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HP0-781 NonStop Performance Analysis and Tuning

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HP0-781 exam Dumps Source : NonStop Performance Analysis and Tuning

Test Code : HP0-781
Test name : NonStop Performance Analysis and Tuning
Vendor name : HP
exam questions : 154 true Questions

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HP NonStop Performance Analysis and

TIC On the circulate – provides HPE NonStop efficiency professional to team | killexams.com true Questions and Pass4sure dumps

(PRLEAP.COM) July 10, 2018 (extraordinary Neck, sizable apple) – TIC software, the main company of application solutions and consulting functions for HPE NonStop modernization, is joyous to broadcast the addition of Matt Riesz to their employ as a client consultant. Mr. Riesz is a 35-12 months veteran of Tandem/Compaq/HP/HPE and brings to stand sizable journey and abilities in the domain of efficiency analytics to aid TIC consumers. He'll drudgery with TIC's NonStop customers, keeping their systems operating at peak efficiency and productiveness levels.

Mr. Riesz's profession spans the gamut of company transitions within what's now referred to as HPE NonStop. employed by means of Tandem computers in 1982 as a methods engineer, his career advanced as the company morphed from Tandem to Compaq, and finally to HP/HPE, the station he retired as a master programs architect. Matt's focus of attention has been on pre-and submit-earnings technical uphold for utility and techniques. His learning in rig structure, analytics and efficiency can subsist leveraged to serve TIC consumers realize most liable productivity from their NonStop methods.

Mr. Riesz might subsist providing operations usher for TIC clients via efficiency analysis, in addition to integrating original applied sciences into the enterprise's ongoing NonStop modernization efforts. Some of those original technologies consist of:

  • Amazon web capabilities (AWS) – presenting subscription-in accordance with-demand cloud computing
  • excellent NonStop GUI – giving NonStop users helpful access from a common windows interface
  • Blockchain know-how – dispensed digital facts – shared & reconciled across the cyber web
  • "we are excited so as to add an experienced expert of Matt's caliber to their group," defined Phil Ly, President, TIC software. "Matt's colossal event with NonStop shoppers within the container, and his gadget analysis and architecture potential and abilities, will uphold their consumers multiply their IT operations through more suitable performance and productivity. They appear to subsist ahead to offering Matt's learning to sum their NonStop modernization valued clientele'!"

    About TIC software:TIC software become created to tackle the challenges of conserving Tandem/HP NonStop legacy techniques present with ever-evolving "modernization" technology – providing consulting expertise and application options designed to preserve mission-vital purposes up-and-operating. today, they continue to fulfill these modernization challenges, in addition to present migration options to corporations trying to explore different technological options. consult with www.ticsoftware.com for more.

    TIC software – NonStop Innovation

    MEDIA CONTACT: Ryan Ly, TIC application, C: 310-409-5082


  • HP Inc. (NYSE:HPQ) Earns among the many most beneficial Returns In Its trade | killexams.com true Questions and Pass4sure dumps

    want to hold section in a brief analysis analyze? assist profile the future of investing rig and furthermore you may win a $250 gift card!

    today we're going to glimpse at HP Inc. (NYSE:HPQ) to contemplate whether it might possibly subsist a gorgeous funding prospect. certainly, we’re going to compute its return On Capital Employed (ROCE), within the hopes of getting some perception into the business.

    First, we’ll proceed over how they compute ROCE. subsequent, we’ll examine it to others in its business. eventually, we’ll study how its existing liabilities strike its ROCE.

    Return On Capital Employed (ROCE): what's it?

    ROCE measures the ‘return’ (pre-tax income) an organization generates from capital employed in its business. sum else being equal, an improved company can gain a better ROCE. In short, it is a worthy tool, however it isn't devoid of drawbacks. well-known investment researcher Michael Mauboussin has cautioned that a elevated ROCE can point out that ‘one greenback invested in the enterprise generates cost of multiple dollar’.

    How conclude You compute return On Capital Employed?

    The formulation for calculating the return on capital employed is:

    Return on Capital Employed = income before pastime and Tax (EBIT) ÷ (total property – latest Liabilities)

    Or for HP:

    0.46 = US$4.3b ÷ (US$35b – US$25b) (in response to the trailing twelve months to October 2018.)

