GREEENDROID

                             GREEENDROID

                                 B.Obuliraj

                                

Content:

Ø Abstract

Ø Introduction

Ø Purpose of Greendriod

Ø Android vs Greendroid

Ø Latest news about Greendroid

Ø How to use Greendriod

Ø Features

Ø Advantages

Ø Disadvantages

Ø More about Greendroid

Ø Conclusion

ABSTRACT:

Mobile application processors are soon to replace desktop processors as the focus of innovation in microprocessor technology. Already, these processors have largely caught up to their more power hungry cousins, supporting out-oforder execution and multicore processing. In the near future, the exponentially worsening problem of dark silicon is going to be the primary force that dictates the evolution of these designs. In recent work, we have argued that the natural evolution of mobile application processors is to use this dark silicon to create hundreds of automatically generated energy-saving cores, called conservation cores, which can reduce energy consumption by an order of magnitude. This article describes GreenDroid, a research prototype that demonstrates the use of such cores to save energy broadly across the hotspots in the Android mobile phone software stack.

greendroid is a development library for the Android platform. It is intended to make UI developments easier and consistent through your applications.
greendroid is an Android library project developed by the French Cyril Mottier. It provides a lot of pre-made UI elements that follows the Android UI Design Patterns.

INTRODUCTION:

          I’m really glad to introduce you to my first attempt of a “true” development library for Android! As you may have noticed it is named “GreenDroid” and is intended to help people in designing Android applications. Keep in mind, it is still a work in progress! Let’s explain everything about GreenDroid before diving into it!......The GreenDroid mobile application

processor is a 45-nm multicore research prototype that targets the Android mobile-phone software stack and can execute

general-purpose mobile programs with 11 times less energy than today’s

most energy-efficient designs, at similar or better performance levels. It does this through theuse of a hundred or so automatically generated,

highly specialized, energy-reducing

cores, called conservation cores.

Our research attacks a key technological

problem for microprocessor architects, which we call the utilization wall.1 The utilization

wall says that, with each process generation, the percentage of transistors that a

chip design can switch at full frequency drops exponentially because of power constraints.

A direct consequence of this is

dark silicon—large swaths of a chip’s silicon area that must remain mostly passive to stay

within the chip’s power budget. Currently, only about 1 percent of a modest-sized

32-nm mobile chip can switch at full frequency within a 3-W power budget.

PURPOSE OF GREENDROID:

·                   Prevent loosing time at copying the same snippets of code over and over again.

• Try to make Android applications alike. The openness of the Android operating system makes it less coherent. Seing so many applications that use totally different ergonomics or designs drives me mad. Indeed, I truly think, Android applications need coherency in order to get more and more users around the world. GreenDroid is kinda of a try to bring coherency to your applications and therefore to the Android environment. Google recently tried to show a path to “great UX and UI” with the Twitter and Google I/O applications - A talk about Android UIs will be given at Google I/O 2010 and I’m looking forward to watch it; I would like to see if Google has understood Android developers need help creating simple and smart interfaces!
• Help developers to code highly functional applications. The Android framework may look like “over-engineered” sometimes. Actually, I believe this is a direct consequence of the fact you can do almost everything you want. Unfortunatly, this openness (again!) makes it harder to apprehend. Let’s say for instance, you are a beginner and wants to develop your own application. You’ll have to read a lot of documentation in order to be “up and ready”. GreenDroid makes development a lot easier
• without dicreasing the powerfulness of the amazing Android framework!
• Leverage the power of the Android framework. Developing on the Android platform may be pretty easy if you’re not taking care of the resources you’re using. Trying to optimize your application is quite hard sometimes and is a very demanding task. GreenDroid has been developed to be as efficient as possible by integrating basic optimizations.
• Use as much XML as possible. It’s not a mystery to anybody. Android UI development is based on amazing techniques. Layouts and views are defined in XML and automatically inflated by the system. Being an “easy-to-read-for-humans” format, XML is very used among Android developers. GreenDroid puts XML in the middle of the library and takes advantage of all amazing possibilities offered by Android XML file
• LATEST NEWS ABOUT GREENDROID:

