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000-086 System x High Performance Servers(R) Technical Support V4

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000-086 exam Dumps Source : System x High Performance Servers(R) Technical Support V4

Test Code : 000-086
Test Name : System x High Performance Servers(R) Technical Support V4
Vendor Name : IBM
: 43 Real Questions

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IBM System x High Performance

IBM energy gadget S922: Rack Server Overview and insight | Real Questions and Pass4sure dumps

See the entire record of precise rack server providers

base line:

The IBM power methods S922 server is designed from the ground up for statistics intensive workloads like databases or analytics. it could actually guide a couple of key business information-intensive eventualities, together with mainstream applications, leading-facet HPC workloads and evolving artificial intelligence (AI) tasks.

consumers in search of principal compute energy may still comprehend this key fact: POWER9 options are the groundwork of the area’s first and third fastest supercomputers, the U.S. department of energy’s Summit and Sierra installations.

IBM power servers tend to have a better charge of entry than x86 machines. although, in keeping with a examine through Quark + Lepton, IBM energy systems running IBM I application have 60% decrease total cost of ownership than home windows/SQL Server or X86 primarily based Oracle systems. IBM’s pitch is that there are limits to what commodity architectures can do.

however, if you are expecting surges favorite and don’t have room for downtime, licensing prices, or occasional crashes a far better commercial enterprise structure may well be required.

Product description:

The S922 is a 1 or 2 socket server that presents a large choice of core configurations and up to four TB of memory. Chip core speeds on the four-core are 2.8 to 3.8GHz, on the eight core are three.4 to three.9 GHz and on the ten core are 2.9 to 3.eight GHz. the one socket edition gives up to six PCIe ( 2 x Gen4 and 4 x Gen3) slots and the two socket version provides as much as 9 slots (3 extra Gen4 slots). One slot is used by means of a mandatory Ethernet adapter. reckoning on what is connected, up to three of these slots could be reserved for other purposes. IBM i is just supported on the 6 cores and 8 core processors and is proscribed to 4 cores of IBM i with a utility tier of P10.

vigour techniques are widespread for their RAS (resiliency, availability, serviceability) points. IBM POWER9-based systems are said to deliver up to 10X sooner bandwidth acceleration and 50% superior memory bandwidth than related x86 options. They additionally assist the latest in records transfer technologies, together with PCIe four.0 and novel NVLink and OpenCAPI interfaces. This new server technology comes along with twice the reminiscence footprint than POWER8. alterations within the reminiscence subsystem and using the newest DIMMs enhance price/efficiency.


number of processors:

up to 2

Processors supported:

IBM POWER9 Scale-Out SMT8 processor (12-core, 10-core, eight-core, four-core choices)

Cores per processor:

4, eight,10 cores per socket

maximum processor frequency/cache:

3.9 GHz/512k L2 and 10 MB L3

I/O enlargement slots:

the one socket version offers up to six PCIe ( 2 x Gen4 and 4 x Gen3) slots and the two socket edition offers up to 9 slots (three more Gen4 slots). One slot is used by using a mandatory Ethernet adapter. depending on what's connected, up to three of those slots may be reserved for different functions.

One front USB three.0 ports – Two rear USB three.0 ports – Two HMC 1 GbE RJ45 ports – One device port with RJ45 connector – 1x USB 3.0 front, 2x USB three.0 rear, 2x HMC 1 GB Eth RJ45 ports, one device port with RJ45 connector, 2x high pace 25 Gb/s ports

optimum reminiscence/# slots/speed:

as much as four TB/32 IS RDIMM slots/as much as 2666 Mhz

optimum Persistent memory:


Storage controller:

S922/S924 has two interior direct connected storage connectors, an NVMe card and a SAS card


The electronic capabilities internet portal is a single web entry point that replaces the diverse entry elements historically used to entry IBM internet functions and support. This web portal allows for you to gain simpler entry to IBM supplies for counsel in resolving technical complications. The newly better My programs and premium Search features make it even less complicated for digital service Agent-enabled shoppers to track equipment stock and find pertinent fixes.

My techniques provides useful studies of put in hardware and utility using tips accrued from the techniques by using IBM electronic provider Agent. experiences are available for any device associated with the client's IBMid. top rate Search combines the function of search and the price of digital carrier Agent advice, offering superior search of the technical aid knowledgebase.