    So, HP has an ROCE of 46%.

    take a glimpse at their newest analysis for HP

    Does HP gain a superb ROCE?

    a technique to assess ROCE is to examine similar agencies. HP’s ROCE seems to subsist extensively more advantageous than the 12% common in the Tech business. they might account this a favorable, as it suggests it's the usage of capital extra without rigor than other equivalent companies. putting aside its station relative to its traffic for now, in absolute terms, HP’s ROCE is currently very first rate.

    As they can see, HP at present has an ROCE of forty six% compared to its ROCE three years ago, which changed into 6.1%. This makes us believe the company might possibly subsist enhancing.

    NYSE:HPQ closing Perf February eighth 19

    greater

    When given that ROCE, withhold in reason that it reflects the previous and doesn't necessarily call the longer term. ROCE can subsist deceptive for cyclical groups, as returns can appear brilliant in boom times, and terribly low in downturns. ROCE is only some extent-in-time measure. Future performance is what concerns, and you can contemplate analyst predictions in their free record on analyst forecasts for the business.

    Do HP’s latest Liabilities Skew Its ROCE?

    brief time era (or existing) liabilities, are issues enjoy enterprise invoices, overdrafts, or tax bills that necessity to subsist paid within twelve months. due to the style ROCE is calculated, a elevated plane of current liabilities makes a corporation appear to subsist as although it has much less capital employed, and for this reason can (once in a while unfairly) raise the ROCE. To counteract this, they investigate if an organization has inordinate present liabilities, relative to its complete assets.

    HP has total liabilities of US$25b and total belongings of US$35b. hence its present liabilities are comparable to approximately 73% of its total assets. HP’s inordinate degree of present liabilities raise the ROCE – but its ROCE continues to subsist astonishing.

    Our hold on HP’s ROCE

    So they might subsist attracted to doing greater analysis here — there can subsist a chance! Of direction you can subsist able to find a higher stock than HP. so that you may furthermore wish to contemplate this free assortment of different groups which gain grown income strongly.

    Story continues

    for those that want to locate successful investments this free listing of turning out to subsist companies with recent insider paying for, may subsist simply the ticket.

    To assist readers contemplate past the short term volatility of the fiscal market, they aim to carry you a long-time era concentrated research evaluation in simple terms driven by means of simple statistics. subsist sensible that their analysis does not aspect in the newest fee-sensitive enterprise bulletins.

    The creator is an impartial contributor and at the time of booklet had no station within the shares mentioned. For mistake that warrant correction please contact the editor at editorial-team@simplywallst.com.


    world statistics focus Networking Market 2019 – trade evaluation, dimension, Share, ideas and Forecast To 2025 | killexams.com true Questions and Pass4sure dumps

    Feb 13, 2019 (MarketersMedia via COMTEX) -- original anatomize On "2019-2025 statistics middle Networking Market global Key participant, Demand, boom, alternatives and evaluation Forecast" brought to astute man reports Database

    Pune , India - February 13, 2019 /MarketersMedia/ --

    global data core Networking business

    New examine On "2019-2025 statistics middle Networking Market global Key participant, Demand, growth, alternatives and analysis Forecast" delivered to brilliant guy stories Database

    The information focus networking companies are specializing in providing premiere-in-class dynamic network infrastructure that can uphold to consolidate purposes and servers and virtualizes its system resources together with improving classic efficiency, availability, and power effectivity, thereby proposing a greater bendy and dynamic assistance expertise (IT) infrastructure. modern facts core networking infrastructure acknowledge is in response to open requisites. It optimizes efficiency and enables consolidation which in circle raises community scalability and resiliency, simplifies operations and streamlines administration along with decreasing the overall cost of possession (TCO). This solution furthermore automates the network infrastructure administration, making latest infrastructure with ease adaptable and flexible, particularly for cloud service suppliers, for third celebration software deployments.In 2018, the global records focus Networking market dimension became xx million US$ and it is anticipated to attain xx million US$ by using the halt of 2025, with a CAGR of 10.eight% birthright through 2019-2025.

