1,256 research outputs found
On Metrics for Location-Aware Games
Metrics are important and well-known tools to measure users’ behavior in games, and gameplay in general. Particularities of location-aware games—a class of games where the player’s location plays a central role-demand specific support in metrics to adequately address the spatio-temporal features such games exhibit. In this article, we analyse and discuss how existing game analytics platforms address the spatio-temporal features of location-aware games. Our analysis reveals that little support is available. Next, based on the analysis, we propose a classification of spatial metrics, embedded in existing literature, and discuss three types of spatial metrics-point-, trajectory- and area-based metrics-, and elaborate examples and difficulties. Finally, we discuss how spatial metrics may be deployed to improve gameplay in location-aware games
EmBench: Quantifying Performance Variations of Deep Neural Networks across Modern Commodity Devices
In recent years, advances in deep learning have resulted in unprecedented
leaps in diverse tasks spanning from speech and object recognition to context
awareness and health monitoring. As a result, an increasing number of
AI-enabled applications are being developed targeting ubiquitous and mobile
devices. While deep neural networks (DNNs) are getting bigger and more complex,
they also impose a heavy computational and energy burden on the host devices,
which has led to the integration of various specialized processors in commodity
devices. Given the broad range of competing DNN architectures and the
heterogeneity of the target hardware, there is an emerging need to understand
the compatibility between DNN-platform pairs and the expected performance
benefits on each platform. This work attempts to demystify this landscape by
systematically evaluating a collection of state-of-the-art DNNs on a wide
variety of commodity devices. In this respect, we identify potential
bottlenecks in each architecture and provide important guidelines that can
assist the community in the co-design of more efficient DNNs and accelerators.Comment: Accepted at MobiSys 2019: 3rd International Workshop on Embedded and
Mobile Deep Learning (EMDL), 201
ACTiCLOUD: Enabling the Next Generation of Cloud Applications
Despite their proliferation as a dominant computing paradigm, cloud computing systems lack effective mechanisms to manage their vast amounts of resources efficiently. Resources are stranded and fragmented, ultimately limiting cloud systems' applicability to large classes of critical applications that pose non-moderate resource demands. Eliminating current technological barriers of actual fluidity and scalability of cloud resources is essential to strengthen cloud computing's role as a critical cornerstone for the digital economy. ACTiCLOUD proposes a novel cloud architecture that breaks the existing scale-up and share-nothing barriers and enables the holistic management of physical resources both at the local cloud site and at distributed levels. Specifically, it makes advancements in the cloud resource management stacks by extending state-of-the-art hypervisor technology beyond the physical server boundary and localized cloud management system to provide a holistic resource management within a rack, within a site, and across distributed cloud sites. On top of this, ACTiCLOUD will adapt and optimize system libraries and runtimes (e.g., JVM) as well as ACTiCLOUD-native applications, which are extremely demanding, and critical classes of applications that currently face severe difficulties in matching their resource requirements to state-of-the-art cloud offerings
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