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IBM C2090-317 : DB2 11 System Administrator for z/OS Exam

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Test Number : C2090-317
Test Name : DB2 11 System Administrator for z/OS
Vendor Name : IBM
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Exam Name IBM Certified System Administrator - DB2 11 for z/OS
Exam Code C2090-317
Duration 90 mins
Number of Questions 58
Passing Score : 38 / 58

- Given a scenario, demonstrate knowledge of the installation/migration plan process
- Ability to identify additional environments necessary (WLM, Unix system services, XML schema support, RACF or equivalent, SMS)
- Given a scenario, describe how to execute the installation/migration plan
- Evaluate appropriate subsystem parameter (DSNZPARM) settings
- Evaluate appropriate migration to extended RBA/LRSN tasks
- Given a scenario, demonstrate knowledge of how to protect DB2 subsystem and resources (implementing external security only)
Knowledge of various System authorities
Support for distributed identities, connection level security enforcement
Program authorization and masking including enhancements
- Describe auditing techniques (policies)
- Describe role-based security and the advantage of deploying it (trusted context)
- Describe how to use and manage DB2 components and processes (adminitrative task scheduler) (REPAIR, STOSPACE, DSNJU003, DSN1COMP)
- Demonstrate knowledge of DB2 commands
- Given a scenario, describe how to monitor and control DB2 threads
- Knowledge of DB2 data sharing system operation and maintenance
- Knowledge of distributed functionality
- Demonstrate knowledge of advanced backup and recovery (system level, data set level, undo, redo, backup, flash copy, DSS, HSM, DSNJU004, DSN1COPY, DSN1LOGP, DSN1PRNT, Extended RBA, etc.)
- Given a scenario, describe how to recover from system failures (restart, conditional restart)
- Describe the process for disaster recovery
- Describe DB2 data sharing recovery scenarios
- Knowledge of consideration/impact of extended and non-extended RBA/LRSN tasks
- Given a scenario, analyze performance
- Demonstrate knowledge of how buffer pools and system parameters (DSNZPARMs) can affect performance - concurrency
- Demonstrate knowledge of persistance threads (bind parameters, reallocate, protected threads)
- Demonstrate knowledge of statistics, tools, and traces used for monitoring (trace definitions)
- Given a scenario, describe techniques to achieve efficient use of memory (flash memory, large buffer pools) Statement Cache (Dynamic, Static, EDM Pool)
- Demonstrate knowledge of workload manager (WLM) settings for DB2
- Knowledge of RUNSTATS (autonomic, manual, RTS, and feedback)
- Knowledge of Reorg in the context of system tuning and performance (system level settings, subsystem parameters, reduction of indirect references)
- Knowledge of specialty engines and how they affect performance (ziips)
- Given a scenario, describe which documentation can be used to solve a problem
- Demonstrate knowledge of DB2 commands and traces for troubleshooting
- Describe how to identify distributed threads
- Demonstrate knowledge of diagnostic utilities (REPAIR, DSNJU003, DSN1CHKR)
- Given a scenario, identify and resolve DB2 data sharing problems



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IBM for Practice Questions

Rethinking GPU Hackathons for a plague and past | C2090-317 Q&A and test Questions

Jan. 13, 2021 — When school of Michigan (UM) professor of aerospace engineering Venkat Raman and his team of graduate students were awarded a provide of compute time on the nation’s most powerful and smartest scientific supercomputer, the IBM AC922 Summit on the very wellRidge leadership Computing Facility (OLCF), they thought they'd it made. definitely, Summit’s computational horsepower would vault their study of rotating detonation engines and their fluid dynamics simulation code into the stratosphere.

at the beginning, youngsters, the actual contrary took place after they didn’t take skills of Summit’s greatest device for accelerating codes: its 27,648 NVIDIA Volta GPUs. The team had no adventure in optimizing code for GPU processors, in order that they didn’t put a great deal effort into it. consequently, their code in reality ran slower than it had on traditional CPU computers.

“We did not consider of GPUs as anything crucial: ‘We recognize how to write codes on CPUs and now people are telling us they should use GPUs?’ So, the first day my students ran the code, they became off all of the GPUs as a result of they idea it became a nuisance. They just ran it on the CPUs—and it was 10 times slower,” Raman spoke of.