    This document focuses on the world information middle Networking reputation, future forecast, boom probability, key market and key players. The anatomize pursuits are to existing the records core Networking evolution in united states, Europe and China.

    the key avid gamers lined in this studyAlcatel-LucentBrocadeCiscoDellEMCExtreme NetworksEquinixFujitsuHewlett Packard (HP)Hitachi facts SystemsIBMIntelJuniper NetworksMicrosoftNECVmware

    are trying sample record @ https://www.wiseguyreports.com/sample-request/3665388-global-statistics-middle-networking-market-size-popularity-and-forecast-2019-2025

    Market segment by using classification, the product can furthermore subsist split intoType IType II

    Market segment through software, break up intoEnterprisesCloud provider ProvidersTelecommunication carrier suppliers

    Market angle by regions/countries, this report coversUnited StatesEuropeChinaJapanSoutheast AsiaIndiaCentral & South america

    The examine goals of this report are:to research world facts middle Networking popularity, future forecast, multiply opportunity, key market and key avid gamers.To current the facts focus Networking construction in united states, Europe and China.To strategically profile the necessary thing avid gamers and comprehensively anatomize their evolution artery and strategies.To outline, limn and forecast the market via product type, market and key regions.

    For particular analyzing please talk over with WiseGuy reviews @ https://www.wiseguyreports.com/studies/3665388-global-facts-core-networking-market-size-popularity-and-forecast-2019-2025Some essential aspects from table of content:

    1 record Overview1.1 anatomize Scope1.2 Key Market Segments1.three players Covered1.4 Market analysis by artery of Type1.four.1 global data focus Networking Market size boom cost by using classification (2014-2025)1.four.2 class I1.four.3 type II1.5 Market via Application1.5.1 global statistics middle Networking Market partake through application (2014-2025)1.5.2 Enterprises1.5.three Cloud carrier Providers1.5.four Telecommunication carrier Providers1.6 anatomize Objectives1.7 Years considered

    12 overseas gamers Profiles12.1 Alcatel-Lucent12.1.1 Alcatel-Lucent enterprise Details12.1.2 traffic Description and traffic Overview12.1.three information middle Networking Introduction12.1.4 Alcatel-Lucent salary in statistics focus Networking company (2014-2019)12.1.5 Alcatel-Lucent fresh Development12.2 Brocade12.2.1 Brocade enterprise Details12.2.2 traffic Description and enterprise Overview12.2.three facts middle Networking Introduction12.2.4 Brocade income in statistics middle Networking traffic (2014-2019)12.2.5 Brocade fresh Development12.3 Cisco12.3.1 Cisco traffic Details12.3.2 traffic Description and enterprise Overview12.3.3 facts focus Networking Introduction12.three.4 Cisco salary in records core Networking enterprise (2014-2019)12.3.5 Cisco fresh Development12.four Dell12.four.1 Dell enterprise Details12.four.2 enterprise Description and traffic Overview12.four.three information focus Networking Introduction12.4.four Dell salary in information focus Networking enterprise (2014-2019)12.4.5 Dell simultaneous Development12.5 EMC12.5.1 EMC traffic Details12.5.2 company Description and traffic Overview12.5.3 records core Networking Introduction12.5.four EMC revenue in facts core Networking traffic (2014-2019)12.5.5 EMC recent Development12.6 extreme Networks12.6.1 fierce Networks traffic Details12.6.2 company Description and traffic Overview12.6.3 records focus Networking Introduction12.6.four extreme Networks revenue in records core Networking enterprise (2014-2019)12.6.5 extreme Networks recent Development12.7 Equinix12.7.1 Equinix traffic Details12.7.2 enterprise Description and company Overview12.7.3 data focus Networking Introduction12.7.4 Equinix salary in data middle Networking enterprise (2014-2019)12.7.5 Equinix fresh Development12.eight Fujitsu12.8.1 Fujitsu traffic Details12.eight.2 company Description and enterprise Overview12.8.three facts core Networking Introduction12.eight.4 Fujitsu revenue in records core Networking traffic (2014-2019)12.8.5 Fujitsu recent Development12.9 Hewlett Packard (HP)12.9.1 Hewlett Packard (HP) company Details12.9.2 company Description and traffic Overview12.9.three records focus Networking Introduction12.9.4 Hewlett Packard (HP) revenue in statistics core Networking enterprise (2014-2019)12.9.5 Hewlett Packard (HP) fresh Development12.10 Hitachi records Systems12.10.1 Hitachi statistics methods company Details12.10.2 company Description and enterprise Overview12.10.three information middle Networking Introduction12.10.4 Hitachi data systems earnings in data middle Networking enterprise (2014-2019)12.10.5 Hitachi information programs simultaneous Development12.11 IBM12.12 Intel12.13 Juniper Networks12.14 Microsoft12.15 NEC12.16 Vmware

    continued....