“it's a transparent choice in case you have already got a longtime IBM AIX atmosphere and need to sustain compatibility and maintain performance. There are comparable options now which may be capable of get you to three nines for 1/2 the expenses,” observed a Senior supervisor of IT within the manufacturing industry. 

Key markets and use circumstances:

IBM energy techniques S922 server conveniently integrates into an organization’s cloud & cognitive approach and promises advanced rate performance for mission important workloads.

POWER9 is designed from the ground up for records intensive workloads like databases or analytics


20 core, 512 GB, $37,222. The utility is expensive.

“it's a product with excessive efficiency, efficiency and financial indices in the IT market,” mentioned an applications Engineering within the schooling business. "Deployment is very handy, but took greater than three months. It proved low-priced in the long run.”


IBM vigour S922

Max Processor Frequency

three.9 GHz/512k L2 and 10 MB L3

Max Persistent memory


form ingredient


Max Processors

2 POWER9 Scale-Out SMT8

Max memory

4 TB

Max Storage

four TB



Key Differentiator

properly processing power

IBM Bets $2B in quest of 1000X AI Hardware performance boost | Real Questions and Pass4sure dumps

For now, AI methods are primarily laptop researching-based and “slender” – powerful as they are by today’s specifications, they’re confined to performing a couple of, narrowly-described tasks. AI of the next decade will leverage the enhanced energy of deep discovering and develop into broader, fixing a improved array of more complex problems. in addition, the popular-aim technologies used these days for AI deployments will get replaced by way of a technology stack that’s AI-selected and exponentially sooner – and it’s going to take some huge cash.

IBM’s Mukesh Khare

in quest of to take center stage in AI’s unfolding, IBM – in combination with ny state and a few know-how heavies – is investing $2 billion in the IBM research AI Hardware core, concentrated on constructing next era AI silicon, networking and manufacturing with a view to, IBM pointed out, bring 1,000x AI efficiency effectivity growth over the subsequent decade.

“nowadays, AI’s ever-increasing sophistication is pushing the boundaries of the trade’s present hardware techniques as users locate extra easy methods to comprise a considerable number of sources of statistics from the side, internet of issues, and extra,” stated Mukesh Khare, VP, IBM research Semiconductor and AI Hardware neighborhood, in a weblog asserting the assignment. “…nowadays’s programs have achieved more advantageous AI efficiency by means of infusing computing device-studying capabilities with high-bandwidth CPUs and GPUs, really good AI accelerators and excessive-performance networking equipment. To retain this trajectory, new thinking is needed to accelerate AI efficiency scaling to suit to ever-expanding AI workload complexities.”

IBM roadmap for 1,000x development in AI compute efficiency effectivity.

IBM pointed out the middle might be the nucleus of a new ecosystem of research and industrial companions participating with IBM researchers. partners introduced today consist of Samsung for manufacturing and research, Mellanox technologies for high-efficiency interconnect equipment, Synopsys for application systems, emulation and prototyping, and IP for developing high-efficiency silicon chips, and semiconductor machine organizations utilized substances and Tokyo Electron.

Hosted at SUNY Polytechnic Institute, Albany, big apple, in collaboration with neighboring Rensselaer Polytechnic Institute center for Computational innovations, IBM said the enterprise and its companions will “enhance more than a few technologies from chip stage instruments, materials, and architecture, to the application supporting AI workloads.”

big Blue spoke of analysis at the core will center of attention on overcoming current computing device-researching boundaries through approaches that consist of approximate computing via Digital AI Cores and in-memory computing via Analog AI Cores. These technologies will provide the thousand-fold raises in performance efficiency required for full attention of deep learning AI, in response to IBM.

“Our analog AI cores are part of an in-memory computing approach in efficiency effectivity which improves via suppressing the so-known as Von Neuman bottleneck through eliminating information transfer to and from memory,” noted IBM. “Deep neural networks are mapped to analog pass point arrays and new non-unstable material characteristics are toggled to shop network parameters within the move aspects.”

“A key area of research and development will be techniques that meet the calls for of deep studying inference and training processes,” Khare observed. “Such systems present colossal accuracy advancements over more normal computing device getting to know for unstructured facts. these severe processing demands will grow exponentially as algorithms develop into more advanced so as to deliver AI methods with improved cognitive abilities.”

