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P2050-007 IBM Optimization Technical Mastery Test v1

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Test Code : P2050-007
Test title : IBM Optimization Technical Mastery Test v1
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: 30 true Questions

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IBM IBM Optimization Technical Mastery

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No influence discovered, try fresh keyword!in spite of this, searching from the technical charting point of view ... the SVP and CFO of IBM, observed within the profits title that the company has been in quest of to enhance its "team of workers optimization productivity ...

E-commerce startup Fluid uses IBM’s Watson. Now IBM invests in Fluid | killexams.com true Questions and Pass4sure dumps

Use IBM’s Watson technology in an flattering way and you’ll exist rewarded.

That’s actually the way things seem nowadays when massive Blue announced it had made an investment in Fluid, an e-commerce software enterprise that has built-in Watson — that may exist trained in accordance with a lot of historical guidance after which reply questions about the wing — into its virtual searching aide utility.

IBM didn’t dispute how much cash it has poured into Fluid, however they effect recognize simply how committed IBM is to backing groups that employ its Watson expertise. previous this year it announced $100 million in undertaking funding would fade to organizations operating “a fresh category of cognitive apps powered by means of Watson.” outdated Watson-oriented investments from IBM encompass health-optimization company Welltok.

IBM referred to in January that Welltok and Fluid would fade to market this year with Watson-enabled capabilities, and now both of them beget achieve up with funding from IBM. MD Buyline, a corporation with utility for tracking clinical device, is likewise slated to hit the market quickly, IBM observed at the time. perhaps when that occurs, IBM will throw some funding at MD Buyline, too.

All these strikes could aid IBM develop into a platform for processing a lot of distinctive styles of records through a cloud. They might support convey tremendous Blue nearer to strategic dreams around working organizations from clouds in preference to undifferentiated servers for facts centers.

The Watson investing additionally buddies IBM with hip startups in residence of sluggish-moving organizations. IBM’s SoftLayer and Cloudant acquisitions beget introduced video game developers and other early-stage IT patrons to IBM. directly backing groups enjoy Fluid after they’re open to showing off what Watson can effect could lead on greater americans to reflect about IBM as not just a mainframe vendor however as an organization that allows for next-technology experiences.

Fluid chief executive Kent Deverell offers americans a taste for such an journey in a weblog post nowadays:

think about buying technical equipment, enjoy a tent, on-line today. you possibly can consult with a retailer or industry web site and search tents with the aid of narrowing down factors enjoy dimension, style, and a pre-determined record of elements. examine that to the in-shop event of attractive with an associate and asserting, “i'm taking my family camping in upstate the tremendous apple in October and that i want a tent. What may still I believe?” Fluid’s Watson-enabled cellular app will allow buyers to invent this identical query online in simple English to find the exemplar tent in addition to sudden gadgets essential to complete the adventure.

via their partnership with IBM, we're taking buyers into a fresh era of computing. soon, you’ll no longer class the exist cognizant ‘backpack’ in a retail site’s search container and spend hours poring through dozens of alternate options, experiences, and extra in discovering the foremost product. instead, you’ll examine Watson what type of backpack is acceptable for a 14-day mountain climbing expedition in Patagonia and immediately acquire personalised, data-pushed responses to efficiently book your determination.


Compiling history To exist cognizant the long dash | killexams.com true Questions and Pass4sure dumps

in case you wish to stand in intelligence where we're going with computing device architectures and the compilers that pressure them, it's instructive to examine how compilers beget made the soar from architecture to architecture genesis six many years ago.

Let’s fade lower back to the very first compiler, IBM Fortran, in 1957. It was an astonishing piece of technology. in case you peer at the residence it begun and what they came up with, it become the contour of endeavor that you can’t even reflect about somebody undertaking today.

IBM desired to sell computer systems, and to sell computer systems it essential to allow more people to application them. on the time programming turned into finished with meeting language. This was too difficult, and IBM knew that. So massive Blue desired a way for people to jot down courses more immediately, with out sacrificing performance. The Fortran builders – which means those who created Fortran, not the classes that used the compiler and language – desired to deliver performance as shut as practicable at hand-tuned desktop code from programs written in what nowadays they title a excessive-level programming language.

When it involves compilers, you should reckon with the three Ps: performance, productivity, and portability. The creators of Fortran had been evaluating performance to laptop code. The productiveness improvement turned into that programmers not had to write that desktop code. I don’t know IBM’s usual intent related to portability, however you couldn't patent utility in 1957 and IBM didn’t bitch about different organizations imposing Fortran. So, shortly thereafter, Fortran compilers for other machines from different carriers appeared. This instantly gave Fortran courses portability that was unattainable with computing device code.