This caught the consideration of the OLCF’s then–director of science, Jack Wells, who despatched the team an e-mail that just about noted, “Are you sure you guys are doing the correct factor? You’re not the usage of the GPUs.” The team, who had been awarded one hundred thirty,000 node-hours on Summit in the course of the US department of power’s (DOE’s) workplace of superior Scientific Computing analysis’s leadership Computing challenge (ALCC) program, couldn’t have enough money for his or her codes to run slower.

The OLCF made the crew privy to its sequence of fingers-on seminars meant to help clients optimize codes for GPUs: the OLCF GPU hackathons. The hackathons have advanced from a single, bold exercise in 2014 to a series hosted at institutions across the nation—and even world wide—to bolster programmers’ applications for scientific study on probably the most largest GPU supercomputers. The OLCF and NVIDIA co-organize the trainings along with the local host associations, and the OLCF additionally works with the Exascale Computing challenge to codeliver a handful of OpenMP-specific GPU hackathons as neatly. teams receive help from professional mentors at the 5-day movements, aiming to depart with a chunk of GPU code in hand.

“My students went to a hackathon in o.k.Ridge and that they despatched me a message from there asserting, ‘We had no conception what this computing device can do—we ought to rethink every little thing.’ in order that’s what they did,” Raman talked about.

Raman’s trio of PhD candidates accountable for coding—Shivam Barwey, Ral Bielawski, and Supraj Prakash—had tried using the OpenACC programming mannequin to offload just a few elements of their simulation software (UM detonation FOAM, or UMdetFOAM, firstly in line with the code OpenFOAM, for “Open-source container Operation and Manipulation”) to GPUs. however the results weren’t a lot more advantageous than their first foray. on the hackathon, where team individuals might talk to IBM and NVIDIA programmers without delay, they discovered why.

“whereas the OpenACC framework at first seemed very convenient to make use of, they necessary much more manage over what information is transmitted, how that statistics is transmitted, and the way they rewrite their algorithms to be able to greatest use that framework,” Barwey noted. “We should rethink the way the algorithm is structured so they saturate the GPU to function a large operation at a single time as opposed to at all times going backward and forward between the CPU and GPU.”

Upon coming back from the hackathon, the students started completely rewriting and editing their solver—which concerned replacing OpenFOAM and other exterior libraries with customized GPU libraries—to take full talents of Summit’s GPUs. Now, more than 95 p.c of the code runs on GPUs. moreover, the team recognized the possibility to leverage the advantages GPUs have for artificial intelligence algorithms and subsequently trained neural networks to speed up the modeling program even additional—a primary in the container. All of those advancements ended in GPU performance attaining near-theoretical limits, restricted most effective with the aid of the communication bandwidth for transferring records between the CPU and GPU.

“This allows for us to get—in some excessive instances—about 1,000-times-faster simulations in comparison to if they had just carried out accepted calculations,” Raman spoke of. “greater than even speedup, they can additionally answer different questions. It’s no longer an incremental exchange.”

because the team completed its ALCC assignment last summer time with groundbreaking effects—whereas additionally advancing the state of the paintings for computational fluid dynamics tools—the OLCF become reimagining the hackathon itself. confronted with a global pandemic, workforce participants were compelled early on to decide even if collaborative programming seminars could transition correctly to the digital area. may groups like Raman’s get as a good deal out of lengthy-distance interactions with mentors as they did in adult? might the OLCF chance a virtual format for the hackathons or may still it forgo them altogether?

“There turned into a considerable dialogue about even if this would be feasible and nonetheless supply cost to the contributors, and the organizers without delay decided that sure, it will be conceivable,” talked about Ashley Barker, section head of operations in the countrywide center for Computational Sciences (NCCS), which hosts the OLCF on the DOE’s very wellRidge countrywide Laboratory (ORNL).

In 2020, the OLCF—a DOE workplace of Science user Facility—cohosted 5 of the virtual hackathons with NVIDIA, with a complete of 296 people from sixty two diverse institutions participating within the pursuits. The success of the hackathons in the new format marks a milestone within the OLCF’s heritage and demonstrates the perseverance and tenacity of the high-efficiency computing (HPC) group to discover inventive solutions to problems—something required for code porting and development even under superior cases. It additionally paves the manner for alternatives to extend upon the hackathon’s fashioned layout and aid users discover new approaches of taking part at a time when it is required most.

a brand new hackathon for new times

Attendees of the OLCF’s virtual hackathon in October. The OLCF cohosted 5 virtual hackathons with NVIDIA in 2020, with a complete of 296 americans from sixty two distinct institutions collaborating. graphic credit score: Jason Smith, ORNL.