Khare mentioned the analysis middle will host R&D, emulation, prototyping, trying out and simulation activities for brand new AI cores peculiarly designed for practicing and deploying superior AI models, together with a verify bed in which contributors can demonstrate innovations in precise-world applications. really good wafer processing for the middle could be executed in Albany with some support at IBM’s Thomas J. Watson analysis center in Yorktown Heights, new york.

Settling In With IBM i For The long Haul | Real Questions and Pass4sure dumps

February eleven, 2019 Timothy Prickett Morgan

If nothing else, the IBM i platform has exhibited superb durability. One may even say legendary sturdiness, if you need to take its historical past the entire manner returned to the equipment/three minicomputer from 1969. this is the precise starting factor in the AS/400 family unit tree and here's when big Blue, for very sound felony and technical and advertising and marketing motives, determined to fork its products to handle the exciting needs of large companies (with the equipment/360 mainframe and its observe-ons) and small and medium companies (starting with the equipment/3 and moving on through the system/34, system/32, system/38, and device/36 in the 1970s and early Nineteen Eighties and passing throughout the AS/400, AS/400e, iSeries, gadget i, after which IBM i on vigor systems platforms.

It has been an extended run certainly, and a lot of customers who've invested in the platform began means back then and there with the early types of RPG and moved their purposes ahead and adjusted them as their organizations developed and the depth and breadth of corporate computing modified, relocating on up through RPG II, RPG III, RPG IV, ILE RPG, and now RPG free form. Being on this platform for even three many years makes you a relative newcomer.

there is a longer run ahead, since they accept as true with that the corporations that are still operating IBM i techniques are the real diehards, the ones who don't have any intention of leaving the platform and that, at the least according to the survey facts we've been privy too, are desiring to proceed investing in, or even expand their investments in, the IBM i platform.

to date, they don't seem to be in a recession and heaven willing there usually are not one, so the priorities that IBM i retail outlets have don't seem to be those that they'd a decade in the past all the way through the peak of the awesome Recession. back then, as changed into the case in just about all IT organizations, IBM i shops were hunkering down and had been trying to cut costs in all ways possible, including deferring equipment improvements and migrations as well as cutting lower back on other initiatives. best 29 percent of the 750 IBM i retail outlets that participated in the 2019 IBM i industry Survey, which HelpSystems did again in October 2018, were involved about cutting back IT spending. this is a remarkably low stage, and i feel is indicative of how quite strong the financial system is – excepting one of the vital suits and begins they saw at the conclusion of 2018 and right here in early 2019 that make us frightened and will delivery placing power on issues. here are the appropriate concerns as culled from the survey:

dealing with the growth in facts and in deciding the analytics to chew on that records ranked a bit bit greater on the 2019 IBM i market Survey than did cutting back prices, and that i feel over the lengthy haul these concerns will turn into extra critical than modernizing functions and dealing with the IBM i knowledge shortages which are a perennial be anxious. both of these issues are being solved as new programmers and new equipment to make new interfaces to database functions are becoming extra general and as applied sciences similar to free kind RPG, which appears more like Java, Python, and Hypertext Preprocessor, are being extra broadly deployed and, importantly, can also be picked up extra straight away by using programmers skilled with these different languages.

Given the nature of the customer base, it appears unlikely to me that protection and excessive availability will now not proceed to be primary considerations, despite the fact that the IBM i platform is among the many most at ease systems on the earth (and not simply because it is vague, however because it is quite tricky to hack) and it has a number of high availability and disaster restoration equipment (from IBM, Syncsort, Maxava, and HelpSystems) obtainable for those that want to double up their programs and give protection to their purposes and records. The bar is commonly bigger than standard backup and restoration for many IBM i stores in the banking, coverage, manufacturing, and distribution industries that dominate the platform. These organizations can’t have protection breaches, and they can’t have downtime.

there is a astonishing amount of steadiness in the IBM i customer base that they consider, at this element, is reflective within the stability of the IBM i platform and large Blue’s personal belief that it wants a healthy IBM i platform to have an average match vigour methods enterprise. they all comprehend that the vigour programs hardware enterprise has simply grew to become in five quarters of revenue boom – anything they mentioned recently in constructing their own earnings model for the energy methods enterprise – but what they did not be aware of, and what be sure to recognize, is that in the 2nd and third quarters of 2018, the IBM i component of the business grew greatly sooner than the standard vigour programs business, and the simplest intent that this did not turn up in the closing quarter of 2018 is that revenue of IBM i machinery in q4 2017 became fairly mighty and represented a really tough examine. The point is, the IBM i enterprise has been raising the energy programs classification average. (These guidelines in regards to the IBM i company come compliments of Steve Sibley, vice president and offering manager of Cognitive techniques at IBM.)