    We are developing a prototype mobile application processor called GreenDroid that leverage "dark silicon" to dramatically reduce energy consumption in smartphones. GreenDroid will provide many specialized processors targeting key portions of Google's Android smartphone platform. GreenDroid will reduce energy consumption for these codes by integrating conservation cores (c-cores).
Our Hutchins 2010 work, GreenDroid: A Mobile Application Processor for a Future of Dark Silicon, flushes out this proposal. This was followed up with this April 2011 IEEE Micro paper. Our ASPLOS 2010 paper is one of the earliest peer-reviewed architecture papers to have a cogent description of the utilization wall that causes the dark silicon problem, and to propose specialization as an architectural solution.
Our group has constructed a fully-automated toolchain that generates c-cores from application source code. The resulting specialized circuits can deliver up to 18x increases in energy efficiency without sacrificing performance. C-cores also incorporate focused reconfigurability that allows them to adapt to small changes in the target application while still realizing efficiency gains.

How to use GreenDroid ?

In order to use GreenDroid you have to do the following steps:

1.     Download the GreenDroid library on your computer with a simple: git clone http://github.com/cyrilmottier/GreenDroid.git

2.     Apply GreenDroid to your project: Go to the scripts/ folder of the GreenDroid folder. Run the script named “greendroid.py” (make sure you can run it - chmod +x greendroid.py) to apply the library to your project : ./greendroid.py apply <project_directory>

3.     By default, the GreenDroid theme inherits from @android:style/Theme. If your project inherits from a different theme, you’ll have to modify the GreenDroid library on your own (and do that everytime you are updating GreenDroid as updating delete all of your changes). Open the res/values/gd_themes.xml and replace the parent theme @android:style/Theme with your own theme

You finally need to make your project use the GreenDroid base theme. In your Android Manifest, go to the application tag and add android:theme="@style/Theme.GreenDroid" as a new attribute (if this attribute already exist override it: if you processed the step 3 correctly, you will have a theme that inherits from this theme)Cyril Mottier. Is the developer of greendroid.

THE GREENDROID ARCHITECTURE:

A GreenDroid processor combines general-purpose processors with application-specific coprocessors

that are very energy efficient. These

conservation cores, or c-cores, execute most of an application’s code and will account for well over 90 percent of execution time. Green-

Droid is a heterogeneous tiled architecture. illustrates how it uses a grid-based organization to connect multiple tiles. Figure 2b show the floor plan for one of the tiles. It

contains an energy-efficient 32-bit 7-stage inorder pipeline that runs at 1.5 GHz in a 45 nm process technology. It includes a single-precision

floating point unit (FPU), multiplier, 16- kbyte I-cache, translation lookaside buffer (TLB), and 32-kbyte banked L1 data cache. The architecture also includes a mesh-basedon-chip network (OCN). The OCN carries memory traffic and supports fast synchronization primitives

ANDROID: GREENDROID’S

TARGET WORKLOAD:

Android is an excellent target for a c-coreenabled, GreenDroid-style architecture. Android comprises three main components: a

version of the Linux kernel, a collection of native libraries (written in C and C++), and the

Dalvik virtual machine (VM). Android’s libraries include functions that target frequently

executed, computationally intensive (or “hot”) portions of many applications (e.g., 2D compositing,

media decoding, garbage collection).

The remaining “cold” code runs on the Dalvik VM, and, as a result, the Dalvik VM is also “hot.” Therefore, a small number of c-cores that

target the Android libraries and Dalvik VM should be able to achieve very high coverage for

Android applications.

We have profiled a diverse set of Android applications including the web browser, Mail,

Maps, Video Player, Pandora, and many other applications. We found that this workload spends

95 percent of its time executing just 43,000 static instructions. Our experience building c-cores

suggests that just 7 mm2 of conservation cores in

a 45 nm process could replace these key instructions.