Compilers had been off and operating from this point ahead. Let’s analyze it through decade.

the first Decade

It changed into in the 1960s, simply earlier than my time, that computer architects and hence compiler writers first begun to believe about parallelism. Even then people were thinking that computer systems had been no longer speedy satisfactory, and were no longer getting sooner directly enough, and saw parallelism as a probable reply to that problem. They saw the introduction of instruction-level parallelism in Seymour Cray’s CDC 6600 and CDC 7600, and within the IBM device/360 mannequin 91. much more aggressive changed into the structure of the ILLIAC IV designed through researchers at the university of Illinois and developed by Burroughs, the manage statistics Corp megastar-one hundred, and the Texas contraptions superior Scientific computer. The CDC and TI systems had been memory-to-reminiscence lengthy vector machines, whereas the ILLIAC IV become what today they might convene a SIMD computer. The ILLIAC was restricted in what it may effect as a result of there become no basic scalar processor, and it was basically tough to program. The Fortran compiler for the celebrity-100 brought syntax to portray lengthy, contiguous vector operations. The TI ASC desktop turned into the most pleasing since it had the primary automatic vectorizing compiler. TI did some astounding work and truly stretched the state of the knack for compiler evaluation at the time.

The 2nd Decade

within the 1970s, the Cray-1 became the first commercially successful supercomputer. It had many observe-ons and many copycats, which lots of readers doubtless understand very neatly. The success of the Cray machines turned into due in massive half to the introduction of vector registers. just enjoy scalar registers, they allowed programs to effect many operations on small vectors of records without needing to load from and shop to reminiscence. Cray analysis likewise developed a really aggressive vectorizing compiler. It was equivalent in lots of respects to the prior TI compiler, but the Cray compiler had additional capabilities that made it reasonably wonderful. one of the most essential turned into the capacity to give compiler comments to the programmer.

When developers collect a program these days, the compiler generates error messages if the program carries syntax errors. The programmer continues fixing these error and re-compiling except they beget got a working application. If a developer wishes a software to dash faster, they could enable a compiler optimization flag to invent the generated executable dash quicker – they hope. The change between optimized code and non-optimized code on most machines is probably a factor of two, and usually a whole lot less – dispute 10 p.c, 20 %, or maybe 50 % – superior performance. distinction that with the vector pilot sets obtainable on the original Cray machines. if so, programmers would often notice a factor of 5X to 10X evolution when code become optimized and vectorized by way of the Cray compiler. Programmers, and particularly HPC programmers, are inclined to effect lots for a factor of 5X or greater in performance development.

The remarks from the Cray compiler would bid the programmer that it had vectorized a loop at line 110 and one more loop at line 220, but it did not vectorize the loop at line 230 because there was an I/O commentary or a characteristic convene at line 235 which couldn’t exist vectorized. Or, probably there changed into an array reference that the compiler couldn’t analyze, or an unknown variable in the 2d subscript of a specific array that hindered dependence analysis, so the loop couldn’t exist vectorized. Cray programmers, desperate to obtain vector efficiency, paid cautious attention to those messages. They modified their code in line with this remarks, maybe taking an I/O remark out of a loop, or pushing a loop prerogative into a subroutine, or editing an array reference to remove an unknown variable. once in a while they'd add a compiler directive to carry to the compiler that it’s secure to vectorize although the compiler can’t verify that via dependence analysis.

due to that compiler feedback, three things happened.

First, more courses beget been vectorized and greater programmers benefited from Cray vector efficiency. second, Cray programmers had been knowledgeable to cease placing I/O statements, conditional statements, and system calls in the seat of their loops. They realized that stride one entry to information became essential and made bound array accesses of their inner loops beget been stride one. Third, courses that auto-vectorized with the Cray compiler may exist recompiled on a lot of an identical vector machines from Alliant, Convex, Digital, Fujitsu, Hitachi, IBM, and NEC. All of those techniques had vector processors with vector registers and automated vectorizing compilers, and code up to now optimized for a Cray happened to vectorize and dash pretty smartly on All of these machines. in short, Cray programmers ended up with a trifecta of performance, productiveness, and portability.

