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250-700 Implementation of HA Solutions for Windows using VCS 4.1

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250-700 exam Dumps Source : Implementation of HA Solutions for Windows using VCS 4.1

Test Code : 250-700
Test designation : Implementation of HA Solutions for Windows using VCS 4.1
Vendor designation : Symantec
: 73 existent Questions

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Symantec Symantec Implementation of HA

Lazarus 'FASTCash' financial institution Hackers Wield AIX Trojan | killexams.com existent Questions and Pass4sure dumps

Anti-Malware , ATM Fraud , Cybercrime as-a-service

Hackers occupy odds of out of date Unix to set up money-Out Malware, Symantec reports Mathew J. Schwartz (euroinfosec) • November 12, 2018     Lazarus 'FASTCash' Bank Hackers Wield AIX Trojan FASTCash assaults in opposition t banks had been tied to tens of millions of greenbacks in losses.

security researchers drawl they've identified an famous piece of the ATM money-out assail confound connected with the so-referred to as FASTCash assaults perpetrated via North Korean hackers.

See also: reside Webinar | Fraud Prevention for Banks: profitable 10 Tech requirements to evaluate

The U.S. executive says the FASTCash assaults are the toil of the Lazarus hacking community, tied to the Pyongyang-based mostly government of North Korea. Authorities drawl that considering that 2016, the attacks hold enabled hackers - and their money mules - to bare tens of hundreds of thousands of bucks in money from ATMs in Africa and Asia.

Now, security researchers at Symantec drawl they've recovered a under no circumstances-earlier than-considered Trojan used within the attacks, which Lazarus operators drop onto compromised bank networks.

"To shape the fraudulent withdrawals, Lazarus first breaches focused banks' networks and compromises the change utility servers handling ATM transactions," the Symantec researchers drawl in a Thursday weblog publish.

"as soon as these servers are compromised, previously unknown malware - Trojan.Fastcash - is deployed," they are saying. "This malware in flip intercepts fraudulent Lazarus cash withdrawal requests and sends fake approval responses, permitting the attackers to swipe cash from ATMs."

FASTCash Alert

In October, the U.S. computing device Emergency Readiness crew issued an alert about "malicious cyber recreation with the aid of the North Korean govt" - which it refers to as Hidden Cobra - perpetrating an ATM money-out scheme, which the U.S. executive refers to as "FASTCash."

US-CERT's alert - collectively issued with the department of homeland security, Treasury department and FBI - notes that for the reason that 2016, the assail push has been focused on institutions in Asia and Africa with malware designed to "remotely compromise cost change software servers inside banks to facilitate fraudulent transactions."

Authorities drawl the attacks hold resulted in tens of tens of millions of greenbacks in suspected losses. One 2017 assail by myself resulted in attackers simultaneously hitting ATMs in additional than 30 countries, whereas a 2018 assault hit ATMs in 23 nations, the alert talked about.

"The preparatory infection vector used to compromise sufferer networks is unknown; although, analysts surmise Hidden Cobra actors used spear-phishing emails in focused assaults against bank personnel," US-CERT spoke of in its alert. "Hidden Cobra actors seemingly used windows-based malware to learn a financial institution's network to determine the payment change application server."

ATM cash-Out attacks

Symantec says that it live recovered multiple models of the Fastcash Trojan, every of which seems to had been personalized for distinctive transaction processing networks. The samples additionally tie to trustworthy primary account numbers, or PANs - the 14 or sixteen-digit numerical strings discovered on financial institution and credit score playing cards that identify a card company and account number.

US-CERT said in its alert that after reviewing log info recovered from an institution that had been attacked by using Hidden Cobra, "analysts trust that the [hackers'] scripts ... inspected inbound financial request messages for selected [PANs]. The scripts generated fraudulent monetary response messages only for the request messages that matched the anticipated PANs. Most money owed used to rouse the transactions had minimal account recreation or zero balances."

In different words, malicious code inserted via Hidden Cobra attackers watched for references tied to attacker-controlled accounts, then again fraudulent advice about those bills in response to queries. as an instance, the code could faux that bills with a zero steadiness in its plot had cash obtainable for withdrawal.

"How the attackers capitalize control of these accounts is still unclear," Symantec says. "it's feasible the attackers are opening the money owed themselves and making withdrawal requests with playing cards issued to those debts. another probability is the attackers are the disburse of stolen cards to execute the attacks."

Hackers occupy odds of out of date AIX

what's now clear, although, is that the attacks were done by means of hackers exploiting outdated models of IBM's AIX - for advanced Interactive government - implementation of the Unix operating gadget, Symantec says.

"In every mentioned FASTCash attacks to date, the attackers hold compromised banking software servers working unsupported versions of the AIX operating gadget, beyond the conclusion of their carrier pack assist dates," Symantec says.

One obtrusive defense is for banks to live confident that they're preserving every systems and software up to date.

"with a view to permit their fraudulent withdrawals from ATMs, the attackers inject a malicious [AIX] executable perquisite into a running, reputable process on the swap utility server of a fiscal transaction community, in this case a network dealing with ATM transactions," Symantec says. "The malicious executable includes common sense to construct fraudulent ISO 8583 messages," which is the international usual for economic transaction messaging.

