4,265 research outputs found
A Taxonomy of Workflow Management Systems for Grid Computing
With the advent of Grid and application technologies, scientists and
engineers are building more and more complex applications to manage and process
large data sets, and execute scientific experiments on distributed resources.
Such application scenarios require means for composing and executing complex
workflows. Therefore, many efforts have been made towards the development of
workflow management systems for Grid computing. In this paper, we propose a
taxonomy that characterizes and classifies various approaches for building and
executing workflows on Grids. We also survey several representative Grid
workflow systems developed by various projects world-wide to demonstrate the
comprehensiveness of the taxonomy. The taxonomy not only highlights the design
and engineering similarities and differences of state-of-the-art in Grid
workflow systems, but also identifies the areas that need further research.Comment: 29 pages, 15 figure
Modelling and Verification of Multiple UAV Mission Using SMV
Model checking has been used to verify the correctness of digital circuits,
security protocols, communication protocols, as they can be modelled by means
of finite state transition model. However, modelling the behaviour of hybrid
systems like UAVs in a Kripke model is challenging. This work is aimed at
capturing the behaviour of an UAV performing cooperative search mission into a
Kripke model, so as to verify it against the temporal properties expressed in
Computation Tree Logic (CTL). SMV model checker is used for the purpose of
model checking
NNgTL: Neural Network Guided Optimal Temporal Logic Task Planning for Mobile Robots
In this work, we investigate task planning for mobile robots under linear
temporal logic (LTL) specifications. This problem is particularly challenging
when robots navigate in continuous workspaces due to the high computational
complexity involved. Sampling-based methods have emerged as a promising avenue
for addressing this challenge by incrementally constructing random trees,
thereby sidestepping the need to explicitly explore the entire state-space.
However, the performance of this sampling-based approach hinges crucially on
the chosen sampling strategy, and a well-informed heuristic can notably enhance
sample efficiency. In this work, we propose a novel neural-network guided
(NN-guided) sampling strategy tailored for LTL planning. Specifically, we
employ a multi-modal neural network capable of extracting features concurrently
from both the workspace and the B\"{u}chi automaton. This neural network
generates predictions that serve as guidance for random tree construction,
directing the sampling process toward more optimal directions. Through
numerical experiments, we compare our approach with existing methods and
demonstrate its superior efficiency, requiring less than 15% of the time of the
existing methods to find a feasible solution.Comment: submitte
GNNHLS: Evaluating Graph Neural Network Inference via High-Level Synthesis
With the ever-growing popularity of Graph Neural Networks (GNNs), efficient
GNN inference is gaining tremendous attention. Field-Programming Gate Arrays
(FPGAs) are a promising execution platform due to their fine-grained
parallelism, low-power consumption, reconfigurability, and concurrent
execution. Even better, High-Level Synthesis (HLS) tools bridge the gap between
the non-trivial FPGA development efforts and rapid emergence of new GNN models.
In this paper, we propose GNNHLS, an open-source framework to comprehensively
evaluate GNN inference acceleration on FPGAs via HLS, containing a software
stack for data generation and baseline deployment, and FPGA implementations of
6 well-tuned GNN HLS kernels. We evaluate GNNHLS on 4 graph datasets with
distinct topologies and scales. The results show that GNNHLS achieves up to
50.8x speedup and 423x energy reduction relative to the CPU baselines. Compared
with the GPU baselines, GNNHLS achieves up to 5.16x speedup and 74.5x energy
reduction
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