OpenSMT：一个同时多线程处理器模拟器的设计和实现

同时多线程（SMT）技术是目前微处理器体系结构的研究热点之一。为了支持对SMT技术和基于SMT核的单芯片多处理器（CMP）体系结构技术的深入研究，我们在广泛使用的超标体系结构模拟器Simple Sealar的基础上，通过对SMT结构的关键特性进行适当的抽象，开发了一个SMT体系结构模拟器OpenSMT。本文介绍了谊模拟器主要的设计思想和实现方法，包括多个线程上下文结构的表示、超标量流水线各个阶段的模拟，以及模拟器设计和实现时需要解决的几个关键问题等。初步的应用研究表明，与现有可免费获得的研究用SMT模拟器相比，该模拟器能够较好地平衡模拟性能、灵活性和精度三个基本设计目标，实现了执行驱动、易于扩展指令集结构、良好的用户接口、灵活的软件结构、适宜评估更广泛的SMT体系结构设计空间等设计要求。     

一种基于传输触发体系结构的可重构Hash函数处理器：TTAH

Hash函数是密码学中保证数据完整性的有效手段,性能需求使得某些应用必须采用硬件实现。本文通过分析常用Hash函数在算法上的相似性设计出了专用可重构单元,并将这些可重构单元耦合到传输触发体系结构中,得到一种可重构Hash函数处理器TTAH。常用Hash算法在TTAH上的映射结果表明：与细粒度可重构结构相比,其速度快,资源利用率高;与ASIC相比,可以在额外开销增加较小的前提下有效地支持多种常用Hash函数。     

Vega: A Computer Systems Approach to Grid Computing

In this paper, we contrast four approaches for Grid computing, and discuss a computer systems approach in detail. This approach views a Grid as a distributed computer system, and its main concerns are systems abstractions and constructs, such as the Grid equivalents of computer architecture, address space, process, device, file system, user/developers interface. Following this methodology, we identified several essential issues, developed a Vega Grid systems architecture, and proposed several systems techniques such as Grid routers, Grid address spaces, Grid process (grip), Grid community (agora), and a Grid Service Markup Language (GSML) software suite. We evaluated the computer systems approach and the associated techniques by discussing an OSGA-compliant Grid system software implementation and a travel agency example.     

TTA-EC:一种基于传输触发体系结构的ECC整体算法处理器

以传输触发体系结构(TTA)为基础,为支持大数运算扩展寄存器堆,增加模乘单元以加速模乘操作,提出一种ECC整体算法处理器TTA-EC.该处理器具有如下特点:(1)利用TTA工具链,可快速开发出基于TTA-EC的完整ECC公钥系统;(2)模乘单元将以基数为处理字长的高基数Montgomery算法与行共享流水结构相结合,具有良好的可扩展性;(3)流水单元实现矢量乘操作,并同时支持GF(p)和GF(2n)双有限域;(4)通过调整总线宽度和流水单元个数,可满足不同性能/面积约束.在0.18μm 1P6M CMOS工艺下,其高性能和紧缩面积版本的规模分别为117.4K和40.6K,可分别在0.87ms和7.83ms内完成一次GF(p)或GF(2n)上的192位EC标量乘运算,峰值功耗分别为242.1mW和28.5mW.     

Deriving parallel computations from functional specifications: A seismic example on a hypercube

Three algorithms developed for a seismic model illustrate that, when the target hardware has many processing elements, functional programs can exhibit better performance than programs developed with conventional techniques. This stands in contrast to the widely held belief that functional programs necessarily pay in poor performance for their advantages in conciseness and likelihood of correctness. Two of the algorithms evolved from an analysis of the seismic model with the goal of finding parts of the computation that could proceed in parallel. The first algorithm has a low communication to computation ratio. The second and third algorithms have higher ratios trading communication time for computation time. The third algorithm was derived from a presentation of the input/output relationship of the model expressed as a composition of mathematical functions. This algorithm exhibited substantially better performance than either of the others. The algorithm achieves its good performance by setting up a producer-consumer pipeline between simultaneously operating portions of the computation. This pipeline balances computation and interprocessor communication more effectively than the other two algorithms.     

Forward Light Cuts: A Scalable Approach to Real‐Time Global Illumination

We present Forward Light Cuts, a novel approach to real‐time global illumination using forward rendering techniques. We focus on unshadowed diffuse interactions for the first indirect light bounce in the context of large models such as the complex scenes usually encountered in CAD application scenarios. Our approach efficiently generates and uses a multiscale radiance cache by exploiting the geometry‐specific stages of the graphics pipeline, namely the tessellator unit and the geometry shader To do so, we assimilate virtual point lights to the scene's triangles and design a stochastic decimation process chained with a partitioning strategy that accounts for both close‐by strong light reflections, and distant regions from which numerous virtual point lights collectively contribute strongly to the end pixel. Our probabilistic solution is supported by a mathematical analysis and a number of experiments covering a wide range of application scenarios. As a result, our algorithm requires no precomputation of any kind, is compatible with dynamic view points, lighting condition, geometry and materials, and scales to tens of millions of polygons on current graphics hardware.     