Even more encouraging, approximately 72

percent of this 95 percent was library or Dalvik code that multiple applications used.

Android’s usage model also reduces the need for the patching support c-cores provide. Since

cell phones have a very short replacement cycle (typically two to three years), it is less important

that a c-core be able to adapt to new software versions as they emerge. Furthermore, handset

manufacturers can be slow to push out new versions. In contrast, desktop machines have an

expected lifetime of between five and ten years, and the updates are more frequent

ADVANTAGE:

Ø Less power consumption

Ø More faster than android

Ø More efficient

Ø Easy to use

Ø Easy to understand than android

Ø Life time is more

Ø More application available in greendroid than android

DISADVANTAGE:

Ø Power consumption is high

Ø More critical to use

Ø Less efficient to use

FEATURES:

Accelerators

Specialized accelerators have been getting increasingly more attention

lately because they let designers trade customized silicon area for

performance and, often, energy efficiency. At the heart of most accelerators’ performance is the fact that designers have figured out how to attain parallel execution of the underlying algorithm, realized efficiently in hardware.

One extreme example of such an accelerator is Anton, which attains

100 times or more improvement in molecular-dynamics simulation versus general-purpose supercomputer machines.1 At a smaller level, we see accelerators throughout the computing space, whether for baseband processing,

3D graphics, or video decoding or encoding. The challenge in creating accelerators is in reorganizing the algorithm to achieve parallel execution. Being able to do this effectively depends

on the availability of exploitable parallelism in the algorithm and the ability to expose this parallelism in the form of an accelerator circuit without errors or excessive effort, complexity, or cost. In particular, creating accelerators for irregular code that’s difficult to analyze or lacks parallelism is often challenging, if not impossible. The conservation cores that comprise the GreenDroid system have different,

but related, underlying goals compared to accelerators. Fundamentally, conservation cores (c-cores) focus on reducing the energy of

executing code, even if they only modestly improve the resulting execution time. As a result, c-cores don’t rely on parallelization technology for a successful outcome; they can target any code in which sufficient execution time is spent. Because of this, a far greater percentage of code can be turned into c-cores than can be transformed into accelerators.

We expect that in a commercialized version of GreenDroid, code

that can be accelerated (such as video encoding or baseband processing)

would be expressed as accelerators and generated using either handcrafted accelerators or high-level synthesis tools. The remaining unacceleratable,

irregular code (millions and millions of lines of it) creates an

Amdahl’s-law-like limit on the system’s maximum energy efficiency. Conservation cores provide an automatic way to reduce the energy of this remaining class of difficult code. This distinction drives some of the

key differences between GreenDroid’s c-cores generation and conventional high-level synthesis technology, which focuses on generating accelerators from parallelizable code.