IBM’s personal economic steadiness of the vigour platform – which has been bolstered by way of a move into Linux clusters for analytics and high performance computing simulation and modeling in addition to through the adoption of the HANA in-memory database with the aid of SAP consumers on large iron machines together with Power8 and now Power9 programs – helps IBM i shoppers think extra assured in investing within the current IBM i platform. The fresh proof from a couple of different surveys, now not just the one completed by using HelpSystems each year, means that companies are through and massive either continuing to make investments within the platform and even in some instances are planning to increase their spending on the IBM i platform in 2019.

As you could see, the pattern of investment plans for the IBM i platform, as proven in the chart above, has no longer modified very tons at all in the past four years. it's a remarkably stable pattern with but a little wiggling here and there that can also now not even be statistically big. simply under 1 / 4 of IBM i stores have reported in the past four years that they plan to increase their investment in the platform in each year, and simply beneath half say that they are maintaining regular. This doesn't mean that the same corporations, 12 months after yr, are investing more and different organizations are staying pat, yr after 12 months. it is far more possible that each handful of years – more like 4 or 5 – clients improve their programs and extend their capacity, and they then sit tight. The ask yourself is that the break up isn’t displaying a long way fewer groups investing and far greater sitting tight. That greater than a tenth of the retail outlets don’t understand what their plan is as each and every prior 12 months comes to a close is a bit of disturbing, nevertheless it is sincere and suggests that a good portion of outlets produce other priorities apart from hardware and operating system improvements. they have talked about this before and they will say it again: They believe that the individuals who respond to surveys and read weekly publications focused on the IBM i platform are essentially the most active retail outlets – the ones greater prone to dwell exceptionally latest on hardware and utility. So the tempo of adoption for brand new applied sciences, and the fee of funding, should be higher than within the specific base, much of which doesn't alternate much in any respect.

So if they needed to alter this statistics to take on the whole base, there might be some distance fewer sites that are investing greater funds, much more agencies that are sitting tight, and perhaps fewer websites that are contemplating relocating off the IBM i platform. I suppose the distribution of data is likely anything like 10 % of outlets have no thought what they're doing investment wise with IBM this year, 5 percent are brooding about relocating some or all of their applications to a further platform, probably 10 percent are investing more this yr, and the ultimate seventy five p.c are sitting tight. here is only a wager, of direction. so far as they will tell, the fee of attrition – what number of sites they definitely lose every 12 months – just a tad over 1 percent. So the expense of circulation of applications off the platform, or incidences of unplugging IBM i databases and purposes, may additionally no longer be any place close as high in the common base because the records above suggests. what's alarming, possibly, is that the cost of moving some or all purposes off the platform is balanced in opposition t those who say they'll increase investments. perhaps these are hopeful survey takers, and people who feel it is effortless to movement locate it is not and people who believe they're going to discover the funds to invest will no longer.

What they do know is that if the expense of utility attrition changed into anywhere close as high as these surveys imply, then the IBM i company would no longer be turning out to be, but shrinking. And they realize it isn't shrinking, so they think there is a disconnect between planning and reality, both on the upside and the downside.

if you drill down into the records for the 2019 IBM i industry Survey, there were 13 p.c of outlets that noted they would be moving some functions to a brand new platform, and yet another 9 p.c that said they had been going to stream all of their purposes off IBM i. (This quantity is in line with the contemporary ALL400s survey finished by way of John Rockwell.)

Anyway, respectable good fortune with that.