i will’t over-emphasize how crucial this compiler remarks become, and how a hit it changed into in practicing a whole era of HPC developers to program vector machines. every person that used it become very happy. returned in those days, i was on the institution of Illinois and they concept about genesis a company based mostly round parallelizing compiler technology. Of path, their technology turned into stronger than any person else’s as a result of they had been the smart teachers. within the early days of the Cray compiler, clients complained that it couldn’t vectorize the relaxation and that they had to rewrite their code. They thought they may transparent up these issues and commenced this limited enterprise to just effect that. when they approached these equal users a few years later and confirmed them their tools to parallelize and vectorize loops, they instructed us they didn’t exigency such tackle since the Cray compiler already vectorized All of their loops. It wasn’t so a whole lot that the Cray compiler obtained smarter, although I’m sure it got more suitable over time. primarily, it was that programmers had been without problems educated in a way to write vectorizable loops.

The Third Decade

The Eighties introduced the widespread implementation and employ of multiprocessors. There had been multiprocessors earlier, together with IBM device/370s and Burroughs methods. nonetheless it become the introduction of 32-bit single-chip microprocessors that drove multiprocessing into the mainstream. To birth a laptop company, you no longer essential to design a processor – you could buy that. You didn’t exigency to write an working system – you could license Unix. All you vital become a person to screw it All collectively and achieve a nameplate on it. Oh, and it will exist respectable to beget a compiler for All of that. Sequent, Encore, and SGI All constructed techniques that had excellent performance with one microprocessor, but when you might snare a bunch of them operating in parallel it became even enhanced.

unlike automatic vectorization, which changed into highly a hit, automated parallelization changed into definitely a bust. It worked terrific for innermost loops, but to obtain significant parallel pace-u.s.you usually must parallelize outer loops. however of direction, outer loops add complexity to the control circulate and sometimes consist of procedure calls, and now your compiler analysis absolutely falls apart. An approach that did work changed into to snare the programmer involved, analyzing the parallelism and entering compiler directives to power it. Out of this, they noticed the emergence of numerous directive sets for parallel loops. Cray, Encore, IBM, and Sequent each had their own directive sets, the only commonality being that SGI adopted the Sequent directives. similarly, there beget been a brace of scalable systems with network message passing, which drove the construction of plenty of supplier-specific and academic message-passing libraries.

The Fourth Decade

in the 1990s, All those message-passing libraries beget been supplanted by the Message Passing Interface (MPI), and All those seller-selected parallelization directives had been replaced by using OpenMP. more scalable parallel techniques looked, such because the thinking Machines CM-5 and others with hundreds of commodity microprocessors. Microprocessor-based mostly scalable techniques beget been programmed generally with MPI, however MPI is a library and is opaque to compilers. The talents of MPI as a programming mannequin is that while verbal exchange is high priced, it is terribly lots uncovered within the application. MPI programmers know when they are inserting express communication calls, and they work very hard to reduce and optimize conversation as a lot as viable. The downside is that from a programming model point of view it’s very low stage, and MPI programmers snare no support from compilers.

OpenMP changed into born out of that diversity of dealer-particular parallel directive units as a result of users demanded it. They refused to rewrite their programs 12 instances just to exist able to dash them on All the diverse programs that beget been obtainable. The whole charter of OpenMP turned into to spell “PARALLEL DO” the identical means. unlike MPI, directives beget to exist supported through a compiler because they're portion of the language. that you may’t implement OpenMP as a library.

additionally within the Nineties, they noticed the emergence of SIMD instruction units for single-chip microprocessors, enjoy SSE and SSE2 from Intel, and they noticed compilers resurrecting the equal vectorization expertise that become by then 20 or 25 years primitive in an endeavor to invent the most these SIMD directions automatically.

The Fifth Decade

shortly after 2000, multicore microprocessors grew to become frequently available from a number of providers. the world All of sudden realized that this parallel programming problem needed to exist solved for the loads.

at the time, a lot of purposes used a flat MPI programming model throughout the entire cores on a chip in addition to across distributed-reminiscence nodes. you could All the time add extra MPI ranks to invent employ of more parallelism. This works to a degree, but some MPI courses achieve across scaling issues in case you delivery to snare to really significant numbers of ranks. The remembrance utilization can snare a bit out of ply when statistics is replicated in every MPI rank. when you delivery inserting a dozen or two dozen cores on each node, this is 12X or 24X the volume of reminiscence in employ for every particular it truly is replicated. For programming a single node, OpenMP and other shared-memory parallel programming models began to gain traction. as an instance, in the Nineteen Nineties Intel delivered its Threading structure Blocks (aka TBB) and by way of some claims it’s essentially the most established parallel programming language nowadays.