Preconclusion GreenDroid:
GreenDroid is a 45 nm multicore research prototype that targets the Android mobile phone software stack and can execute general-purpose mobile programs with 11 times less energy than today’s most energy-efficient designs, at similar or better levels of performance. It does this through the use of 100 or so automatically generated, highly specialized, energy-reducing cores, called conservation cores,or c-cores. GreenDroid will serve as a prototype for mobile application processors in the next five to ten years.
The GreenDroid presents a novel concept for Smartphone users: a CPU custom-designed around the apps (or perhaps types of apps) one uses the most. It has a specially built structure that can analyze a current Android phone and determine which apps, and which CPU circuits the phone is using the most. Then it can dream up a processor design that best takes advantage of those usage habits, creating a CPU that's both faster and more energy efficient.
In particular, GreenDroid attacks one of the most important realities of Moore’s Law(The number of transistors that can be placed inexpensively on an integrated circuit doubles approximately every two years.) as it continues from today into the future, which we refer to as the DARK SILICON.
Energy efficiency or saving energy is the next evolution in application processors. Dark silicon is becoming the rule rather than the exception as process technologies shrink, and the problem is getting worse.To combat this we need to have a mobile application processor with specialized cores that flip dark silicon from a liability to a benefit.
DARK SILICON
Conventional chip designs in a mobile phone lose significant energy due to the presence of unused transistors called dark silicon. Dark silicon is when we have three billion transistors on our chip but we can only use 1.8 percent of them at a time, so as to squeak under the threshold of our chip's draconian energy budget. So the lights are out on vast swaths of a chip's area; hence "dark silicon”. As the transistor counts are growing faster, the underlying energy efficiency is improving, a direct consequence of this is the phenomenon of dark silicon. The dark silicon problem is directly responsible for the desktop processor industry’s decision to stop scaling clock frequencies and instead build multi-core processors. It plays an equally pivotal role in shaping the future of mobile processors as well. With each process generation, dark silicon is a resource that gets exponentially cheaper, while the power budget becomes exponentially more valuable in comparison.Dark silicon is necessary, because engineers are unable to reduce chips' operating voltages any further to offset increases in power consumption and waste heat produced by smaller, faster chips.This dark silicon limits the ultilization of the application processors to the fullest.
GreenDroid proves to be a boon to this dark silicon problem. It follows the ideology “If you fill the chip with highly specialized cores, then the fraction of the chip that is lit up at one time can be the most energy efficient for that particular task”.These highly specialized cores are known as conservation cores or c-cores.
Specialized, energy-efficient processors can increase parallelism by reducing the per-computation power requirements and allowing more computations to execute under the same power budget. To pursue this goal Conservation cores, or c-cores, are specialized processors that focus on reducing energy and energy-delay instead of increasing performance. This focus on energy makes c-cores an excellent match for many applications that would be poor candidates for hardware acceleration (e.g., irregular integer codes). A toolchain is used for automatically synthesizing c-cores from application source code and demonstrating that they can significantly reduce energy and energy-delay for a wide range of applications. The c-cores support patching, a form of targeted reconfigurability, that allows them to adapt to new versions of the software they target.

CONCLUSION:

GreenDroid is a 45 nm multicore research prototype that targets the Android mobile phone software stack and can execute general-purpose mobile programs with 11 times less energy than today’s most energy-efficient designs, at similar or better levels of performance. It does this through the use of 100 or so automatically generated, highly specialized, energy-reducing cores, called conservation cores,or c-cores. GreenDroid will serve as a prototype for mobile application processors in the next five to ten years.

So greendroid is always better than androd.

DATA MINING

B.OBULIRAJ

B.E CSE

DATA MINING

INTRODUCTION

Data mining, a branch of computer science  and artificial intelligence, is the process of extracting patterns from data. Data mining is seen as an increasingly important tool by modern business to transform data into business intelligence giving an informational advantage. It is currently used in a wide range of profiling practices, such as marketing, surveillance, fraud detection, and scientific discovery.

DEFINITION

Data mining, the extraction of hidden predictive information from large databases, is a powerful new technology with great potential to help companies focus on the most important information in their data warehouses.

Data mining commonly involves four classes of tasks:

·         Clustering - is the task of discovering groups and structures in the data that are in some way or another "similar", without using known structures in the data.

·         Classification - is the task of generalizing known structure to apply to new data. For example, an email program might attempt to classify an email as legitimate or spam. Common algorithms include decision tree learning, nearest neighbour, naive Bayesian classification, neural networks and support vector machines.

·         Regression - Attempts to find a function which models the data with the least error.

·         Association rule learning - Searches for relationships between variables. For example a supermarket might gather data on customer purchasing habits. Using association rule learning, the supermarket can determine which products are frequently bought together and use this information for marketing purposes. This is sometimes referred to as market basket analysis.