The first-ever virtual NVIDIA GPU hackathon was hosted via the San Diego Supercomputer core in may additionally. unlike regular hackathons, during which teams attend a 5-day-long consecutive adventure, the attending groups had one initial day of code profiling to investigate which items of their codes have been ingesting up their computational time. After that, they'd every week’s damage to put into effect changes and revise or adapt their suggestions for porting to the GPUs in Ascent, an OLCF verify bed device with an structure akin to that of the OLCF’s Summit supercomputer. within the 2d week, members had 3 full days to Excellerate and port their codes all through the digital hackathon experience.

the new format boasted many benefits appropriate off the bat. The hackathon organizers discovered it less complicated to establish mentors as a result of the skill to participate practically. along with the enhanced availability of mentors, the organizers identified preserving that separate introductory session become a superior practice for future digital hackathons since it better prepared members for the three-day experience.

The success of the first event marked the new format for the remainder of the 2020 hackathon sequence. The digital structure wasn’t devoid of its challenges, of route. Time zone changes and digital assembly fatigue were some of the biggest inconveniences. however, most participants observed efficiency good points and were capable of finding the same price they got at the in-grownup routine.

“individuals don’t ought to go back and forth, and that i feel they’re still capable of do what they were doing earlier than and get it accomplished in an analogous amount of time,” talked about Tom Papatheodore, HPC engineer within the Operations part at NCCS.

First-timers reap huge benefits

One crew that noticed success this 12 months changed into the stream–computational fluid dynamics (CFD), or movement-CFD, crew from Rensselaer Polytechnic Institute. The crew works with the parallel, hierarchic, adaptive, stabilized transient evaluation (PHASTA) code, which may also be used for range useful instances of pastime that can involve complex geometries—such as certain aerospace configurations or human arterial system—in addition to complicated physics, such as fluid turbulence or multiphase interactions. PHASTA makes it possible for researchers to achieve excessive accuracy for modeling turbulent flows in these techniques. The group took a mini-edition of PHASTA to the OLCF-hosted GPU hackathon on October 19 and October 26–28 and, with aid from NVIDIA mentors on the hackathon, ported the mini-application to the OLCF’s Ascent examine bed system and carried out a ten-fold speedup.

a further group, led by aerospace researcher Ioannis Nompelis at the tuition of Minnesota (UMN), introduced a mini-software that changed into purposely developed to imitate the US3D CFD solver, which is used for simulating hypersonic flows round geometrically advanced shapes. The crew, AeroUMN, accompanied a forty-fold speedup of the mini-utility—which is named MY3D—from a single compute unit (CPU core) when the usage of the total CPU resource of an Ascent-class node. And when the team employed probably the most six GPU accelerators found on the compute node, MY3D carried out 50 percent better than the usual CPU-most effective version.

For the long run, no longer digital but virtual-inspired

Papatheodore mentioned the organizers intend for the 2021 GPU hackathons to continue in the digital structure.

“I think they have confirmed this 12 months that they can run these events virtually and spot similar results to the in-adult events,” Papatheodore referred to.

Papatheodore emphasized that the hackathons possible gained’t remain virtual invariably. however the organizers plan to maintain some variety of the hackathons virtual even after a return to in-adult pursuits. this could allow members to reap the merits of the new structure, equivalent to increased availability of mentors.

“sooner or later, we’ll probably include some of the most a success points of the virtual hackathons into their in-person pursuits as smartly, so they have invariably modified the layout of those hackathons,” Papatheodore talked about.

UT-Battelle LLC manages alrightRidge national Laboratory for DOE’s office of Science, the single greatest supporter of simple analysis in the actual sciences in the u.s.. DOE’s workplace of Science is working to handle probably the most most urgent challenges of their time. For greater tips, consult with https://energy.gov/science.

click on right here for the whole post.

source: Coury Turczyn and Rachel McDowell, OLCF


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