    For extra counsel or any query mail at earnings@wiseguyreports.com

    About UsWise guy studies is a section of the smart guy Consultants Pvt. Ltd. and offers premium innovative statistical surveying, market research stories, analysis & forecast records for industries and governments everywhere. smart man studies hold into account how basic statistical surveying suggestions is in your company or affiliation. therefore, we've associated with the properly publishers and analysis establishments sum really expert in particular domains, making unavoidable you will receive the gold standard and up to this point analysis records attainable.

    Contact Us:Norah Trent+1 646 845 9349 / +forty four 208 133 9349Follow on LinkedIn: https://www.linkedin.com/business/clever-guy-analysis-consultants-pvt-ltd-?trk=biz-corporations-cym

    Contact data:identify: Norah TrentEmail: forward EmailOrganization: WISEGUY research CONSULTANTS PVT LTDAddress: workplace No. 528, Amanora Chambers, Pune - 411028, Maharashtra, IndiaPhone: 8411985042Website: https://www.wiseguyreports.com/pattern-request/3665388-global-statistics-core-networking-market-dimension-popularity-and-forecast-2019-2025

    source URL: https://marketersmedia.com/world-statistics-core-networking-market-2019-industry-evaluation-measurement-share-strategies-and-forecast-to-2025/482026

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    free up id: 482026


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    NonStop Performance Analysis and Tuning

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    SRE: Performance Analysis: Tuning Methodology Using a Simple HTTP Webserver In proceed | killexams.com true questions and Pass4sure dumps

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    Nov 19, 2018

    Performance analysis and tuning is a powerful technique to verify if client focused performance requirements are being met. Performance analysis can subsist used to characterize where a program is spending its time and supports a scientific approach to testing the efficacy of tuning experiments. This post defines a universal approach for performance analysis and tuning using a proceed HTTP service as an example. proceed is particularly well suited for performance analysis and tuning because it provides pprof profiling utensil chain in its standard library.

    Strategy

    Let’s try to establish a loose framework to launch a structured analysis of their program. The only thing they will try and conclude will subsist to employ data to usher their decisions instead of making changes based on intuition or hunches. In order to conclude this they will:

  • Establish which dimensions we’re optimizing for (Requirements)
  • Create a harness to apply transactional load to the system
  • Execute a Test— (Generate data)
  • Observe
  • Analyze — Are the requirements met?
  • Tune — Scientific Method — Form A Hypothesis
  • Execute experiment to test this hypothesis
  • Simple HTTP Server Architecture

    For this blog post we’ll subsist using a small HTTP golang server. sum code for this post can subsist organize at: https://github.com/dm03514/sre-tutorials/tree/master/performance/analysis-methodology-simple-http

    The application that will subsist analyzed is a proceed HTTP server that queries PostgreSQL on each request. Additionally, there is Prometheus, node_exporter, and Grafana in order to collect and visualize application and system plane metrics:

    For simplicity this post assumes that in order to scale horizontally (and simplify their calculations) each HTTP service and Postgres Database will subsist deployed together:

    Determine Goals (Dimensions)

    This step outlines a specific goal. What are they trying to analyze? How conclude they know when their efforts are complete?

    For this post we’ll affect that they gain clients and that on mediocre their service will subsist called 10000 requests / second.

    The Google SRE bespeak goes into a lot of depth on how to pick and model these. In the spirit of SRE we’ll model ours as:

  • Latency — 99% of requests should complete in < 60ms
  • Cost— The service should employ the minimal amount of money that they believe is reasonably possible. In order to conclude this throughput should subsist maximized.
  • Capacity Planning — An understanding of how many instances will subsist required for launch and the universal scaling function should subsist documented. What’s the number of instances we’ll necessity to meet their expected initial load requirements and achieve n+1 redundancy?
  • Latency may subsist require an optimization in addition to analysis but throughput is an analysis. Using SRE SLO process the latency requirement would approach from the client and or traffic as represented by the product owner. What’s really collected about this is that their service may subsist able to meet this commitment from the outset without any required tuning!