"The purpose of this executable has now not been in the past documented," Symantec says. "It became prior to now believed that the attackers used scripts to control respectable application on the server into enabling the fraudulent exercise."

How Fastcash Trojan helps ATM cash-Out attacks source: Symantec

In different phrases, attackers Do not appear to had been subverting professional financial institution software by means of scripts, as ultimate month's US-CERT alert suggested. in its place, the attackers hold been deploying their personal AIX malware, custom-made for the target atmosphere.

"FASTCash illustrates that Lazarus possesses an in-depth potential of banking techniques and transaction processing protocols and has the erudition to leverage that erudition in an exertion to swipe colossal sums from recumbent banks," Symantec says. "Lazarus continues to pose a sedate random to the economic sector and businesses may still occupy every imperative steps to shape confident that their fee programs are absolutely up to date and secured."

North Korea Hacks

protection experts drawl the executive of North Korea continues to invest in hacking to assist it carry cash to offset crippling international sanctions imposed over its weapons edifice and trying out classes (see: record: North Korea Seeks Bitcoins to bypass Sanctions).

"Lazarus continues to pose a significant possibility to the monetary sector."

Lazarus has been previously tied to a pair of cybercrime and cyber espionage attacks, including the wiper malware assault against Sony photographs amusement in 2014; the tried theft of practically $1 billion from the principal financial institution of Bangladesh's mammoth apple Federal Reserve account, resulting in $81 million being stolen; the WannaCry ransomware outbreak in may additionally 2017, as smartly because the disburse of cryptocurrency mining malware named Adylkuzz to assail the equal flaw in windows server shroud messaging that WannaCry moreover centered.

Lazarus has additionally persevered to swipe cryptocurrency by the disburse of phishing attacks as well as at once hacking cryptocurrency exchanges, in keeping with Moscow-based security solid neighborhood-IB.

Lazarus community features prominently on community-IB's tally of cyberattacks against cryptocurrency exchanges.

but Lazarus is just one of what issue to live a few diverse North Korea-subsidized hacking businesses, every of which share malware progress substances (see: Cybercrime corporations and Nation-State Attackers Blur collectively).

final month, cybersecurity solid FireEye mentioned that it had tracked a pair of assaults perpetrated with the aid of APT38, a North Korean hacking community part to Lazarus (see: North Korean Hackers Tied to $one hundred Million in SWIFT Fraud).

"due to the fact that as a minimum 2014, APT38 has conducted operations in additional than sixteen corporations in at the least eleven nations, every so often simultaneously, indicating that the neighborhood is a big, prolific operation with huge elements," FireEye researchers noted.


patrons File class motion Lawsuit in opposition t Symantec for faulty AntiVirus software | killexams.com existent Questions and Pass4sure dumps

Schubert law enterprise Investigating Claims towards safety application company regarding Vulnerabilities in network safety application products

SAN FRANCISCO, Nov. 8, 2018 /PRNewswire/ -- patrons hold filed a category action lawsuit towards Symantec supplier (SYMC), alleging that from 2005 to 2016, the protection utility company bought antivirus items containing sedate defects that exposed whole laptop operating systems to numerous safety vulnerabilities and made them extra at risk of cyberattacks, according to Schubert Jonckheer & Kolbe, which represents the patrons.

The lawsuit, filed on April 2, 2018 in the U.S. District courtroom for the Northern District of California, alleges that consumers who bought Symantec's security items under the Norton manufacturer, in addition to companies that bought products under the Symantec company, suffered from exposure to reminiscence corruption, faraway attacks, and other conveniently exploitable protection vulnerabilities. Symantec handiest released patches for these defects in June 2016, with the back of chore Zero, a team of knowledgeable cybersecurity analysts who first establish out the crucial defects within the core engine that supported Symantec's complete product line.

based on the lawsuit, Symantec had didn't patch third-celebration source code in its products over a several-yr duration spanning at least seven years, opposite to cybersecurity most advantageous practices, which require timely implementation of safety updates to application. in addition, Symantec didn't design its products to restrict the repercussion of a vulnerability on a computer's operating device by violating the fundamental precept of least privilege, one more primary cybersecurity most efficient observe. It turned into now not except 2017 that Symantec up-to-date its product line to conform to the precept of least privilege by means of including a sandbox that directs the core engine to dash and anatomize unknown files in an isolated, included ambiance.

if you purchased a Norton or Symantec manufacturer safety product between 2005 and 2016, you may well live entitled to money back of the buy fee. in case you suppose you were affected by these antivirus application defects, gratify contact us nowadays to learn more.

About Schubert Jonckheer & KolbeSchubert Jonckheer & Kolbe represents shareholders, personnel, and buyers in classification movements against corporate defendants, as well as shareholders in by-product moves against their officers and directors. The enterprise is based mostly in San Francisco, and with the aid of co-assistance, litigates instances nationwide. attorney promoting. Prior results Do not assure equivalent results.

ContactCassidy KimSchubert Jonckheer & Kolbe LLPckim@sjk.lawTel: 415-788-4220

 

View fashioned content material:http://www.prnewswire.com/news-releases/buyers-file-classification-action-lawsuit-in opposition t-symantec-for-faulty-antivirus-application-300746568.html


Symantec (SYMC) this drop 2018 outcomes - income designation Transcript | killexams.com existent Questions and Pass4sure dumps

No outcome discovered, are attempting novel keyword!Symantec Corp. (NASDAQ:SYMC ... They plot to give you compress duration on a quarterly groundwork going forward. They are expecting that the implementation of the novel earnings cognizance common will hold an effect on their metr...

