraint, traditional
thermal estimation techniques have severe limitations in modeling critical
aspects affecting the temperature in modern-day chips. Existing thermal
modeling techniques often ignore the effects of parameter variation, which can
lead to significant errors. Such methods also ignore the dependence of
conductivity on temperature and its variation. Leakage power is also
incorporated inadequately by state-of-the-art techniques. Thermal modeling is a
process that has to be repeated at least thousands of times in the design
cycle, and hence speed is of utmost importance.
To overcome these limitations, we propose VarSim, an ultrafast thermal
simulator based on Green's functions. Green's functions have been shown to be
faster than the traditional finite difference and finite element-based
approaches but have rarely been employed in thermal modeling. Hence we propose
a new Green's function-based method to capture the effects of leakage power as
well as process variation analytically. We provide a closed-form solution for
the Green's function considering the effects of variation on the process,
temperature, and thermal conductivity. In addition, we propose a novel way of
dealing with the anisotropicity introduced by process variation by splitting
the Green's functions into shift-variant and shift-invariant components. Since
our solutions are analytical expressions, we were able to obtain speedups that
were several orders of magnitude over and above state-of-the-art proposals with
a mean absolute error limited to 4% for a wide range of test cases.
Furthermore, our method accurately captures the steady-state as well as the
transient variation in temperature.Comment: 15 page
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