2 research outputs found
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
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Covert- and Side-Channel Attacks on Integrated and Distributed GPU Systems
Graphics Processing Units (GPUs) were introduced as peripheral devices for accelerating graphics and multi-media workloads. The inherent parallel computational model of graphics rendering makes GPUs suited for other workloads that operate on massive data and that are throughput oriented. To enable such general purpose applications to leverage GPUs, Nvidia introduced Compute Unified Device Architecture (CUDA) that allowed general purpose computing on GPUs. GPUs are currently ubiquitous in all computing platforms, from portable devices to high-end servers on the cloud. Customarily, GPUs are available in a discrete form where the GPUs are connected to rest of the system as a peripheral device with its own separate memory. This dissertation explores the security of emerging classes of GPUs to a type of microarchitectural attacks -- those targeting the architecture of the computing devices-- called covert- and side-channel attacks. The last decade has seen a rise in these types of attacks, primarily targeting CPU microarchitectural structures. Specifically, in these attacks an attacker uses malicious software that exploits resource sharing on the underlying architecture to either communicate secret data through covert channel or to extract information from the victim application indirectly by observing measurable contention. While the majority of these attacks have targeted conventional CPU resources, some recent work has shown that GPUs are also vulnerable to this type of attack.This dissertation explores the feasibility of these attacks, and demonstrates several end to end attacks in two emerging GPU domains: (1) Integrated GPUs: GPUs are also increasingly offered as integrated processors on the same chip as CPUs, enabling lower form factors and cost, while providing support for multi-media workloads which are important for consumer machines. Chip manufacturers like Intel have GPU integrated in the same die as the CPU. GPUs are currently available in distributed form as well where multiple GPUs are connected by proprietary connectors. We show that attacks from the GPU on the CPU and vice versa are possible in these environments. To enable these attacks, we have to solve a number of unique challenges many of which originate due to the heterogeneous view of the shared resources between the CPU and integrated GPU. This is the first known attack of this type that crosses heterogeneous components, which has important implications to future heterogeneous computing designs; and (2) Multi-GPU high performance servers: on the other end, there is an emerging class of multi-GPU systems targeted at high-performance applications in general, and machine learning workloads in particular. We demonstrate a number of covert and side-channel attacks on this type of environment, exploiting remote sharing of GPU caches.
Our cache based covert channel obtained a bandwidth of 120 KB/s and 3.2 MB/s in the integrated and distributed GPU settings. We have also demonstrated side channel attacks in both the computing environments. Our work substantially expands our understanding of the threat models facing these important and emerging systems, and helps define how future systems should be built to mitigate these attacks