250-700 Implementation of HA Solutions for Windows using VCS 4.1

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250-700 exam Dumps Source : Implementation of HA Solutions for Windows using VCS 4.1

Test Code : 250-700
Test designation : Implementation of HA Solutions for Windows using VCS 4.1
Vendor designation : Symantec
: 73 existent Questions

Dont forget to strive these latest dumps questions for 250-700 exam.
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Implementation of HA Solutions for Windows using VCS 4.1

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Configure a GlassFish Cluster With Automatic Load Balancing | killexams.com existent questions and Pass4sure dumps

Configuring application servers may not live as picayune as it seems. There are some configuration commands and parameters that may not toil as the users intuit. To shape matters worse, configuring clusters not only involves tuning parameters, but moreover requires you to deal with the availability of servers, SSH configurations, operating application server in every nodes, etc. Thus below we’ll justify how to easily create a GlassFish cluster with Docker and Jelastic using solutions such as CloudScript.

For this instance guide, we’ve chosen Oracle GlassFish application server, as it offers reference implementation of Java EE 7 and has a centralized passage to operate clusters, applications, and configurations without the necessity to manage every cluster node. According to the official GlassFish documentation, administration node has the following architecture:Administration Node Architecture

In the picture above, the Domain Administration Server (DAS) is the administration node of a cluster, which can communicate between cluster nodes by DCOM (in case of GlassFish clusters in Windows nodes) or by SSH (in case of Linux-, Solaris- and macOS-based cluster nodes). Also, there exists the third option, named CONFIG, intended to manage each node locally. In order to centralize the administration, GlassFish Docker image is using SSH for communication between DAS and other GlassFish worker nodes. Now, let’s narrate how the Docker images were prepared.

Docker Images

For this demo, we’ll disburse two Docker images:

  • One GlassFish Docker image ready to shape centralized clusters, hosted in this repository.
  • One HAProxy Docker image, provided by Jelastic, to toil as a load balancer.
  • The identical GlassFish Docker image can result in containers performing both DAS and cluster node roles. What makes it feasible are several customizations made in the Docker image, originally created by Bruno Borges from his GlassFish 4.1.1 image. So, they customized the images to stir away from Oracle Linux to Debian, add the OpenSSH install step, and define several configurations in the image provisioning and start up processes.

    The containers necessity to communicate between them through SSH, so installing an SSH server is mandatory (we’ve used OpenSSH in this case). Additionally, the entries PubkeyAuthentication, StrictModes, AuthorizedKeysFile, PermitRootLogin, and IdentityFile in /etc/ssh/sshd_config had to live properly set. Moreover, the SSH keys are scanned during startup process to avoid SSH getting stuck due to the key’s fingerprint acceptance, by ssh-keyscan doing it for us. Once the SSH was properly configured, they are able to automate cluster configuration.

    Cluster configuration in GlassFish 4.1.1 is not a picayune chore to automate, requiring some plane of expertise. This Docker image can toil as a DAS by setting -e ‘DAS=true’ in the Docker dash command. If a container is dash with this parameter set, it will start the domain, create a cluster named cluster1, desist the domain, and start it again with -v parameter. If a GlassFish container has a DAS container linked, it assumes itself as a cluster node, creates a cluster node by using asadmin create-local-instance command, stops the domain, updates the node definitions by using the command asadmin update-node-ssh from the DAS node in order for the DAS node to metamorphose the callee into a SSH cluster node, and finally starts the instance by using nadmin start-instance command. For a better understanding, gratify read the run.sh source code in the GlassFish Docker image repository.

    Jelastic offers a HAProxy Docker image ready to live used in Jelastic environments. Containers from jelastic/haproxy-managed-lb Docker image can add or remove nodes from the load balancer configuration by running the shell script /root/lb_manager.sh inside the container, with one of the following parameters:

  • /root/lb_manager.sh –addhosts [Container LAN IP]
  • /root/lb_manager.sh –removehost [Container LAN IP]
  • Running the GlassFish Cluster

    For this demo, they used Jelastic as a Docker infrastructure and they created a CloudScript of this demo, in this repository. They moreover used the clusterjsp.ear application to test whether the cluster and the load balancer were working as expected.

    Before using the JSON file from https://github.com/jelastic-jps/glassfish-cluster, let’s check what exactly this environment should start for us. In their case, they compose this CloudScript file, moreover called JPS or Jelastic Packaging Standard, that describes the topology that Jelastic must create, what should live installed, what should live started, and the responses to the events triggered by Jelastic administration — such as scale out and scale in. In the topology section, they contemplate three GlassFish nodes, each one in its own group, and an HAProxy node. After this section, there is an onInstall JavaScript object, calling some actions. Lastly, there is a procedures object, which defines some routines to dash in the installation process, and the in the CloudScript uses a shell script to add GlassFish nodes to the cluster.