The role of social interaction on users motivation to exercise: A persuasive web framework to enhance the self-management of a healthy lifestyle

The current research guidelines of the European community suggest the importance of the development of systems that help users manage their health themselves. The increasing amount of communication technologies and devices from which users can access information, and the possibility to interact through social media channels, play an important role in this scenario. Based on these considerations, in this paper we present an innovative persuasive web application, designed both to exploit social networking sites and to cooperate with a mobile application that already operates in the e-health and motivational domains. In particular, the innovative aspects introduced by the web application are the possibility to access also from a web browser some features previously available only through a mobile application and a more direct and user-friendly integration of social network sites. Indeed, thanks to an extensive interaction with the Facebook social network, users are allowed to share their experience with the application. This generates a strong social influence effect, which inspires and motivates other users to improve their exercising activity. Experimental results put in evidence that our web application, also thanks to social interactions, is favoring an enhancement of users’ motivation to a more active lifestyle. This is mainly due to its capability to have an impact on the other users thanks to the posts generated on the Facebook social network.     

A Novel Approach to Lossy Real-Time Image Compression: Hierarchical Data Reorganization on a Low-Cost Massively Parallel System

This paper discusses an innovative real-time oriented image compression system, based on a simple algorithm designed explicitly to be implemented on a low-cost SIMD computer architecture featuring a much lower power consumption than traditional DSPs or dedicated hardware. For this reason the considered approach is suitable to be integrated on portable systems, where power consumption is a critical design issue.The algorithm, based on a hierarchical decomposition of the input image, has been tested on a special purpose SIMD system, PAPRICA, exploiting its features such as its massive parallelism and its capability to operate on local data as well as to handle hierarchical data structures.According to the proposed approach, the quality of the decompressed image can be traded for a lower power consumption as well as a higher processing speed. A comparison between the discussed algorithm and the standard JPEG compression technique is also presented.     

From laser-scanned data to feature human model: a system based on fuzzy logic concept

This paper describes the development of a prototype system using fuzzy logic concept for constructing a feature human model, which is to be stored in a 3D digital human model database. In our approach, the feature human model is constructed by unorganized cloud points obtained from 3D laser scanners. Firstly, noisy points are removed, and the orientation of the human model is adjusted; secondly, a feature based mesh generation algorithm is applied on the cloud points to construct the mesh surface of a human model; lastly, semantic features of the human model are extracted from the mesh surface. Compared with earlier approach, our method strongly preserves the topology of a human model; more details can be constructed; and both the robustness and the efficiency of the algorithm are improved. At the end of the paper, in order to demonstrate the functionality of feature human models, potential applications are given.     

Towards a unified Media-User Typology (MUT): A meta-analysis and review of the research literature on media-user typologies

Considering the increasingly complex media landscape and diversity of use, it is important to establish a common ground for identifying and describing the variety of ways in which people use new media technologies. Characterising the nature of media-user behaviour and distinctive user types is challenging and the literature offers little guidance in this regard. Hence, the present research aims to classify diverse user behaviours into meaningful categories of user types, according to the frequency of use, variety of use and content preferences. To reach a common framework, a review of the relevant research was conducted. An overview and meta-analysis of the literature (22 studies) regarding user typology was established and analysed with reference to (1) method, (2) theory, (3) media platform, (4) context and year, and (5) user types. Based on this examination, a unified Media-User Typology (MUT) is suggested. This initial MUT goes beyond the current research literature, by unifying all the existing and various user type models. A common MUT model can help the Human–Computer Interaction community to better understand both the typical users and the diversification of media-usage patterns more qualitatively. Developers of media systems can match the users’ preferences more precisely based on an MUT, in addition to identifying the target groups in the developing process. Finally, an MUT will allow a more nuanced approach when investigating the association between media usage and social implications such as the digital divide.     

Planning to see: A hierarchical approach to planning visual actions on a robot using POMDPs

Flexible, general-purpose robots need to autonomously tailor their sensing and information processing to the task at hand. We pose this challenge as the task of planning under uncertainty. In our domain, the goal is to plan a sequence of visual operators to apply on regions of interest (ROIs) in images of a scene, so that a human and a robot can jointly manipulate and converse about objects on a tabletop. We pose visual processing management as an instance of probabilistic sequential decision making, and specifically as a Partially Observable Markov Decision Process (POMDP). The POMDP formulation uses models that quantitatively capture the unreliability of the operators and enable a robot to reason precisely about the trade-offs between plan reliability and plan execution time. Since planning in practical-sized POMDPs is intractable, we partially ameliorate this intractability for visual processing by defining a novel hierarchical POMDP based on the cognitive requirements of the corresponding planning task. We compare our hierarchical POMDP planning system (HiPPo) with a non-hierarchical POMDP formulation and the Continual Planning (CP) framework that handles uncertainty in a qualitative manner. We show empirically that HiPPo and CP outperform the naive application of all visual operators on all ROIs. The key result is that the POMDP methods produce more robust plans than CP or the naive visual processing. In summary, visual processing problems represent a challenging domain for planning techniques and our hierarchical POMDP-based approach for visual processing management opens up a promising new line of research.