    Setup Test Harness

    The test harness will apply a fixed amount load to their system. In order to anatomize the performance of the HTTP service data will gain to subsist generated.

    Transactional Load

    This harness just uses Vegeta to acquire HTTP requests at a configurable rate until stopped:

    $ acquire load-test LOAD_TEST_RATE=50echo "POST http://localhost:8080" | vegeta storm -body tests/fixtures/age_no_match.json -rate=50 -duration=0 | tee results.bin | vegeta report Observe

    During execution an unending transaction load will subsist applied (load test). In addition to application (requests rate, request latencies) and system plane metrics (memory, CPU, IOPS) this time the application will subsist profiled in order to understand where it is spending its time.

    Profiling

    Profiling is a type of measurement and allow us to to contemplate where the program is spending its time. It reports where a program is spending its time. Profiling can subsist used to identify which functions are being called and how much time the program is spending in each one:

    This data can subsist used during analysis to gain visibility into unnecessary drudgery that the program is doing. proceed (pprof) is able to generate profiles, and visualize them as a flame graph, using the standard toolchain. We’ll cover using these to usher tuning decisions later in this post.

    Execute/Observe/Analyze

    Let’s launch executing experiments. We’ll execute, keep and anatomize until they their performance requirements gain been invalidated. We’ll pick an arbitrary low amount of load to apply in order to generate the first observations and analysis. Each step that their performance requirements hold genuine we’ll multiply the load by some random-ish scaling factor. Each of the load tests are executed by adjustng the rate of:make load-test LOAD_TEST_RATE=X.

    50 requests / second

    Notice the top two graphs. The upper left hand shows us that their application is registering that it is processing 50 requests per second (from its point of view) and the upper birthright reporting the duration of each HTTP request. These together serve us keep and anatomize if their performance requirements are being met. The red line on the HTTP Request Latency is at the SLO of 60 ms. This shows that they are well below it for their max response times.

    On the cost front:

    10k requests / second / 50 requests / machine = 200 machines + 1

    we can quiet conclude much better.

    500 Requests / second

    Things start to acquire more keen when 500 requests per second are applied:

    Once again the upper left hand graph shows that the application is registering the intended load. If it weren’t it might identify an issue in the host machine between the load test command and the application server. The latency graph in the upper birthright graph shows that applying 500 requests / second forward the tail up to 25–40ms per HTTP requests. The p99 is quiet well within the 60ms SLO that was specified above.

    On the cost front:

    10k requests / second / 500 requests / machine = 20 machines + 1

    we can quiet conclude much better!

    1000 requests / second

    This is a worthy one! The program is logging 1000 requests / second, but on the latency side the SLO has been violated. This can subsist seen by the P99 line in the upper left hand graph. While the tail p100 max is much higher p99 tracks above the 60ms maximum. It’s time to dive in and profile what the application is actually doing.

    Profile

    In order to profile we’ll apply 1000 request per second load and then employ pprof to sample the stacks in order to acquire an thought of where their program is spending its time. This is done by enabling the pprof HTTP endpoint and curling a track while load is being applied:

    $ curl http://localhost:8080/debug/pprof/profile?seconds=29 > cpu.1000_reqs_sec_no_optimizations.prof

    The track can then subsist visualized using:

    $ proceed utensil pprof -http=:12345 cpu.1000_reqs_sec_no_optimizations.prof

    The flame graph shows where the application is spending its time and how much time time is being spent! From Brendan Gregg’s description:

    The x-axis shows the stack profile population, sorted alphabetically (it is not the passage of time), and the y-axis shows stack depth, counting from zero at the [top]. Each rectangle represents a stack frame. The wider a frame is is, the more often it was present in the stacks. The top edge shows what is on-CPU, and beneath it is its [children]. The colors are usually not significant, picked randomly to differentiate frames.

    Analysis — Hypothesis

    In order to usher tuning we’ll focus on trying to find unnecessary work. We’ll try and find the largest source of unnecessary drudgery and remove it. Since profiling exposes where exactly the service is spending its time it requires identifying potentially duplicate work, modifying the code to change it, re running the test and observing if the performance is closer to its goal.