    In this CloudScript, they can notice that there are two events to update the load balancer’s configuration — onAfterScaleOut and onAfterScaleIn — and one event to update the cluster — onBeforeScaleIn. In the case of an onAfterScaleOut event, a procedure is called to add the nodes to the load balancer, using the script inside HAProxy container. The event onAfterScaleIn just calls the /root/lb_manager.sh directly inside a for loop, and passing every the nodes from event.response.nodes. In the case of an onBeforeScaleIn event, a procedure is called to remove GlassFish nodes from the cluster.

    Now, as you hold a better understanding of the proposed JSON file, let’s import it in Jelastic to create an environment. You can access the GlassFish DAS administration console by getting the HTTP URL for it — you can obtain it by clicking on ‘Open in browser’ button at the perquisite of the das node designation — and accessing it using HTTPS and in the port 4848. To test whether the load balancer works, deploy the clusterjsp application in cluster1 and set the Availability option. Accessing the load balancer container URL, you may contemplate a GlassFish web page indicating that it is running. Set the context of the deployed application and you’ll access the application. After one or two page reloads, you’ll contemplate that the node accessed varies.

    Create Clustered Environment

    You can import the environment by following the steps below:

    1. Access Jelastic console and click Import.Import Environment Jelastic

    2. Select the URL tab and set this URL from the cluster JSON.Import by URL Jelastic

    3. Set the designation for the environment and click Install.Set the  designation Jelastic

    4. Finally, as they can see, the cluster was created.Created Cluster in Jelastic

    Deploy and Configure Application

    After creating the cluster, they can deploy an application to the cluster and declar that the cluster is working correctly and the load balancer is operating as it should.

    1. At the GlassFish das node row, click the button Open in browser located at the perquisite of node’s name.Open in browser Jelastic

    2. Once the browser window is open, it shows the default GlassFish web page. Change the URL browser to https://<environment_domain>:4848.

    3. Enter the GlassFish console with the user admin and password glassfish.GlassFish console

    4. depart to the Application option and deploy the clusterjsp.ear application:Deploy application

    5. Now open the HAproxy node in browser and add /clusterjsp at the wait of the URL.open HAproxy in browser

    Every time you refresh the page, the executed server IP address changes, indicating that the load balancer is working.

    Conclusion and Future Works

    Docker images and scripting Do a much job at automating environment creation together. Docker containers from images, which dash in Jelastic the identical passage they dash in any other infrastructure, and scripting, to capture the rectify topology of the servers involved in the environment, are the bread and butter of any Docker environment that can live used in Jelastic infrastructure. They capitalize from its advantages in terms of resource management and are able to migrate from other platforms to Jelastic and migrate away if needed.


    Real-Time High-Performance Computing with NI LabVIEW | killexams.com existent questions and Pass4sure dumps

    1. Introduction

    This White Paper is piece of a sequence of articles related to Real-Time High-Performance Computing (RT-HPC) and Scientific Computing applications with NI LabVIEW. In this document, they present a comprehensive review of the different approaches, technologies and methods that can live implemented in NI LabVIEW to occupy odds of parallel computing architectures.

    Some scientific and technical applications are very demanding in terms of computational intensity, size of data sets and number of I/O channels for online data streaming, analysis and visualization. These applications execute High-Performance Computing-type (HPC) computations under real-time constraints. As processing capabilities increase and parallel programming barriers decrease, they hope this trend to continue and most likely accelerate (see device 1.1) in terms of Tera and Giga- Floating Point Operations Per Second (TFLOPS and GFLOPS) and Giga Multiply- Accumulate operations per Second (GMACS). This includes such applications as plasma control in nuclear fusion, adaptive optics in extremely big telescopes, RF MIMO systems, smart electrical grid control, high-resolution medical imaging, multi- agent distributed robotic systems and big real-time hardware-in-the-loop simulations for complex dynamic systems, among many others.

    Fig. 1.1: Real-Time HPC Trend

    In order to respond to these and other challenges, the following key elements are supported: a) Intel® multicore CPUs (mCPUs or nCores) running a real-time operating system (RTOS), b) capability for the direct implementation of algorithms and signal processing functions at the embedded level, on Xilinx® domain Programmable Gate Arrays (FPGAs), c) access to nVIDA® Graphical Processing Units (GPUs) as hardware accelerators for numerical/computational tasks through the CUDA™ and CUBLAS™ toolkits and libraries, d) multi-channel, high-speed, mixed signal (analog/digital) acquisition and generation with NI’s modular hardware and, e) precise synchronization/timing between every measurements and computations with NI’s synchronization boards.

    By combining different hardware components and sub-systems, available as Commercial-Off-The-Shelf (COTS) technologies, a hybrid, heterogeneous architecture is easily configured. This architecture combines mCPUs, GPUs and FPGAs as piece of an integrated system. The hardware architecture is based on the PXI Express (PXIe) platform and is compatible with the PICMG’s CompactPCI Express standard. CompactPCI Express brings PCI Express technology to the approved PICMG 2.0 CompactPCI (cPCI) shape factor, while maintaining compatibility with CompactPCI hardware and software. The suggested system is based on a combination of the PXIe 1U and 3U shape factors.

    Fig. 1.2: RT-HPC system with PXI and nVIDIA® GPU Computing system

    The system includes a numerical computing/accelerator sub-system, and a measurement/control sub-system.