Porting functions from one platform to an extra, of purchasing a brand new suite on that new platform, is an quite elaborate task. It is not like attempting to trade a tire whereas driving down the highway, as is a common metaphor, however fairly like trying to take the tire off one car relocating down the toll road and setting up it on one other automobile riding beside it within the adjoining lane with out crashing both vehicle or smashing into any one else on the highway. Optimism abounds, however when push involves shove, very few corporations are attempting this kind of maneuver, and after they do, it is usually because there's a company mandate, more instances than not caused by way of a merger or acquisition, that pits any other platform in opposition t IBM i working on vigour techniques. organizations that say they are making such a stream off IBM i are sanguine for their personal personal factors, perhaps, but they aren't necessarily simple about how lengthy it may take, what disruption it'll can charge, and what most excellent advantage, if any, could be realized.

if you do the maths on the chart above, eight-tenths of the bottom has no idea how lengthy a movement will take, one other 1.7 percent thinks it's going to take greater than five years, and three p.c say it'll take between two years and 5 years. only three.four p.c of the overall base say they can do it in under two years. They suppose all of these numbers are positive, and the organizations who could readily depart OS/400 and IBM i already did a very long time ago and people that are remain have a harder time, now not an easier time, relocating. If this have been not true, the IBM i base could be a hell of a whole lot smaller than the 120,000 consumers they believe are obtainable, in accordance with what big Blue has advised us in the past. here is the difference between fear or pressure or culture and the reality of making an attempt to circulation a company off one platform and onto an extra. These strikes are all the time an awful lot more durable than they seem on the entrance conclusion, and they suspect many of the advantages additionally don’t materialize for those that do jump structures.

at the ordinary attrition expense counseled by using this survey information – 9 percent circulate off the platform in someplace between three hundred and sixty five days and greater than 5 years, with most businesses no longer being able to see greater than five years into the longer term this is a neat trick – the installed base would reduce dramatically. it is hard to claim how far because of the big selection of timeframes in the survey. If it turned into 9 percent of the base within two years – name it four.5 p.c of the bottom per year – then inside a decade the normal base would reduce from one hundred twenty,000 IBM i sites worldwide down to about seventy two,000. this could dramatic indeed. but at a 1 % attrition fee per 12 months, the base remains at 107,500 entertaining purchasers (not websites and not installed machines, both of that are greater) with the aid of 2029. They think there's each possibility that the attrition price will in fact slow and drop under 1 percent as IBM demonstrates dedication to the vigour techniques platform and its IBM i working system. There are at all times some new shoppers being added in new markets, to make certain, however the bleed cost (in spite of the fact that it is small) is still probably an order of magnitude better than the feed price.

once they do consider about making the movement, IBM i retail outlets know precisely the place they want to go, and this reply has been step by step altering through the years: Linux as an alternative choice to IBM i is on the upward thrust and windows Server as an alternative is on the wane. in the latest survey, fifty two percent of the agencies that referred to they were moving all or some of their purposes to yet another platform said they have been picking windows Server, whereas 34 % selected Linux. This displays the relative popularity of home windows Server and Linux in the datacenters of the area at tremendous, and might be tipped just a bit greater heavily towards Linux in comparison to the relaxation of the area. curiously, 10 percent of those polled who spoke of they had been moving had been AIX systems, and a further four percent had been going upscale to gadget z mainframes – as unlikely as this may additionally seem. platforms are likely to roll downhill; they do not continually defy gravity like that.

The factor about such surveys is that they exhibit intent, now not motion. They commonly intend to do a lot more than they definitely can accomplish, and moving platforms after spending a long time of build up competencies is not constantly a very sensible flow except the platform is in precise quandary – like the Itanium programs from Hewlett Packard business operating OpenVMS or HP-UX or the HP 3000s running MPE or the Sparc techniques from Oracle working Solaris. These had been once first rate systems with huge put in bases and colossal profits streams, however now, IBM is the closing of these Unix and proprietary platforms with its power systems line. And it's with the aid of a ways the biggest and for sure the only 1 showing any increase.

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System x High Performance Servers(R) Technical Support V4

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When Databases Meet FPGA: Achieving 1 Million TPS With X-DB Heterogeneous Computing | real questions and Pass4sure dumps

X-Engine is a new generation storage engine developed by Alibaba Database Department and is the basis of the distributed database X-DB. To achieve 10 times the performance of MySQL and 1/10 the storage cost, X-DB combines software with hardware to make full use of the most cutting-edge technical advantages in both software and hardware fields.

FPGA acceleration is their first attempt in the custom computing field. At present, the FPGA-accelerated X-DB has been subject to small-scale online grayscale release. FPGA will assist X-DB in the 6.18 and Double 11 shopping carnivals this year and will meet Alibaba's business departments' high database performance requirements.