around this identical time heterogeneous HPC programs began to seem, besides the fact that children heterogeneity turned into not in reality new. They had heterogeneity in the Sixties with connected co-processors for array processing. attached processors beget been very common as a result of they might effect really estimable operations sooner than the CPU. They may often bring the efficiency of a mainframe at the cost of a minicomputer, some thing that Floating aspect techniques did neatly for about 15 years. within the early 2000s, there beget been a number of accelerators developed exceptionally or re-purposed for the HPC market, such because the ClearSpeed accelerator and the IBM cellphone processor. each items confirmed some actual technical success, but the HPC market proved to exist too small to wait on the evolution of customized processor chips on its own.

That left a gap open for GPU computing. The abilities of GPUs over customized accelerators is that a GPU has a very estimable day job – in the early days it changed into pictures and gaming, and now it includes AI and deep getting to know as well – so the very inordinate cost of developing a processor on the latest silicon thoughts and the application essential to force it may likewise exist sustained.

With the upward thrust of heterogeneity got here the glaring query – how effect they software this stuff? back in the day, Floating aspect techniques delivered a subroutine library that used their accelerators under the hood. Programmers known as into the library, and the HPC hundreds may readily employ the accelerator without in reality programming it (although there beget been some programmers who did). today, to address the latitude of applications that beget been developed during the years on scalar, vector, and scalable techniques, HPC developers deserve to exist able to software accelerators effectively and that they exigency their classes to peer as regular as viable. using OpenCL or CUDA gives you loads of control but can exist very intrusive with recognize to the impact on current HPC supply code. The programmer customarily extracts code to exist dash on the accelerator into in particular annotated services, and often writes it in a very diverse manner than it’s written for the host. this is where directive-based programming models and languages notably designed for ordinary-goal parallel programming beget some benefits, and an outstanding optimizing compiler can are available very effortless.

dwelling In A Parallel Universe

This All brings us to yet another very censorious point. the entire applied sciences developed in the 1960s for guideline-level parallelism are actually inside of your microprocessor. The vector processing concepts from the Seventies live on in the SIMD registers inner of your microprocessor. The distinctive processors of the 1980s Crays and commodity processor-based methods are All inside of your microprocessor in the type of dissimilar cores. The microprocessor these days is in reality the sum of All architectural work finished during the past 50 years, and they can beget the funds for to effect this on account of the miraculous transistor budget – billions of gates – that they now have. And they additionally now beget heterogeneity, and, in some cases, they even beget that on the identical chip. for instance, the Sunway chip in the TaihuLight tackle in China is one chip from a packaging standpoint, but every of the quadrants on the die comprises a grasp processor and sixty four compute processors. So, it's fundamentally heterogeneous, and you software it more enjoy a CPU-GPU hybrid than a multicore processor.

To seize plenary potential of heterogeneous GPU-accelerated nodes, a application should expose a lot of parallelism – and the usurp sort of parallelism. These incredibly parallel processors deliver performance not from a faster clock, and never because of some extraordinarily exotic architecture. fairly, it’s as a result of extra of the attainable gates are used for parallel cores than are used for cache, out of order execution, or branch prediction. All of these staples of commodity CPUs are neglected of GPUs, and the influence is a vastly parallel processor which gives you extra throughput on germane kernels and applications.

Which brings us returned to the three P’s, and the indisputable fact that performance isn't the best purpose for HPC builders. Programmers want inordinate efficiency, estimable productiveness, and extensive portability. And most of them are looking to write their classes handiest once, now not varied times within the equal file with different paths chosen in line with conditional compilation flags.

in the days when All that you simply had beget been single processors on a node and All parallelism spanned throughout nodes, programmers may snare away with using flat MPI. Such programs worked smartly throughout a huge family of machines, but that is not any longer the HPC world they live in these days. they now beget multiple processors in a node, they now beget SIMD guidelines, and they now beget heterogeneous accelerators that interject much more sorts of parallelism. To achieve portability, they exigency to exist in a position to write programs to exist able to map efficaciously across differing numbers of cores, discrete SIMD or vector lengths, and techniques that are either homogeneous or heterogeneous – with no exigency to rewrite programs every time they stream to a fresh system. For productiveness, they exigency to abstract as a estimable deal of this away as feasible. What we’re attempting to deliver in compilers for today’s HPC systems are the equal summary traits that IBM delivered six many years ago with the first Fortran compiler, which truly was the primary high-stage language compiler of any category.