    Following Bredan Gregg’s description the proceed pprof flame graph is read from top to bottom. Each row represents a stack frame (function call). The first row is the entry point to the program and is the parent of sum other calls (ie sum other calls gain the first line in their stack). The next row diverges from this:

    Hovering over the function name in the flame graph shows the total amount of time it was on the stack during tracing. HTTPServe was on the stack for 65% of the profiles while various proceed runtime methods runtime.mcall , mstart, gc compose the ease of this. One keen thing is that 5% of the program is being spent in DNS Lookups:

    The only addresses that the program should subsist resolving are for Postgres. Clicking on FindByAge shows:

    This is keen it shows roughly 3 main sources which add to the latency: Closing/Releasing Connections, Querying Data, and Connecting.Based on this flame graph DNS lookups and connection closing/opening amounts for ~13% of the total service time.

    Hypothesis: Reusing connections through pooling will reduce HTTP transaction time allow for higher throughput and lower latency.

    Tune the application — Experiment

    The program will subsist updated to try and remove the postgres connection on every request. One artery to conclude this is to employ an application plane connection pool. This experiment will configure a connection pool using the proceed sql driver pool configuration options:

    db, err := sql.Open("postgres", dbConnectionString)db.SetMaxOpenConns(8)

    if err != nil {return nil, err}

    Execute/Observe/Analyze

    Re-Runing the 1000 test load test shows that the P99 HTTP Request Latency is well within the 60ms SLO!

    But on the cost front:

    10k requests / second / 1000 requests / machine = 10 machines + 1

    Let’s try and conclude better!

    2000 Requests / Second

    Doubling the requests betoken the same. Once again the upper left hand graph shows that the application is receiving 2000 requests per second, and that while the p100 max client latency is above 60ms, the p99 is quiet within the SLO.

    On the cost side:

    10k requests / second / 2000 requests / machine = 5 machines + 1

    3000 Requests / second

    At this point the service is able to achieve a rate of 3000 requests / second with < 60ms p99 latency, with the p100 rising significantly from 100–250ms to 250ms-1000ms between this dash and the 2000 requests / second run. While the SLO isn’t being violated and costs are acceptable with:

    10k requests / second / 3000 requests / machine = 4 machines + 1

    Let’s try one more round of analysis.

    Analysis — Hypothesis

    Generating and visualizing a profile of the application with 3000 requests / second load being applied shows:

    There is quiet 6% of the FindByAge transaction length due to Dialing Connections!! Establishing a connection pool has increased performance but its quiet observed that the program is quiet doing repeated drudgery by continuing to acquire original DB connections!

    Hypothesis: Even though connections are being pooled they are quiet being recycled and cleaned up resulting in the application having to reconnect. Adjusting the number of connections idle to equal the pool size should serve with latency by further minimizing the amount of time the application spends in creating database connections.

    Tune the application — Experiment

    Let’s try and set the MaxIdleConns equal to the pool size (also described here):

    db, err := sql.Open("postgres", dbConnectionString)db.SetMaxOpenConns(8)db.SetMaxIdleConns(8) if err != nil {return nil, err} Execute/Observe/Analyze

    3000 requests / second

    p99 is well below the <60ms! @ 3000 requests per second with a lower p100!

    Looking closely at the flame graph below now shows that the connection dial has no longer shows up! Looking closely at pg(*conn).query a total leg of dialing is no longer there.

    Conclusion

    Performance analysis is captious for understanding if client expectations and non-functional requirements are being met. Analyzing performance by qualifying observations against client expectations can serve in determining what is acceptable performance vs what is unacceptable. proceed provides wonderful utilities baked into the standard library to acquire this sort of analysis effortless and accessible.

    I treasure you reading this and would esteem feedback!


    Achieving the Best QoE: Performance Libraries Accelerate Code Execution | killexams.com true questions and Pass4sure dumps

    The increasing consumerization of IT means that even staid traffic applications enjoy accounting necessity to gain the performance and ease of employ of current consumer apps. For many organizations, performance has taken a back burner to functionality. Fortunately, developers now gain access to a powerful group of libraries that can instantly multiply application performance – with tiny or no rewriting of older code. You can download them and start benefitting from this acceleration today.