    1.1 Numerical Computing/Accelerator Sub-system

    As shown in device 1.3, two NI 8353 multicore computers (1U shape factor) are used in conjunction with one nVIDIA® Tesla™ GPU computing system (which includes four T10 GPUs) for highly demanding computational tasks and as co-processors or accelerators for the PXIe controllers. These two NI 8353 computers and the nVIDIA® Tesla™ GPU computing system conform the Numerical Computing/Accelerator System. Although GPUs are not directly programmed with NI LabVIEW, the architecture of this framework is designed to integrate GPU execution into LabVIEW's parallel execution system by establishing a compute context in which to execute the CUDA™ code.

    Fig. 1.3: Numerical Computing/Accelerator Sub-system

    At the hardware level, access to four nVIDIA® T10 processors via a Tesla™ S1070 computing system (see device 1.4) offers a abstract maximum of 4 TFLOPS on single-precision data that, in practice, could capitulate 400 GFLOPS or better performance. The NI 8353 multicore computers are designed for high-performance measurement and control applications. Each one of these computers comprise four removable SATA II difficult drives in a RAID 0 arrangement (high-capacity hard-disks are supported).

    Fig. 1.4: Four nVIDIA® T10 GPUs in the Numerical Computing/Accelerator Sub-system

    1.2 Real-Time Measurement and Control Sub-system

    For direct, real-time measurement and control, two or more PXIe chassis (8 or more slots each) are used; in this example, the PXIe-1082 chassis are used. Each chassis has its own embedded controller with a mCPU and one or more FPGA-based data acquisition/control boards. The number of boards installed on each chassis can vary. In this example, two PXIe controllers occupy odds of the FPGAs available on the data acquisition boards as co-processors and algorithm accelerators. A Real-Time Operating System (RTOS) integrated with LabVIEW RT is used on each multicore controller.

    Fig. 1.5: Real-Time Measurement and Control Sub-system

    More specifically, one PXIe chassis (chassis on the left, device 1.5), includes two FPGA-based, reconfigurable data acquisition boards (PXI-7852R), each one with 8 analog-input/outputs with 16-bit resolution ADC/DACs and simultaneous sampling rate of up to 750 kHz per channel (analog inputs). On the other PXI Express chassis (chassis on the right, device 1.5), three FlexRIO high-speed digitizers based on DSP- optimized FPGAs are included (PXIe-7965R), each one with a FlexRIO Adapter Module (FAM) with 32 analog inputs and 12-bit analog ADCs that are simultaneously sampled at 50 MS/s (NI 5752). Using Gigabit Ethernet, the two PXIe embedded controllers communicate with the NI 8353 multicore computers that will in rotate hold access to four GPUs in the nVIDIA® computing system to offload computations of the control algorithms and logs every acquired data for further use.

    Fig. 1.6: P2P using two FlexRIO boards on the PXI Express Bus

    Another feature of this configuration is the novel NI peer-to-peer (P2P) streaming technology that uses the PXIe bus to enable direct, point-to-point transfers between two or more FlexRIO boards and other instruments without sending data through the host processor in the embedded controller or memory. This enables devices, such as the FPGA-based FlexRIO and R sequence DAQ boards in each PXIe system to share information directly on the PXIe bus without burdening other system resources (see device 1.6 above).

    Finally, each PXIe chassis includes a timing board (PXIe-6672) for synchronization purposes. A timing board on one of the two PXIe chassis (as shown in device 1.5) is configured as the “master” and provides a synchronization signal through a high- resolution DDS clock generation (DC to 105 MHz) and an onboard high-stability reference TCXO (1 ppm); the other timing board is configured as a “slave”.

    Back to Top

    2. RT-HPC Programming

    A graphical system design approach is used taking odds of the NI LabVIEW programming environment. every the software and codes for a NI LabVIEW-based RTHPC system are every written in G (LabVIEW’s compiled programming language), including the embedded, parallel code to live executed in the mCPU, FGPAs and GPUs (see device 2.1); NI LabVIEW is moreover used for every the GPU-based algorithms as well as every the communication and synchronization functions. The LabVIEW Professional progress System (PDS), the LabVIEW Real-Time module and LabVIEW FPGA module plus various toolkits and toolsets (Control Design and Simulation, Advanced Signal Processing, etc.) are used to design, prototype and deploy the data acquisition, control, analysis and visualization system that conforms the RT-HPC system.

    Figure 2.1: LabVIEW as a common programming implement for every “targets”

    Based on the dataflow paradigm, the NI LabVIEW programming language is intrinsically parallel and can easily occupy odds of multi-core processors such as the Intel® Core Duo/Quad and higher-core density processors using such programming techniques as chore Parallelism, Data Parallelism, Pipelining, Structured Grid (Systolic Arrays) and more. Also, NI LabVIEW is used to program FPGAs; GPUs are supported through a “wrapper: that allows the access to the nVIDIA® CUDA™ and CUBLAS™ toolkit and libraries; additionally, C code can live generated by NI LabVIEW and compiled with a different implement chain to target a specific architecture or custom hardware.

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    3. Taking odds of Multicore Processors with NI LabVIEW

    Thanks to its dataflow architecture and the champion of different models of computation (e.g. State-Charts), the NI LabVIEW graphical programming paradigm makes parallel programming easy, even for novice users. In addition to the three common methods to achieve parallelism (Figure 2.1), other methods can moreover live implemented in NI LabVIEW.