Overview of Alibaba's X-Engine

Owning the world's largest online transaction website, Alibaba's OLTP (online transaction processing) database system needs to satisfy high-throughput service requirements. According to their statistics, several billion records get written into their OLTP database system on a daily basis. During the 2017 Double 11 (Singles' Day) shopping carnival, the system's peak throughput reached 10 million TPS (transactions per second). Alibaba's business database systems mainly have the following characteristics:

  • High transaction throughput and low latency in write and read operations.
  • Write operations make up a relatively high proportion in comparison to that of traditional databases; the read to write workload ratio usually is more than 10:1. However, the number for Alibaba's transaction system reached 3:1 on the day of the 2017 Double 11 shopping carnival.
  • Data access hotspots are relatively concentrated. A newly written data record will be accessed mainly (99%) within the first seven days, and the possibility it may be accessed later is extremely low.
  • To meet Alibaba's stringent requirements on performance and cost, they have designed a new storage engine; it is called X-Engine. They have used many cutting edge database technologies in X-Engine; these include highly-efficient memory index structures, asynchronous write assembly-line processing mechanism, and optimistic concurrency control for in-memory databases.

    To achieve the best write performance and facilitate the separation of cold and hot data for tiered storage, X-Engine has borrowed the design of LSM-Tree. X-Engine maintains multiple memtables in its memory. It appends all newly written data to these memtables, rather than directly replacing existing records. As the data storage is relatively large, it is impossible to store all data in memory.

    When data in memory reaches a specified volume, they flush it to the persistent storage to form an SSTable. To reduce latency in read operations, X-Engine regularly schedules compaction tasks to compact SSTables in the persistent storage. X-Engine merges key-value pairs in multiple SSTables by keeping only the latest version of key-value pairs if multiple versions exist (all key-value pair versions currently referenced by transactions will also be kept).

    Based on the characteristics of data access, X-Engine applies tiered storage to persistent data, where they store active data in relatively high data layers, and merge less active data (seldom accessed) with base-layer data and store it in the base-layer. It compresses base-layer data at a high compression rate and migrates it to storage media featuring large capacity but the relatively low price (such as SATA HDDs) to achieve the goal of storing a large quantity of data at a relatively low cost.

    In this case, tiered storage creates a new problem: the system must frequently compact data, and the larger number of data writes requires more frequent compaction processes. Compaction is a compare and merge process which requires high consumption of CPU and storage I/O. In high-throughput write cases, a large number of compaction operations will occupy a large number of system resources. This can surely cause the performance of the entire system to drop tremendously thus leading to a huge impact on the application system.

    The completely new X-Engine has extraordinary multi-core expansion capability to achieve very high performance. Its front-end transaction alone can almost completely consume all CPU resources, and it has a much higher resource using efficiency than InnoDB. They have shown the comparison between the two in the following figure:

    Image title

    At such a performance level, the system does not have any other resources for compaction operations; otherwise, performance levels will drop.

    Based on their testing results, in DbBench benchmark's write-only scenario, the system periodically suffers from performance jitter. When a compaction task occurs, the system performance drops by more than 40%, and when the compaction task ends, the system performance returns to normal. They have shown this behavior in the following figure:

    Image title

    However, if they do not conduct compaction promptly, the accumulation of multi-version data can seriously affect the read operations.

    To solve the performance jitter caused by compaction, academic experts have put forward many structures such as VT-tree, bLSM, PE, PCP, and dCompaction. Although these algorithms can optimize the compaction performance across multiple aspects, they cannot reduce consumption of CPU resources by compaction. Based on relevant research statistics, when using SSD storage devices, the computing operations of compaction in the system consumes approximately 60% of computing resources. Therefore, no matter what optimizations they implement for compaction in the software layer, for all LSM tree-based storage engines, performance jitter caused by compaction is always an Achilles' heel.