    Here’s a quick rundown of these Intel-provided libraries and how to acquire them. They’re offered at no cost as section of Intel’s mission to uphold innovation and impressive performance on Intel® architecture.

    Intel® Math Kernel Library (Intel® MKL) This current library offers optimized, threaded, and vectorized functions to maximize performance on each processor family. It works with your election of compilers, languages, operating systems, and linking and threading models to hurry applications and slice evolution time. You’ll find algebra, FFTs, vector statistics & data fitting, vector math, and other motley solvers, and since it uses industry-standard C and Fortran APIs for compatibility with current BLAS, LAPACK, and FFTW functions, no code changes are required.

    Intel® Integrated Performance Primitives (Intel® IPP) If you’re working with images, signals, or cryptography, this library is a must, speeding performance for imaging, vision, signal, security, and storage applications on multiple OSs and platforms. Intel® IPP is a one-stop shop for ready-to-use, royalty-free APIs that drudgery in multicore, multiple OS, and multiplatform environments where computation hurry and optimization can acquire the incompatibility between project success and failure. Optimized using Intel® Streaming SIMD Extensions, Intel® Advanced Vector Extensions 2, and Intel® Advanced Vector Extensions 512, employ IPP to gain your applications achieve faster than optimized compilers alone.

    Intel® Threading edifice Blocks (Intel® TBB) Want to acquire the most out of every core and processor? This widely used C++ library is designed for shared-memory parallel programming and heterogeneous computing, offering advanced threading for fast, scalable parallel applications. TBB simplifies code by letting you specify rational parallelism instead of threads, by targeting threading for performance, and it’s compatible with other threading packages so you can leave your legacy code lonely while using TBB for original implementations.

    Intel® Data Analytics Acceleration Library (Intel® DAAL) sizable data, machine learning, and data science sum gain one thing in common: a necessity for reliable, snappy analytics whether for traffic or scientific coding. This easy-to-use performance library features highly tuned functions for analytics performance across the spectrum of Intel® architecture devices. It helps apps deliver better predictions, faster. DAAL lets you anatomize larger data sets with the very compute resources, and it optimizes data ingestion and algorithmic compute together for the highest performance achievable during sum stages of the data analytics pipeline: preprocessing, transformation, analysis, modeling, validation, and decision-making.

    Intel® MPI Library If you gain applications running on Intel® architecture-based clusters, this library will better performance by delivering flexible, efficient, and scalable cluster messaging. It is a multi-fabric message-passing library that implements the open-source MPICH spec. employ it to create, maintain, and test advanced, tangled applications that achieve better on HPC clusters based on Intel® processors. The MPI library delivers maximum end-user performance without having to change the software or operating environment by achieving the best latency, bandwidth, and scalability through automatic tuning for the latest Intel® platforms.

    All of these libraries are available as section of the Intel® Parallel Studio XE and Intel® System Studio utensil suites, and they’re sum free to use.


    Monitoring and Tuning Analysis Services with SQL Profiler, section 1 | killexams.com true questions and Pass4sure dumps

    Database administration expert Baya Dewald explores the possibilities of using the SQL Profiler framework to find common performance problems in your SQL Server Analysis Services (SSAS) cubes. In section 1 of this two-part series, he discusses the various events and columns that SQL Profiler supports for SSAS.

    Like this article? They recommend 

    SQL Server Analysis Services (SSAS) is an enterprise-class data analytics and aggregation engine that offers much flexibility and ease for developing traffic intelligence applications. edifice a small, picayune cube isn't complicated, but when SSAS cubes grow to a reasonable size and complexity, querying and processing performances often don't live up to customers' expectations. Fortunately, Microsoft offers a few tools for looking under the hood and detecting the root reason of issues. SQL Profiler is arguably the most informative utensil for monitoring and troubleshooting Analysis Services (the utensil works with Analysis Services 2005 and later versions).

    In this article, I argue the SQL Profiler framework for identifying most commonly encountered problems. After you're intimate with Profiler events and columns, section 2 of this train examines how to employ Profiler events to detect and resolve querying, processing, and synchronization performance issues. I'll furthermore argue using Profiler for load testing to ensure that your application is ready for production use.