    We know that “Task parallelism” is easily implemented in NI LabVIEW, where independent parallel tasks can dash concurrently on every available cores in the mCPU, resulting in significant speedups. Also, “Data parallelism” and “Pipelining” can live implemented on multicore processors using NI LabVIEW. In order to justify how easy is to implement these methods in NI LabVIEW, an instance of parallelized matrix-vector multiplication operation is shown in device 3.1.

     

    Figure 3.1: Matrix vector multiplication in NI LabVIEW (one core vs. eight cores)

    As shown, a matrix-vector multiplication can depart from one to eight cores very easily in NI LabVIEW. In a real-world application, data could live captured from sensors to provide the vector on a per-loop basis. The result of the matrix-vector multiplication operation can then live used to control the corresponding actuators.

    The identical applies to matrix multiplication. The performance gain when using multicore CPUs is shown in device 3.2. In this instance a matrix multiplication is performed. As shown, as the number of threads (and number of cores) increases, so the performance gain increases. This performance gain is obtained even if the size of the matrix moreover increases. These gains are achievable in a more deterministic passage when using a RTOS and LabVIEW RT; still, as shown in the Figure, NI LabVIEW for MS Windows offers some significant performance gains although with less determinism (higher “jitter”).

    Figure 3.2: Multicore Matrix Multiplication Performance Gains

    In order to occupy odds of these parallel architectures, different programming techniques are required (Figure 2.1). In addition to the obvious ones (Task Parallelism, Data Parallelism and Pipelining), NI LabVIEW supports other models of computation that can occupy odds of parallel architectures. One of those models is the Systolic Arrays which is analyzed in the next section.

    3.1 Systolic Arrays (Structured Grids) on Multicore processors:

    Many computations that involve physical models can occupy odds of Systolic Arrays (SA), moreover known as structured grid patterns (Figure 3.3). SAs are commonly used in such applications as genomics, bioinformatics, sequence alignment, computational fluid dynamics and dynamic programming.

    In SAs, you cipher a 2D or nD grid or array on every iteration; each updated grid value is a duty of its neighbors as each processor component (PE) shares information with their them; information is shared after each PE performs the needed operations on the data, flowing synchronously across the array between neighbors. Dataflow computing and multicores and FPGAs are obvious platforms for SAs.

    Just as in SAs where cells are connected by “wires”, in NI LabVIEW graphical programming paradigm blocks or nodes or “icons” are connected with “graphical wires”.

    With a parallel version of a SA, you split the grid into sub-grids and compute each sub- grid independently. Communication between workers is only the width of the neighborhood. Parallel efficiency is a duty of the area-to-perimeter ratio. 

    Figure 3.3: A basic Systolic Array

    SAs are moreover an option for performing basic matrix-matrix multiplication operations. As shown in device 3.4, a systolic algorithm can live easily applied for a 3x3 matrix multiplication operation.

    Figure 3.4: A Matrix-Matrix multiplication operation with a Systolic Array

    SAs are moreover natural architectures for developing solvers to Partial Differential Equations (PDEs). For example, the shroud diagram in device 3.5 (a 2-D Systolic Array) can unravel the Heat Equation, where the border conditions constantly change. The 16 visible icons in device 3.4 delineate tasks that can unravel the equation of a unavoidable grid size. In this example, the 16 tasks map to a 16-core Intel® multicore CPU. On every iteration, the 16 cores exchange border conditions and the process builds up a global solution. The Feedback Nodes, which issue as arrows above minuscule dots, delineate data exchange between elements. You can moreover map such a shroud diagram to a two-, four-, or eight-core CPU. As computers with more cores become available, you can disburse a similar strategy.

    Figure 3.5: Solving the Heat Equation with NI LabVIEW using a Systolic Array (Structured Grid)

    Systolic Arrays can moreover live implemented on FPGAs. Processing elements or cells can live created on the FPGA, together with the corresponding shift-registers, adders and other required elements. For example, NI LabVIEW FPGA can live used for implementing a two dimensional SA that performs a matrix-vector or matrix-matrix multiplication operation in a few clock cycles. Clock cycles can live as fleet as 10ns (100 Mhz clock).

    Performance is usually measured in terms of Giga Cell Updates Per Second (GCUPS), representing the ratio of the number of processors or cells in the array to the total time consumed. On FPGAs, the performance can moreover live measured by number of cells in the array and the clock frequency used. In short, the performance of SAs in FPGAs mainly depends on the number and efficiency of the cells, including the clock cycles used and the belt required (# of slices). For example, a approved algorithm in bioinformatics is dash (Basic Local Alignment Search Tool), used for comparing primary biological sequence information, searching both protein and DNA sequence databases for sequence similarities; the dash algorithm is a impeccable candidate for a NI LabVIEW implementation of a Systolic Array on a FPGA.

    Back to Top

    4. Taking odds of GPUs in NI LabVIEW

    Some applications are tailor made for deployment to GPUs, such as those related to matrix- vector operations. LabVIEW GPU Computing unleashes the computing power of nVIDIA® GPUs via the CUDA™ toolkit interface from within a LabVIEW application. Code that calls the GPU for computation is integrated into the autochthonous parallel execution system of LabVIEW as if it were any other multi-threaded external library duty call.

