6,916 research outputs found
Synapse: Synthetic Application Profiler and Emulator
We introduce Synapse motivated by the needs to estimate and emulate workload
execution characteristics on high-performance and distributed heterogeneous
resources. Synapse has a platform independent application profiler, and the
ability to emulate profiled workloads on a variety of heterogeneous resources.
Synapse is used as a proxy application (or "representative application") for
real workloads, with the added advantage that it can be tuned at arbitrary
levels of granularity in ways that are simply not possible using real
applications. Experiments show that automated profiling using Synapse
represents application characteristics with high fidelity. Emulation using
Synapse can reproduce the application behavior in the original runtime
environment, as well as reproducing properties when used in a different
run-time environments
Massively parallel approximate Gaussian process regression
We explore how the big-three computing paradigms -- symmetric multi-processor
(SMC), graphical processing units (GPUs), and cluster computing -- can together
be brought to bare on large-data Gaussian processes (GP) regression problems
via a careful implementation of a newly developed local approximation scheme.
Our methodological contribution focuses primarily on GPU computation, as this
requires the most care and also provides the largest performance boost.
However, in our empirical work we study the relative merits of all three
paradigms to determine how best to combine them. The paper concludes with two
case studies. One is a real data fluid-dynamics computer experiment which
benefits from the local nature of our approximation; the second is a synthetic
data example designed to find the largest design for which (accurate) GP
emulation can performed on a commensurate predictive set under an hour.Comment: 24 pages, 6 figures, 1 tabl
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Faithful reproduction of network experiments
The proliferation of cloud computing has compelled the research community to rethink fundamental aspects of network systems and architectures. However, the tools commonly used to evaluate new ideas have not kept abreast of the latest developments. Common simulation and emulation frameworks fail to provide scalability, fidelity, reproducibility and execute unmodified code, all at the same time.
We present SELENA, a Xen-based network emulation framework that offers fully reproducible experiments via its automation interface and supports the use of unmodified guest operating systems. This allows out-of-the-box compatibility with common applications and OS components, such as network stacks and filesystems. In order to faithfully emulate faster and larger networks, SELENA adopts the technique of time-dilation and transparently slows down the passage of time for guest operating systems. This technique effectively virtualizes the availability of host’s hardware resources and allows the replication of scenarios with increased I/O and computational demands. Users can directly control the tradeoff between fidelity and running-times via intuitive tuning knobs. We evaluate the ability of SELENA to faithfully replicate the behaviour of real systems and compare it against existing popular experimentation platforms. Our results suggest that SELENA can accurately model networks with aggregate link speeds of 44 Gbps or more, while improving by four times the execution time in comparison to ns3 and exhibits near-linear scaling properties.This is the author accepted manuscript. The final version is available from ACM via http://dx.doi.org/10.1145/2658260.265827
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