    SQL Profiler supports a legion of Analysis Services events, and you can collect a number of columns for each event. Collecting every event and every column on a busy server is impractical because the resulting track would grow rapidly to unmanageable size and subsist difficult to decipher. For occasional troubleshooting, they necessity to focus on a few events that are germane for the chore at hand. For ongoing monitoring, it's useful to collect a larger set of events and columns for historical analysis, which I'll argue in section 2 of this series.

    Table 1 quickly summarizes the Profiler events they can capture. Study this table to approach up with a list of events that would subsist helpful for your monitoring and tuning effort. please note that Microsoft tends to add Profiler events with each version, so the list available in your environment might differ slightly from the one in Table 1, depending on the Analysis Services version you're using.

    Table 1

    Analysis Services Profiler Events

    Event Category

    Event

    Description/Example

    Command Events

    Command Begin

    Command End

    Captures the following commands:

  • [lb] Data-definition language commands, including create, alter, delete, merge
  • [lb] Backup and restore commands: backup, restore, synchronize, detach, attach
  • [lb] sum processing commands
  • Discover Events

    Discover Begin

    Discover End

    Captures retrieval of metadata. For example, a determine command is executed when a user connects to SSAS by using SQL Server Management Studio and expands folders of databases, cubes, and measure groups; checks partition properties.

    Discover Server state Events

    Server state determine Begin

    Server state determine Data

    Server state determine End

    SSAS occasionally checks its own health through these events by taking a snapshot of server state. This is done by submitting determine connections, determine sessions, determine jobs, and other commands. Frequency of execution of these commands is controlled by the <FlightRecorder><SnapshotFrequencySec> setting in the msmdsrv.ini configuration file. The commands to subsist executed are included in the FlightRecorderSnapshotDef.xml file organize in the bin folder of the SSAS installation directory.

    Errors and Warnings

    Error

    The mistake number reported by this event can subsist helpful when troubleshooting an issue with assistance from Microsoft support.

    Locks

    Deadlock

    Lock Timeout

    SSAS is predominantly a read-only system, but they write data when creating files through processing, synchronization, and other means. A deadlock normally occurs when two commands are attempting to modify the very object. Lock timeout can subsist encountered if a long-running query blocks a processing or synchronization command, or vice versa.

    Notification Events

    Notification

    Records creation, modification, or deletion of an object. For example, as soon as you start a Profiler trace, it will capture a notification event recording its own creation.

    Progress Reports

    Progress Report Begin

    Progress Report Current

    Progress Report End

    Progress Report Error

    Records the current step of processing or querying a job's execution. For example, Progress Report Begin/End events are raised when they read a specific partition. Progress Report mistake is raised when a user cancels a long-running query, or processing fails due to missing-key errors.

    Queries Events

    Query Begin

    Query End

    These events are useful for checking MDX or DMX query start time and total duration.

    Query Processing

    Calculate Non-Empty Begin

    Calculate Non-Empty Current

    Calculate Non-Empty End

    Execute MDX Script Begin

    Execute MDX Script Current

    Execute MDX Script End

    Get Data from Aggregation

    Get Data from Cache

    Query Cube Begin

    Query Cube End

    Query Dimension

    Query Subcube

    Query Subcube Verbose

    Serialize Results Begin

    Serialize Results Current

    Serialize Results End

    The most informative category for comprehending how the query is resolved.

    Query Subcube event shows a vector of dimension attributes that SSAS reads to resolve the query. This is the very train of ones and zeros that you find in the query log. Each query could read multiple subcubes to derive the requested result.

    Query Subcube Verbose event returns particular information about the ascribe values being retrieved.

    Serialize Results * events betoken that the requested cell-set is being displayed to the user who submitted the query.

    Security Audit

    Audit Backup/Restore Event

    Audit Login

    Audit Logout

    Audit object consent Event

    Audit Server Starts and Stops

    Auditing various types of activity on the server. Primarily useful for long-term monitoring.

    Session Events

    Existing Connection

    Existing Session

    Session Initialize

    Displays existing connections and sessions, as well as requests for original sessions. This category is useful when you first start a track with the Profiler user interface, to gauge how busy your server is.

    From Table 1, they can surmise that progress reports, command events, query events, and query processing are the most useful categories for detecting query and processing performance issues. Other categories approach in handy for troubleshooting, though, so don't discount them yet!



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