9,373 research outputs found
Finite State Machine Synthesis for Evolutionary Hardware
This article considers application of genetic algorithms for finite machine
synthesis. The resulting genetic finite state machines synthesis algorithm
allows for creation of machines with less number of states and within shorter
time. This makes it possible to use hardware-oriented genetic finite machines
synthesis algorithm in autonomous systems on reconfigurable platforms
A Non-Blocking Priority Queue for the Pending Event Set
The large diffusion of shared-memory multi-core machines has impacted the way Parallel Discrete Event Simulation (PDES) engines are built. While they were originally conceived as data-partitioned platforms, where each thread is in charge of managing a subset of simulation objects, nowadays the trend is to shift towards share-everything settings. In this scenario, any thread can (in principle) take care of CPU-dispatching pending events bound to whichever simulation object, which helps to fully share the load across the available CPU-cores. Hence, a fundamental aspect to be tackled is to provide an efficient globally-shared pending events’ set from which multiple worker threads can concurrently extract events to be processed, and into which they can concurrently insert new produced events to be processed in the future. To cope with this aspect, we present the design and implementation of a concurrent non-blocking pending events’ set data structure, which can be seen as a variant of a classical calendar queue. Early experimental data collected with a synthetic stress test are reported, showing excellent scalability of our proposal on a machine equipped with 32 CPU-cores
Hardware-based Security for Virtual Trusted Platform Modules
Virtual Trusted Platform modules (TPMs) were proposed as a software-based
alternative to the hardware-based TPMs to allow the use of their cryptographic
functionalities in scenarios where multiple TPMs are required in a single
platform, such as in virtualized environments. However, virtualizing TPMs,
especially virutalizing the Platform Configuration Registers (PCRs), strikes
against one of the core principles of Trusted Computing, namely the need for a
hardware-based root of trust. In this paper we show how strength of
hardware-based security can be gained in virtual PCRs by binding them to their
corresponding hardware PCRs. We propose two approaches for such a binding. For
this purpose, the first variant uses binary hash trees, whereas the other
variant uses incremental hashing. In addition, we present an FPGA-based
implementation of both variants and evaluate their performance
A Conflict-Resilient Lock-Free Calendar Queue for Scalable Share-Everything PDES Platforms
Emerging share-everything Parallel Discrete Event Simulation (PDES) platforms rely on worker threads fully sharing the workload of events to be processed. These platforms require efficient event pool data structures enabling high concurrency of extraction/insertion operations. Non-blocking event pool algorithms are raising as promising solutions for this problem. However, the classical non-blocking paradigm leads concurrent conflicting operations, acting on a same portion of the event pool data structure, to abort and then retry. In this article we present a conflict-resilient non-blocking calendar queue that enables conflicting dequeue operations, concurrently attempting to extract the minimum element, to survive, thus improving the level of scalability of accesses to the hot portion of the data structure---namely the bucket to which the current locality of the events to be processed is bound. We have integrated our solution within an open source share-everything PDES platform and report the results of an experimental analysis of the proposed concurrent data structure compared to some literature solutions
GPU accelerated Nature Inspired Methods for Modelling Large Scale Bi-Directional Pedestrian Movement
Pedestrian movement, although ubiquitous and well-studied, is still not that
well understood due to the complicating nature of the embedded social dynamics.
Interest among researchers in simulating pedestrian movement and interactions
has grown significantly in part due to increased computational and
visualization capabilities afforded by high power computing. Different
approaches have been adopted to simulate pedestrian movement under various
circumstances and interactions. In the present work, bi-directional crowd
movement is simulated where an equal numbers of individuals try to reach the
opposite sides of an environment. Two movement methods are considered. First a
Least Effort Model (LEM) is investigated where agents try to take an optimal
path with as minimal changes from their intended path as possible. Following
this, a modified form of Ant Colony Optimization (ACO) is proposed, where
individuals are guided by a goal of reaching the other side in a least effort
mode as well as a pheromone trail left by predecessors. The basic idea is to
increase agent interaction, thereby more closely reflecting a real world
scenario. The methodology utilizes Graphics Processing Units (GPUs) for general
purpose computing using the CUDA platform. Because of the inherent parallel
properties associated with pedestrian movement such as proximate interactions
of individuals on a 2D grid, GPUs are well suited. The main feature of the
implementation undertaken here is that the parallelism is data driven. The data
driven implementation leads to a speedup up to 18x compared to its sequential
counterpart running on a single threaded CPU. The numbers of pedestrians
considered in the model ranged from 2K to 100K representing numbers typical of
mass gathering events. A detailed discussion addresses implementation
challenges faced and averted
Self-synchronized duty-cycling for sensor networks with energy harvesting capabilities: Implementation in Wiselib
In this work we present a protocol for a self- synchronized duty-cycling mechanism in wireless sensor net- works with energy harvesting capabilities. The protocol is im- plemented in Wiselib, a library of generic algorithms for sensor networks. Simulations are conducted with the sensor network simulator Shawn. They are based on the specifications of real hardware known as iSense sensor nodes. The experimental results show that the proposed mechanism is able to adapt to changing energy availabilities. Moreover, it is shown that the system is very robust against packet loss.Postprint (published version
Recommended from our members
Combinatorial optimization and metaheuristics
Today, combinatorial optimization is one of the youngest and most active areas of discrete mathematics. It is a branch of optimization in applied mathematics and computer science, related to operational research, algorithm theory and computational complexity theory. It sits at the intersection of several fields, including artificial intelligence, mathematics and software engineering. Its increasing interest arises for the fact that a large number of scientific and industrial problems can be formulated as abstract combinatorial optimization problems, through graphs and/or (integer) linear programs. Some of these problems have polynomial-time (“efficient”) algorithms, while most of them are NP-hard, i.e. it is not proved that they can be solved in polynomial-time. Mainly, it means that it is not possible to guarantee that an exact solution to the problem can be found and one has to settle for an approximate solution with known performance guarantees. Indeed, the goal of approximate methods is to find “quickly” (reasonable run-times), with “high” probability, provable “good” solutions (low error from the real optimal solution). In the last 20 years, a new kind of algorithm commonly called metaheuristics have emerged in this class, which basically try to combine heuristics in high level frameworks aimed at efficiently and effectively exploring the search space. This report briefly outlines the components, concepts, advantages and disadvantages of different metaheuristic approaches from a conceptual point of view, in order to analyze their similarities and differences. The two very significant forces of intensification and diversification, that mainly determine the behavior of a metaheuristic, will be pointed out. The report concludes by exploring the importance of hybridization and integration methods
- …