THE FOUNDATION OF DATA MINING

     Data mining techniques are the result of a long process of research and product development. This evolution began when business data was first stored on computers, continued with improvements in data access, and more recently, generated technologies that allow users to navigate through their data in real time. Data mining takes this evolutionary process beyond retrospective data access and navigation to prospective and proactive information delivery. Data mining is ready for application in the business community because it is supported by three technologies that are now sufficiently mature:

·         Massive data collection

·         Powerful multiprocessor computers

·         Data mining algorithms

THE SCOPE OF DATA MINING

     Data mining derives its name from the similarities between searching for valuable business information in a large database — for example, finding linked products in gigabytes of store scanner data — and mining a mountain for a vein of valuable ore. Both processes require either sifting through an immense amount of material, or intelligently probing it to find exactly where the value resides. Given databases of sufficient size and quality, data mining technology can generate new business opportunities by providing these capabilities:

·         Automated prediction of trends and behaviors. Data mining automates the process of finding predictive information in large databases. Questions that traditionally required extensive hands-on analysis can now be answered directly from the data — quickly. A typical example of a predictive problem is targeted marketing. Data mining uses data on past promotional mailings to identify the targets most likely to maximize return on investment in future mailings. Other predictive problems include forecasting bankruptcy and other forms of default, and identifying segments of a population likely to respond similarly to given events.

·         Automated discovery of previously unknown patterns. Data mining tools sweep through databases and identify previously hidden patterns in one step. An example of pattern discovery is the analysis of retail sales data to identify seemingly unrelated products that are often purchased together. Other pattern discovery problems include detecting fraudulent credit card transactions and identifying anomalous data that could represent data entry keying errors.

                  Data mining techniques can yield the benefits of automation on existing software and hardware platforms, and can be implemented on new systems as existing platforms are upgraded and new products developed. When data mining tools are implemented on high performance parallel processing systems, they can analyze massive databases in minutes. Faster processing means that users can automatically experiment with more models to understand complex data. High speed makes it practical for users to analyze huge quantities of data. Larger databases, in turn, yield improved predictions.

                  Databases can be larger in both depth and breadth:

·         More columns. Analysts must often limit the number of variables they examine when doing hands-on analysis due to time constraints. Yet variables that are discarded because they seem unimportant may carry information about unknown patterns. High performance data mining allows users to explore the full depth of a database, without preselecting a subset of variables.

·         More rows. Larger samples yield lower estimation errors and variance, and allow users to make inferences about small but important segments of a population.

                        A recent Gartner Group Advanced Technology Research Note listed data mining and artificial intelligence at the top of the five key technology areas that "will clearly have a major impact across a wide range of industries within the next 3 to 5 years."2 Gartner also listed parallel architectures and data mining as two of the top 10 new technologies in which companies will invest during the next 5 years. According to a recent Gartner HPC Research Note, "With the rapid advance in data capture, transmission and storage, large-systems users will increasingly need to implement new and innovative ways to mine the after-market value of their vast stores of detail data, employing MPP [massively parallel processing] systems to create new sources of business advantage (0.9 probability)."3

The most commonly used techniques in data mining are:

·         Artificial neural networks: Non-linear predictive models that learn through training and resemble biological neural networks in structure.

·         Decision trees: Tree-shaped structures that represent sets of decisions. These decisions generate rules for the classification of a dataset. Specific decision tree methods include Classification and Regression Trees (CART) and Chi Square Automatic Interaction Detection (CHAID) .

·         Genetic algorithms: Optimization techniques that use processes such as genetic combination, mutation, and natural selection in a design based on the concepts of evolution.

·         Nearest neighbor method: A technique that classifies each record in a dataset based on a combination of the classes of the k record(s) most similar to it in a historical dataset (where k ³ 1). Sometimes called the k-nearest neighbor technique.

·         Rule induction: The extraction of useful if-then rules from data based on statistical significance.

                        Many of these technologies have been in use for more than a decade in specialized analysis tools that work with relatively small volumes of data. These capabilities are now evolving to integrate directly with industry-standard data warehouse and OLAP platforms. The appendix to this white paper provides a glossary of data mining terms.