Getting as shut as practicable at hand-coded efficiency on these days’s extraordinarily complicated hardware is the aim. these days, the PGI OpenACC compiler targeting NVIDIA GPUs can commonly deliver nigh to native CUDA efficiency for small, straightforward code blocks. For more complicated code sequences enjoy tremendous capabilities, or when complete convene trees are being ported to the GPU, it’s a estimable deal greater tricky to routine native code efficiency.

To exist reasonable, the OpenACC compiler is at an inherent drawback. When rewriting a code in CUDA, a programmer might determine that a given information constitution isn't usurp for the GPU and may alternate it to boost parallelism or efficiency. possibly the programmer determines a portion of the application logic isn’t rather correct for a GPU, or that facts access patterns are suboptimal, and rewrites that common sense or loop to augment records access patterns. in case you invent these identical alterations within the equal OpenACC software, you frequently snare tons more advantageous performance as smartly. but you could possibly rather not ought to effect this, mainly if the rewrite slows down the code on a multicore CPU. in consequence, the OpenACC code can likewise not snare the identical stage of efficiency as if you had expended that coding effort. however, if the performance of the OpenACC code is shut ample to CUDA on the GPU, the productivity and portability merits of a directive-based programming model will commonly succeed.

despite All the progress we’ve made in compiler know-how over the ultimate 60 years, some americans are nonetheless of the view that compilers are more of an issue than an answer. They exigency predictability from a compiler, not the superior evaluation and code transformations that optimizing compilers effect at the back of the scenes. that may lead down a path the residence they try to seize functionality out of compilers and achieve greater accountability within the fingers of the programmer. you'll exist able to argue that here is exactly the direction that OpenMP is following nowadays, and the hazard I notice is that they may additionally achieve to exist sacrificing portability and productiveness for predictability. as an instance, as an alternative of automatic vectorization for the SIMD guidelines on Intel Xeon processors, imagine in case you still needed to vectorize every loop manually the usage of Intel’s SSE and AVX intrinsic capabilities. You’re effortlessly writing inline assembly code. That is terribly predictable, however few programmers wish to write All of their compute-intensive code at that level. primarily if you exigency to rewrite that code for every generation of SIMD instructions, or to invent employ of the SIMD guidelines on non-X86 CPUs.

Any estimable value boost in computing device structure can and should comprise an affordable further in compiler technology. The odds isn't measured simplest via the performance these architectures and compilers can bring. It likewise exigency to exist measured by means of the human endeavor saved and productivity gained when programs can exist ported from one generation of HPC tackle to the next without having to exist fully rewritten.

Michael Wolfe has labored on languages and compilers for parallel computing considering that graduate college at the institution of Illinois within the Nineteen Seventies. along the way, he co-headquartered Kuck and associates (obtained by Intel), tried his hand in academia at the Oregon Graduate Institute (considering merged with the Oregon fitness and Sciences school), and labored on high performance Fortran at PGI (bought by STMicroelectronics and more recently through Nvidia). He now spends most of his time as the technical lead on a group that develops and improves the PGI compilers for incredibly parallel computing, and in selected for Nvidia GPU accelerators.


P2050-007 IBM Optimization Technical Mastery Test v1

Study pilot Prepared by Killexams.com IBM Dumps Experts


Killexams.com P2050-007 Dumps and true Questions

100% true Questions - Exam Pass Guarantee with high Marks - Just Memorize the Answers



P2050-007 exam Dumps Source : IBM Optimization Technical Mastery Test v1

Test Code : P2050-007
Test title : IBM Optimization Technical Mastery Test v1
Vendor title : IBM
: 30 true Questions

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Unfriendly Skies: Predicting Flight Cancellations Using Weather Data (Part 2) | killexams.com true questions and Pass4sure dumps

As they described in portion 1 of this series, their objective is to wait on foretell the probability of the cancellation of a flight between two of the ten U.S. airports most affected by weather conditions. They employ historical flights data and historical weather data to invent predictions for upcoming flights.

Over the course of this four-part series, they employ different platforms to wait on us with those predictions. Here in portion 2, they employ the IBM SPSS Modeler and APIs from The Weather Company.