    Supporting GPU computing from NI LabVIEW requires an interface component and a compute framework. The interface is made up of two LabVIEW libraries – lvcuda.lvlib and lvcublas.lvlib – that accommodate Controls and VIs that map to CUDA™ types and CUDA™ runtime or CUBLAS™ functions. The interface exposes key nVIDIA® CUDA™ resource and CUBLAS™ library management functions. It cannot comprise the real-world solutions developed by users, designed to dash on GPUs, and invoked from C/C++ code. For the user-defined class of CUDA™ implementations, a special compute framework is available through NILabs.

    The NI LabVIEW GPU Computing includes:

    - A collection of LabVIEW data types and VIs (see device 4.1), called LVCUDA and LVCUBLAS, for interfacing with the CUDA runtime and CUBLAS Library functions.- A framework, called NI LabVIEW GPU Computing that establishes a compute context in which user-define GPU functions execute.

    Figure 4.1: LabVIEW VIs (LVCUDA) for the nVIDIA® CUDA™ and CUBLAS™ libraries

    Using these data types, VIs and framework allow LabVIEW users to:

    -Target multiple GPU devices from the LabVIEW diagram.- Manage resources across every GPU devices.- Facilitate numeric array data transfers to and from the GPU.- Execute GPU process in parallel with CPU execution.- Provide shared resource protection when performing GPU processes from different user libraries.- shroud invalid references when used on the wrong GPU device.- Establish clean-up callbacks that ensure resources are freed even when the application is aborted.

    A basic matrix-matrix multiplication operation (AxB) using the NI LabVIEW GPU Computing library for nVIDIA® GPUs is shown in device 4.2.

    Figure 4.2: Matrix vector multiplication in LabVIEW using the nVIDIA® CUDABLAS® library

    In the above example, a square matrix (m=n) of size 300 is used for the multiplication operation. In order to execute this operation, the following four steps are taken:

    a) Step 1: Set the GPU Device Context and the Generate the two matrices (A and B), as shown in device 4.3

    Figure 4.3: Step 1 for the AxB matrix multiplication operation with NI LabVIEW and an nVIDIA® GPU

    b) Step 2: memory allocation for matrices A, B and C (Figure 4.4)

    Figure 4.3: Step 2, memory allocation for “C” on the CPU and for A, B and C on the GPU.

    c) Step 3: AxB multiplication operation to obtain C (Figure 4.4)

    Figure 4.4: Step 3, memory allocation for “C” on the CPU and for A, B and C on the GPU.

    d) Step 4: Free allocated resources (Figure 4.5)

     

    The performance gain when using GPUs can moreover live significant. As shown in device 4.6, GPUs are another processor architecture that has peak performance beyond that of CPUs and multicore processors. Instead of throughput, data size dictates the tipping point between CPU and GPU usage.

    Figure 4.6: GPU-based parallel Architectures Drive Performance (Source: nVIDIA®)

    Back to Top

    5. Taking odds of FPGAs with NI LabVIEW

    The NI LabVIEW FPGA Module uses LabVIEW embedded technology to extend LabVIEW graphical progress and target field-programmable gate arrays (FPGAs) on NI reconfigurable I/O (RIO) hardware. LabVIEW is distinctly suited for FPGA programming because it clearly represents parallelism and dataflow. With the LabVIEW FPGA Module, you can create custom measurement and control hardware without low-level hardware description languages or board- plane design. You can disburse this custom hardware for unique timing and triggering routines, ultrahigh-speed control, interfacing to digital protocols, digital signal processing (DSP), RF and communications, and many other applications requiring high-speed hardware reliability and tight determinism.

    Despite much strides in raw processing performance on CPUs, it can’t match the raw computing skill of FPGAs. In fact, some applications are tailor made for deployment to FPGAs. dote GPUs, FPGAs can live an excellent option for accelerating algorithms and computations in real- time simulation and control systems (See device 5.1)

    Figure 5.1: FPGA-based Parallel Architectures Drive Performance

    When using FPGAs, the inherent parallelism of LabVIEW is realized in hardware as dependable simultaneous execution. The compiled code is implemented in hardware by configuring logic cells in the FPGA. Your embedded VI does not necessity access to a processor to execute. Independent sections of code, such as parallel “while loops”, are implemented in independent sections of the FPGA.

    Figure 5.2: LabVIEW FPGA-based code

    After the FPGA is configured, data is clocked through the device at a rate specified by the onboard clock, executing independent areas of the chip simultaneously. In device 5.2, the parallelism of LabVIEW FPGA and reconfigurable I/O enables the analog and digital loops to execute simultaneously without competing for execution time from the host processor. The timing functions occupy odds of the on-board clock, achieving timing resolution of 25 nanoseconds or better (time cycles of 40 Mhz, 80 Mhz, 100 Mhz, 200 Mhz and more are available).

    As an additional example, the following streaming application represents a class of problems that simply cannot live efficiently addressed by more universal purpose processors dote CPUs.

    The problem consists in applying a picayune pattern matching algorithm to megapixel images at exceptionally tall frame rates – 20Kf/s. This represents a throughput is 20 GSamples/s which is well out of the compass of CPUs (including multicore CPUs) but well within the bounds of FPGAs(see device 5.3).