ARCHITECTURE FOR DATA MINING

            To best apply these advanced techniques, they must be fully integrated with a data warehouse as well as flexible interactive business analysis tools. Many data mining tools currently operate outside of the warehouse, requiring extra steps for extracting, importing, and analyzing the data. Furthermore, when new insights require operational implementation, integration with the warehouse simplifies the application of results from data mining. The resulting analytic data warehouse can be applied to improve business processes throughout the organization, in areas such as promotional campaign management, fraud detection, new product rollout, and so on.

    The ideal starting point is a data warehouse containing a combination of internal data tracking all customer contact coupled with external market data about competitor activity. Background information on potential customers also provides an excellent basis for prospecting. This warehouse can be implemented in a variety of relational database systems: Sybase, Oracle, Redbrick, and so on, and should be optimized for flexible and fast data access.

            An OLAP (On-Line Analytical Processing) server enables a more sophisticated end-user business model to be applied when navigating the data warehouse. The multidimensional structures allow the user to analyze the data as they want to view their business – summarizing by product line, region, and other key perspectives of their business. The Data Mining Server must be integrated with the data warehouse and the OLAP server to embed ROI-focused business analysis directly into this infrastructure. An advanced, process-centric metadata template defines the data mining objectives for specific business issues like campaign management, prospecting, and promotion optimization. Integration with the data warehouse enables operational decisions to be directly implemented and tracked. As the warehouse grows with new decisions and results, the organization can continually mine the best practices and apply them to future decisions.

            This design represents a fundamental shift from conventional decision support systems. Rather than simply delivering data to the end user through query and reporting software, the Advanced Analysis Server applies users’ business models directly to the warehouse and returns a proactive analysis of the most relevant information. These results enhance the metadata in the OLAP Server by providing a dynamic metadata layer that represents a distilled view of the data. Reporting, visualization, and other analysis tools can then be applied to plan future actions and confirm the impact of those plans.

PROFITABLE APPLICATIONS

            A wide range of companies have deployed successful applications of data mining. While early adopters of this technology have tended to be in information-intensive industries such as financial services and direct mail marketing, the technology is applicable to any company looking to leverage a large data warehouse to better manage their customer relationships. Two critical factors for success with data mining are: a large, well-integrated data warehouse and a well-defined understanding of the business process within which data mining is to be applied (such as customer prospecting, retention, campaign management, and so on).

Some successful application areas include:

·         A pharmaceutical company can analyze its recent sales force activity and their results to improve targeting of high-value physicians and determine which marketing activities will have the greatest impact in the next few months.

·         A credit card company can leverage its vast warehouse of customer transaction data to identify customers most likely to be interested in a new credit product. Using a small test mailing, the attributes of customers with an affinity for the product can be identified. Recent projects have indicated more than a 20-fold decrease in costs for targeted mailing campaigns over conventional approaches.

·         A diversified transportation company with a large direct sales force can apply data mining to identify the best prospects for its services. Using data mining to analyze its own customer experience, this company can build a unique segmentation identifying the attributes of high-value prospects. Applying this segmentation to a general business database such as those provided by Dun & Bradstreet can yield a prioritized list of prospects by region.

·         A large consumer package goods company can apply data mining to improve its sales process to retailers. Data from consumer panels, shipments, and competitor activity can be applied to understand the reasons for brand and store switching. Through this analysis, the manufacturer can select promotional strategies that best reach their target customer segments.

                  Each of these examples have a clear common ground. They leverage the knowledge about customers implicit in a data warehouse to reduce costs and improve the value of customer relationships. These organizations can now focus their efforts on the most important (profitable) customers and prospects, and design targeted marketing strategies to best reach them.

CONCLUSION

            Comprehensive data warehouses that integrate operational data with customer, supplier, and market information have resulted in an explosion of information. Competition requires timely and sophisticated analysis on an integrated view of the data. Both relational and OLAP technologies have tremendous capabilities for navigating massive data warehouses, but brute force navigation of data is not enough. A new technological leap is needed to structure and prioritize information for specific end-user problems. Quantifiable business benefits have been proven through the integration of data mining with current information systems, and new products are on the horizon that will bring this integration to an even wider audience of users.