Tools Used in This employ Case Solution

IBM SPSS Modeler is designed to wait on learn patterns and trends in structured and unstructured data with an intuitive visual interface supported by advanced analytics. It provides a sweep of advanced algorithms and analysis techniques, including text analytics, entity analytics, conclusion management, and optimization to deliver insights in near real-time. For this employ case, they used SPSS Modeler 18.1 to create a visual representation of the solution, or in SPSS terms, a stream. That's right: not one line of code was written in the making of this blog.

We likewise used The Weather Company APIs to retrieve historical weather data for the ten airports over the year 2016. IBM SPSS Modeler supports calling the weather APIs from within a stream. That is accomplished by adding extensions to SPSS, available in the IBM SPSS Predictive Analytics resources page, AKA Extensions Hub.

A Proposed Solution

In this blog, they pose one practicable solution for this problem. It's not meant to exist the only or the best practicable solution, or a production-level solution for that matter, but the discussion presented here covers the typical iterative process (described in the sections below) that helps us accumulate insights and refine the predictive model across iterations. They inspirit the readers to try and achieve up with different solutions and provide us with your feedback for future blogs.

Business and Data Understanding

The first step of the iterative process includes understanding and gathering the data needed to train and test their model later.

Flights Data

We gathered 2016 flights data from the US Bureau of Transportation Statistics website. The website allows us to export one month at a time, so they ended up with 12 CSV (comma-separated value) files. They used IBM SPSS Modeler to merge All the CSV files into one set and to select the ten airports in their scope. Some data clean-up and formatting was done to validate dates and hours for each flight, as seen in motif 1.

Image title

Figure 1: Gathering and preparing flights data in IBM SPSS Modeler 

Weather Data

From the Extensions Hub, they added the TWCHistoricalGridded extension to SPSS Modeler, which made the extension available as a node in the tool. That node took a CSV file listing the 10 airports latitude and longitude coordinates as input, and generated the historical hourly data for the entire year of 2016, for each airport location, as seen in motif 2.

Image title

Figure 2: Gathering and preparing weather data in IBM SPSS Modeler

Combined Flights and Weather Data

To each flight in the first data set, they added two fresh columns, ORIGIN and DEST, containing the respective airport codes. Next, flight data and the weather data were merged together. Note: The "stars" or SPSS supernodes in motif 3 are placeholders for the diagrams in Figures 1 and 2 above.

Image title

Figure 3: Combining flights and weather data in IBM SPSS Modeler

Data Preparation, Modeling, and Evaluation

We iteratively performed the following steps until the desired model qualities were reached:

  • Prepare data

  • Perform modeling

  • Evaluate the model

  • Repeat

  • Figure 4 shows the first and second iterations of their process in IBM SPSS Modeler.

    Image title

    Figure 4: Iterations (prepare data, dash models, evaluate) — and effect it again

    First Iteration

    To start preparing the data, they used the combined flights and weather data from the previous step and performed some data cleanup (for example, took trust of null values). In order to better train the model later on, they filtered out rows where flight cancellations were not related to weather conditions (such as cancellations due to technical issues, security issues, etc.)

    Image title

    Figure 5: Imbalanced data create in their input data set

    This is an enchanting employ case, and often a hard one to solve, due to the imbalanced data it presents, as seen in motif 5. By "imbalanced" they carry weight that there were far more non-canceled flights in the historical data than canceled ones. They will argue how they dealt with imbalanced data in the following iteration.

    Next, they defined which features were required as inputs to the model (such as flight date, hour, day of the week, origin and destination airport codes, and weather conditions), and which one was the target to exist generated by the model (i.e. foretell the cancellation status). They then partitioned the data into training and testing sets, using an 85/15 ratio.

    The partitioned data was fed into an SPSS node called Auto Classifier. This node allowed us to dash multiple models at once and preview their outputs, such as the zone under the ROC curve, as seen in motif 6.

    Figure 6: Models output provided by the Auto Classifier node

    That was a useful step in making an initial selection of a model for further refinement during subsequent iterations. They decided to employ the Random Trees model since the initial analysis showed it has the best zone under the curve as compared to the other models in the list.

    Second Iteration

    During the second iteration, they addressed the skewed-ness of the original data. For that purpose, they chose one of the SPSS nodes called SMOTE (Synthetic Minority Over-sampling Technique). This node provides an advanced over-sampling algorithm that deals with imbalanced datasets, which helped their selected model work more effectively.

    Figure 7: Distribution of canceled and non-canceled flights after using SMOTE

    In motif 7, they notice a more balanced distribution between canceled and non-canceled flights after running the data through SMOTE.

