282,553 research outputs found
Learning Parallel Computations with ParaLab
In this paper, we present the ParaLab teachware system, which can be used for learning the parallel computation methods. ParaLab provides the tools for simulating the multiprocessor computational systems with various network topologies, for carrying out the computational experiments in the simulation mode, and for evaluating the efficiency of the parallel computation methods. The visual presentation of the parallel computations taking place in the computational experiments is the key feature of the system. ParaLab can be used for the laboratory training within various teaching courses in the field of parallel, distributed, and supercomputer computations
Networked Computing in Wireless Sensor Networks for Structural Health Monitoring
This paper studies the problem of distributed computation over a network of
wireless sensors. While this problem applies to many emerging applications, to
keep our discussion concrete we will focus on sensor networks used for
structural health monitoring. Within this context, the heaviest computation is
to determine the singular value decomposition (SVD) to extract mode shapes
(eigenvectors) of a structure. Compared to collecting raw vibration data and
performing SVD at a central location, computing SVD within the network can
result in significantly lower energy consumption and delay. Using recent
results on decomposing SVD, a well-known centralized operation, into
components, we seek to determine a near-optimal communication structure that
enables the distribution of this computation and the reassembly of the final
results, with the objective of minimizing energy consumption subject to a
computational delay constraint. We show that this reduces to a generalized
clustering problem; a cluster forms a unit on which a component of the overall
computation is performed. We establish that this problem is NP-hard. By
relaxing the delay constraint, we derive a lower bound to this problem. We then
propose an integer linear program (ILP) to solve the constrained problem
exactly as well as an approximate algorithm with a proven approximation ratio.
We further present a distributed version of the approximate algorithm. We
present both simulation and experimentation results to demonstrate the
effectiveness of these algorithms
Quantum memory for images - a quantum hologram
Matter-light quantum interface and quantum memory for light are important
ingredients of quantum information protocols, such as quantum networks,
distributed quantum computation, etc. In this Letter we present a spatially
multimode scheme for quantum memory for light, which we call a quantum
hologram. Our approach uses a multi-atom ensemble which has been shown to be
efficient for a single spatial mode quantum memory. Due to the multi-atom
nature of the ensemble it is capable of storing many spatial modes, a feature
critical for the present proposal. A quantum hologram has a higher storage
capacity compared to a classical hologram, and is capable of storing quantum
features of an image, such as multimode superposition and entangled quantum
states, something that a standard hologram is unable to achieve. Due to optical
parallelism, the information capacity of the quantum hologram will obviously
exceed that of a single-mode scheme.Comment: 5 pages, 3 figure
High-Performance Multi-Mode Ptychography Reconstruction on Distributed GPUs
Ptychography is an emerging imaging technique that is able to provide
wavelength-limited spatial resolution from specimen with extended lateral
dimensions. As a scanning microscopy method, a typical two-dimensional image
requires a number of data frames. As a diffraction-based imaging technique, the
real-space image has to be recovered through iterative reconstruction
algorithms. Due to these two inherent aspects, a ptychographic reconstruction
is generally a computation-intensive and time-consuming process, which limits
the throughput of this method. We report an accelerated version of the
multi-mode difference map algorithm for ptychography reconstruction using
multiple distributed GPUs. This approach leverages available scientific
computing packages in Python, including mpi4py and PyCUDA, with the core
computation functions implemented in CUDA C. We find that interestingly even
with MPI collective communications, the weak scaling in the number of GPU nodes
can still remain nearly constant. Most importantly, for realistic diffraction
measurements, we observe a speedup ranging from a factor of to
depending on the data size, which reduces the reconstruction time remarkably
from hours to typically about 1 minute and is thus critical for real-time data
processing and visualization.Comment: work presented in NYSDS 201
Workflow-based Fast Data-driven Predictive Control with Disturbance Observer in Cloud-edge Collaborative Architecture
Data-driven predictive control (DPC) has been studied and used in various
scenarios, since it could generate the predicted control sequence only relying
on the historical input and output data. Recently, based on cloud computing,
data-driven predictive cloud control system (DPCCS) has been proposed with the
advantage of sufficient computational resources. However, the existing
computation mode of DPCCS is centralized. This computation mode could not
utilize fully the computing power of cloud computing, of which the structure is
distributed. Thus, the computation delay could not been reduced and still
affects the control quality. In this paper, a novel cloud-edge collaborative
containerised workflow-based DPC system with disturbance observer (DOB) is
proposed, to improve the computation efficiency and guarantee the control
accuracy. First, a construction method for the DPC workflow is designed, to
match the distributed processing environment of cloud computing. But the
non-computation overheads of the workflow tasks are relatively high. Therefore,
a cloud-edge collaborative control scheme with DOB is designed. The low-weight
data could be truncated to reduce the non-computation overheads. Meanwhile, we
design an edge DOB to estimate and compensate the uncertainty in cloud workflow
processing, and obtain the composite control variable. The UUB stability of the
DOB is also proved. Third, to execute the workflow-based DPC controller and
evaluate the proposed cloud-edge collaborative control scheme with DOB in the
real cloud environment, we design and implement a practical workflow-based
cloud control experimental system based on container technology. Finally, a
series of evaluations show that, the computation times are decreased by 45.19%
and 74.35% for two real-time control examples, respectively, and by at most
85.10% for a high-dimension control example.Comment: 58 pages and 23 figure
Polychronous mode automata
International audienceAmong related synchronous programming principles, the model of computation of the Polychrony workbench stands out by its capability to give high-level description of systems where each component owns a local activation clock (such as, typically,distributed real-time systems or systems on a chip). In order to bring the modeling capability of Polychrony to the context of a model-driven engineering toolset for embedded system design, we define a diagramic notation composed of mode automata and data-flow equations on top of the multi-clocked synchronous model of computation supported by the Polychrony workbench. We demonstrate the agility of this paradigm by considering the example of an integrated modular avionics application. Our presentation features the formalization and use of model transformation techniques of the GME environment to embed the extension of Polychrony's meta-model with mode automata
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