    Figure 5.3: LabVIEW FPGA-based streaming sensor data processing

    In order to unravel this problem, a four (4) quadrant partitioning is used to address the input saturation by the tall frame rate. The goal is to scale an incoming 1k x 1k sensor grid by a 1k x 1k coefficient matrix, and integrate it to one measurement. “Image” sensor data is streaming at 50 us per frame, or 20,000 frames per second. The computation challenge is 1k x 1k / 50us = 20 Giga Operations per Second (20 x 10^9 operations per second or 20 GOPS).

    Using a lone NI FlexRIO board with LabVIEW FPGA (see device 5.4), it is feasible to process 64 “pixel” streams to compute a 512x512 image size at a rate of 24,000 frames per second. The benchmarked computational power is 512x512x24k/s = 6.3 GOPS. Even at these very tall rates, the resource (FPGA) utilization is only at 25% of the logic and only 10% (64/640) of the DSP blocks, at only 100MHz (10 ns), on the NI FlexRIO (PXIe-7965R). This application requires at least 3 FlexRIO modules to exploit the incoming data bandwidth if each sensor “pixel” is 8 bits. For 16-bit sensor data, they would require twice the number of FlexRIO modules.

    Figure 5.4: Mask and integrate a 64 PE Array in LabVIEW FPGA

    Back to Top

    6. Conclusions

    The availability of COTS technologies for real-time high-performance computing and real-time simulation and control allow scientists and engineers to easily respond to novel challenges and demanding applications with tall computational requirements, big data sets and big number of I/O channels. Powerful multicore processors, FPGAs and GPUs provide increased processing capabilities, overcoming the parallel programming barrier. Dataflow-based graphical programming for heterogeneous architectures is piece of the response to these novel challenges. They hope this trend to accelerate in terms of GFLOPS, GOPS, GCUPS and GMACS in a clear path to compass Exascale computing levels in the next few years.

    NI LabVIEW and the NI hardware platform allows scientists and engineers to develop scalable systems that Do not require highly specialized application programmers to explicitly manage system complexity, in terms of how to achieve dependable parallelism, how to exploit memory issues and how to program heterogeneous architectures from a high-level of abstraction; to achieve higher performance, shorter time-to-prototype and time-to-deployment, and dynamic adaptability to novel requirements (reconfigurability) at the cabinet, desktop and embedded levels. NI LabVIEW provides a wide variety of tools and models of computation for rapid conversion (design-to-deployment) of parallelized algorithms to COTS hardware, using readily available IP cores or custom-designed ones, an extensive library of math and signal processing functions that can live easily implemented on multicore CPUs and FPGAs, or accelerated by GPUs through the nVIDIA® CUDA™ and CUBLAS™ toolkit/libraries.

    There seems not to exist one solution for every applications, so the heterogeneous, hybrid architecture approach seems to live the best option to today’s very demanding scientific and engineering applications. Also, the scalability of these systems seems to live a key success factor as a typical application could comprise very minuscule shape factor embedded elements (e.g. smart sensors) as well as large, multichannel real-time systems such as the ones described in this document, as piece of a large, distributed complex cyber-physical system that involves different data types and structures, math operations, signal processing and analysis, and visualization, every within one integrated system. RT-HPC responds to some of those needs and can live easily adapted to different applications and scalability needs.

    Contact: Igor Alvarado – Academic business progress Manager

    E-mail: igor.alvarado@ni.com

    URL: www.ni.com/academic

    References

    1. High-Performance Computing (HPC) in a Real-Time Environment, http://zone.ni.com/devzone/cda/tut/p/id/74312. LabVIEW tall Performance Analysis Library, http://decibel.ni.com/content/docs/DOC-120863. Prototyping Algorithms for Next-Generation Radio Astronomy Receivers Using PXI- Based Instruments and High-Speed Streaming, http://sine.ni.com/cs/app/doc/p/id/cs-129724. Developing Real-Time Control for the World’s Largest Telescope, http://zone.ni.com/devzone/cda/pub/p/id/7115. Scientific Computing with Graphical System Design, http://zone.ni.com/devzone/cda/tut/p/id/76616. Programming Strategies for Multicore Processing: Data Parallelism, http://zone.ni.com/devzone/cda/tut/p/id/64217. Deterministic Synchronization of Distributed Simulation Systems, http://zone.ni.com/devzone/cda/tut/p/id/32468. LabVIEW GPU Computing, http://decibel.ni.com/content/docs/DOC-60649. Programming Strategies for Multicore Processing: Pipelining, http://zone.ni.com/devzone/cda/tut/p/id/642510. Optimizing your LabVIEW FPGA VIs: Parallel Execution and Pipelining, http://zone.ni.com/devzone/cda/tut/p/id/374911. novel Parallel Technologies -From Signal to Software, http://zone.ni.com/devzone/cda/tut/p/id/940312. NILabs, http://decibel.ni.com/content/groups/ni-labs13. NI Academic Research, www.ni.com/academic/researchers14. Advantages of the Xilinx Virtex-5 FPGA, http://zone.ni.com/devzone/cda/tut/p/id/7440


    CDW (CDW) Q3 2018 Results - Earnings convene Transcript | killexams.com existent questions and Pass4sure dumps

    No result found, try novel keyword!The customer was looking for a comprehensive end-to-end solution to ensure they had trustworthy availability and ... they hold repurchased roughly 4.1 million shares for $326 million, at an indifferent ...


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    References :


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