    As mentioned earlier, they picked the Random Trees model for this sample solution. This SPSS node provides a model for tree-based classification and prediction that is built on Classification and Regression Tree methodology. Due to its characteristics, this model is much less prostrate to overfitting, which gives a higher likelihood of repeating the identical test results when you employ fresh data, that is, data that was not portion of the original training and testing data sets. Another odds of this routine — particularly for their employ case — is its capacity to ply imbalanced data.

    Since in this employ case they are dealing with classification analysis, they used two common ways to evaluate the performance of the model: confusion matrix and ROC curve. One of the outputs of running the Random Trees model in SPSS is the confusion matrix seen in motif 8. The table shows the precision achieved by the model during training.

    Image title

    Figure 8: Confusion Matrix for canceled vs. non-canceled flights

    In this case, the model's precision was about 95% for predicting canceled flights (true positives), and about 94% for predicting non-canceled flights (true negatives). That means, the model was correct most of the time, but likewise made wrong predictions about 4-5% of the time (false negatives and unfounded positives).

    That was the precision given by the model using the training dataset. This is likewise represented by the ROC curve on the left side of motif 9. They can see, however, that the zone under the curve for the training data set was better than the zone under the curve for the testing dataset (right side of motif 9), which means that during testing, the model did not achieve as well as during training (i.e. it presented a higher rate of errors or higher rate of unfounded negatives and unfounded positives).

    Image title

    Figure 9: ROC curves for the training and testing data sets

    Nevertheless, they decided that the results were still estimable for the purposes of their discussion in this blog, and they stopped their iterations here. They inspirit readers to further refine this model or even to employ other models that could solve this employ case.

    Deploying the Model

    Finally, they deployed the model as a relaxation API that developers can convene from their applications. For that, they created a "deployment branch" in the SPSS stream. Then, they used the IBM Watson Machine Learning service available on IBM Bluemix here. They imported the SPSS stream into the Bluemix service, which generated a scoring endpoint (or URL) that application developers can call. Developers can likewise convene The Weather Company APIs directly from their application code to retrieve the forecast data for the next day, week, and so on, in order to pass the required data to the scoring endpoint and invent the prediction.

    A typical scoring endpoint provided by the Watson Machine Learning service would peer enjoy the URL shown below.

    https://ibm-watson-ml.mybluemix.net/pm/v1/score/flights-cancellation?accesskey=<provided by WML service>

    By passing the expected JSON corpse that includes the required inputs for scoring (such as the future flight data and forecast weather data), the scoring endpoint above returns if a given flight is likely to exist canceled or not. This is seen in motif 10, which shows a convene being made to the scoring endpoint — and its response — using an HTTP requester tool available in a web browser.

    Image title

    Figure 10: Actual request URL, JSON body, and response from scoring endpoint

    Notice in the JSON response above that the deployed model predicted this particular flight from Newark to Chicago would exist 88.8% likely to exist canceled, based on forecast weather conditions.

    Conclusion

    IBM SPSS Modeler is a powerful tool that helped us visually create a solution for this employ case without writing a single line of code. They were able to ensue an iterative process that helped us understand and prepare the data, then model and evaluate the solution to finally deploy the model as an API for consumption by application developers.

    Resources

    The IBM SPSS stream and data used as the basis for this blog are available on GitHub. There you can likewise find instructions on how to download IBM SPSS Modeler, snare a key for The Weather Channel APIs, and much more.


    The Week In Review: Design | killexams.com true questions and Pass4sure dumps

    M&AIoT-focused remembrance chipmaker Adesto Technologies acquired S3 Semiconductors, a provider of mixed-signal and RF ASICs and IP. Based in Ireland, S3 Semiconductors was founded in 1986. S3 Semiconductors will become a traffic unit of Adesto and will continue to operate under its current model in the $35 million deal. S3 Semiconductor’s parent company, S3 Group, will continue as a separate company focused on smart medical devices.

    ToolsCadence debuted a fresh design-for-reliability tool for analog and mixed-signal IC design. The Legato Reliability Solution incorporates analog defect analysis and simulation to evaluate the capacity to eradicate die with manufacturing defects and resulting test escapes that antecedent territory failures, as well as electro-thermal and aging analysis to obviate thermal overstress and foretell wear-out.

