68,246 research outputs found
Fault-Tolerant Consensus in Unknown and Anonymous Networks
This paper investigates under which conditions information can be reliably
shared and consensus can be solved in unknown and anonymous message-passing
networks that suffer from crash-failures. We provide algorithms to emulate
registers and solve consensus under different synchrony assumptions. For this,
we introduce a novel pseudo leader-election approach which allows a
leader-based consensus implementation without breaking symmetry
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
Exploring Scientific Application Performance Using Large Scale Object Storage
One of the major performance and scalability bottlenecks in large scientific
applications is parallel reading and writing to supercomputer I/O systems. The
usage of parallel file systems and consistency requirements of POSIX, that all
the traditional HPC parallel I/O interfaces adhere to, pose limitations to the
scalability of scientific applications. Object storage is a widely used storage
technology in cloud computing and is more frequently proposed for HPC workload
to address and improve the current scalability and performance of I/O in
scientific applications. While object storage is a promising technology, it is
still unclear how scientific applications will use object storage and what the
main performance benefits will be. This work addresses these questions, by
emulating an object storage used by a traditional scientific application and
evaluating potential performance benefits. We show that scientific applications
can benefit from the usage of object storage on large scales.Comment: Preprint submitted to WOPSSS workshop at ISC 201
Quantifying Resource Use in Computations
It is currently not possible to quantify the resources needed to perform a
computation. As a consequence, it is not possible to reliably evaluate the
hardware resources needed for the application of algorithms or the running of
programs. This is apparent in both computer science, for instance, in
cryptanalysis, and in neuroscience, for instance, comparative neuro-anatomy. A
System versus Environment game formalism is proposed based on Computability
Logic that allows to define a computational work function that describes the
theoretical and physical resources needed to perform any purely algorithmic
computation. Within this formalism, the cost of a computation is defined as the
sum of information storage over the steps of the computation. The size of the
computational device, eg, the action table of a Universal Turing Machine, the
number of transistors in silicon, or the number and complexity of synapses in a
neural net, is explicitly included in the computational cost. The proposed cost
function leads in a natural way to known computational trade-offs and can be
used to estimate the computational capacity of real silicon hardware and neural
nets. The theory is applied to a historical case of 56 bit DES key recovery, as
an example of application to cryptanalysis. Furthermore, the relative
computational capacities of human brain neurons and the C. elegans nervous
system are estimated as an example of application to neural nets.Comment: 26 pages, no figure
Memory and information processing in neuromorphic systems
A striking difference between brain-inspired neuromorphic processors and
current von Neumann processors architectures is the way in which memory and
processing is organized. As Information and Communication Technologies continue
to address the need for increased computational power through the increase of
cores within a digital processor, neuromorphic engineers and scientists can
complement this need by building processor architectures where memory is
distributed with the processing. In this paper we present a survey of
brain-inspired processor architectures that support models of cortical networks
and deep neural networks. These architectures range from serial clocked
implementations of multi-neuron systems to massively parallel asynchronous ones
and from purely digital systems to mixed analog/digital systems which implement
more biological-like models of neurons and synapses together with a suite of
adaptation and learning mechanisms analogous to the ones found in biological
nervous systems. We describe the advantages of the different approaches being
pursued and present the challenges that need to be addressed for building
artificial neural processing systems that can display the richness of behaviors
seen in biological systems.Comment: Submitted to Proceedings of IEEE, review of recently proposed
neuromorphic computing platforms and system
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