    Aldec made several updates to its Riviera-PRO verification platform, adding a UVM Register Generator for automated register model creation. The UVM Register Generator supports input of IP-XACT component files and CSV files. Other additions comprise a Unit Linting function that runs ALINT-PRO in the background and support for the latest extensions to VHDL.

    XJTAG uncorked free DFT software for users of Mentor’s Xpedition product to augment the design for test and debug capabilities of the schematic capture and PCB design environment. XJTAG DFT aide helps validate correct JTAG chain connectivity by finding and repairing errors.

    IPArasan Chip Systems uncorked its MIPI I3C Host Controller Interface (I3C HCI) Master IP core compliant to the recently released MIPI I3C HCI Specification 1.0. The IP kit includes a Linux software stack compatible with the I3C HCI Master IP Core, an I3C Hardware evolution Kit to enable prototyping and compliance testing of I3C products, and the I3C Slave IP Core.

    Chips&Media launched its first computer vision IP, which implements a convolutional neural network capable of processing 4K resolution at 60 frames per second input in real-time for remonstrate detection. The IP targets edge devices for automotive and surveillance applications.

    Chipus taped out a set of its ultra-low power analog IP on SilTerra’s 0.18um I18L IoT Platform. The test chip includes a temperature sensor, RTC oscillators, 6MHz oscillators, and an ultra-low power bandgap reference.

    StandardsThe Si2 Compact Model Coalition published the Open Model Interface, a C-language application programming interface that supports SPICE compact model extensions. OMI allows for simulation and analysis of physical effects such as self-heating and aging as well as design optimization. It is based on TMI2, the TSMC Model Interface, which was donated to Si2 by TSMC in 2014. So far, OMI supports BSIM, BSIM-CMG, HiSIM2, and BSIM-SOI.

    The MIPI Alliance released MIPI Touch, a family of publicly available specifications for the design and implementation of touch applications, such as in smartphones. The endeavor ties together several MIPI specifications for touch, sensor, and software integration: MIPI Touch Command Set (MIPI TCS) for writing device drivers across operating systems; MIPI Touch Adaptation Layer for I3C (MIPI ALI3C) to translate touch commands for employ on the MIPI I3C protocol; MIPI I3C Host Controller Interface (MIPI I3C HCI), which enables touch commands and multiple data streams for advanced systems; and MIPI I3C v1.0, which connects an application processor to a touch sensor.

    DealsRambus and GigaDevice formed a joint venture, Reliance Memory, in China to focus on evolution and commercialization of Resistive Ram (RRAM), a non-volatile remembrance that works by changing the resistance of materials when different voltages are applied. The joint venture has investment funding from THG Ventures, West Summit Capital, Walden International, and Zhisland Capital.

    Esperanto Technologies inked deals for both NetSpeed Systems’ network-on-chip IP and design environment and UltraSoC’s embedded analytics and debug IP for its Supercomputer-on-a-Chip for AI applications. The 7nm chip will integrate thousands of RISC-V compatible processors each with vector/tensor accelerators.

    NetSpeed Systems and Northwest Logic are teaming up on a high throughput remembrance subsystem solution that uses NetSpeed’s interconnect IP and Northwest Logic’s HBM2 and GDDR6 remembrance controllers for customers designing SoCs for hyperscale and automotive applications. The companies will provide a common platform to extend NetSpeed’s dynamic-priority height approach to the remembrance controller so that system-level traffic-flow priorities are handled in an end-to-end solution.

    NumbersIntel Capital placed investments in two firms: Movellus and SiFive. Movellus provides generator tools enabling automatic creation and implementation of PLL, DLL, and LDO digital IP and will employ the funds to accelerate market deployment and adoption of its technology. SiFive is a provider of RISC-V IP cores, SoCs, and evolution boards, as well a RISC-V-focused IP catalog.

    Rambus released pecuniary results for first quarter 2018 with revenue of $46.4 million. On a GAAP basis, there was a loss per share of $0.36; on a non-GAAP basis there was a loss per share of $0.10. Rambus changed to a fresh reporting standard this year; under the previous reporting standard, revenue for Q1 2018 was $100.5 million, up 4% from the identical quarter ultimate year. GAAP income per share for the quarter was $0.05, up 66.7% from $0.03 in Q1 2017; non-GAAP income per share was $0.21, up 23.5% from $0.17. The company posted record revenue for its remembrance and interface IP cores.

    Jesse Allen   (all posts)Jesse Allen is the knowledge seat administrator and a senior editor at Semiconductor Engineering.

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