    Fortunately, special hardware opens a new door for solving performance jitter caused by compaction. In fact, it has become a trend to use special hardware in solving traditional databases' performance bottlenecks. They have already offloaded database operations such as Select and Where to FPGA, and more complex operations such as Group By are under research. However, the current FPGA acceleration solutions have a couple of drawbacks:

  • The current acceleration solutions are designed for the SQL layer; FPGA is generally placed between storage and host and is used as a filter. Although, researchers have made numerous attempts to use FPGA to accelerate the OLAP system, the FPGA acceleration design for the OLAP system remains a challenge.
  • While FPGA's chip size is getting smaller and smaller, FPGA's internal errors such as single event upset (SEU) pose greater and greater threats to FPGA reliability. For a single chip, the probability of internal error is 3-5 years. Therefore, the fault tolerance mechanism design becomes vitally important for systems in need of large-scale availability.
  • To ease the impact of compaction on X-Engine's system performance, they have used an asynchronous hardware device FPGA, rather than the CPU to complete the compaction operation. This approach is crucial for a storage engine that satisfies stringent service requirements by maintaining the overall system performance at a high-level and avoiding performance jitters. Here are the major design features:

  • Highly efficient design and implementation of FPGA compaction: Using streamlined compaction operations, FPGA compaction achieves a processing performance 10 times the CPU single-thread processing performance
  • Hybrid storage engine's asynchronous scheduling logic design: As FPGA can complete compaction's link requests in milliseconds, using a traditional synchronous scheduling method will block a large number of compaction threads and cause heavy thread-switching cost. Through asynchronous scheduling, they have successfully reduced the thread-switching cost and improved the system's engineering availability.
  • Fault tolerance mechanism design: As limits of entered data and FPGA internal errors may cause a rollback of some compaction tasks, to ensure data integrity, all tasks that have been rolled back by FPGA will be re-executed by the equivalent CPU compaction threads. The fault tolerance mechanism design as described in this article meets Alibaba's actual business requirements and avoids FPGA's internal instability.
  • X-Engine Compaction

    X-Engine's storage structure contains one or multiple memory buffer areas (memtable), and multilayer persistent storage L0, L1... Each layer contains multiple SSTables.

    Image title

    When memtable is full, it turns into an immutable memtable and then flushes to an SSTable to L0. Each SSTable contains multiple data blocks and one index block to index the data block. When it reaches the maximum number of L0 files, it triggers the merge of SSTables that have the overlapped key ranges; this process is called compaction. Likewise, when they reach the maximum number of SSTables at a layer, it merges with lower layer data. In this way, cold data constantly flows downward while hot data remains at a relatively higher layer.

    We can specify a range of key-value pairs that merge during a compaction process and this range may contain multiple data blocks. Generally, a compaction process involves merging data blocks between two adjacent layers. However, they need to pay special attention to compaction tasks between L0 and L1. This is because as SSTables in L0 directly flushes from the memory, keys of SSTables in this layer may get overlapped. Therefore, a compaction task between L0 and L1 may involve merging multiple data blocks.

    Image title

    For read operations, X-Engine needs to search for the required data from all memtables. If it fails to find the data in memtables, it searches in the persistence storage, from higher to lower layers. As a result, timely compaction operations not only shorten the read path but also save the storage space. However, this method uses a lot of system computing resources and causes performance jitter. This is an urgent problem that X-Engine must solve.

    FPGA Accelerated Database

    From the perspective of the existing FPGA accelerated databases' status quo, they can divide FPGA accelerated database architectures into two types; the bump-in-the-wire design and the hybrid design. In the early stage, because of the FPGA card's insufficient memory resources, the former type of architecture is relatively popular. In this architecture, they place FPGA on the storage data path and use the host as a filter. The advantage is that it requires zero data replication, while the drawback is that the acceleration operation must be a part of the streamlined process, therefore making it not flexible enough in terms of the design method.

    The latter architecture design uses FPGA as a coprocessor, where they have connected FPGA to host via PCIe and use the DMA method for data transmission. As long as the offloading computation is intensive enough, data transmission costs are acceptable. The hybrid architecture design allows more flexible offloading methods. For complex operations such as compaction, data transmission between FPGA and host is necessary. Therefore, they have used the hybrid architecture design for hardware acceleration in their X-Engine.

    Image title

    System Design

    In traditional LSM-tree-based storage engines, CPU is responsible for handling normal user requests, as well as the scheduling and execution of compaction tasks. In other words, CPU is both the producer and consumer of compaction tasks. However, in a CPU-FPGA hybrid storage engine, CPU is only responsible for producing and scheduling compaction tasks. In this method, they need to offload the execution of compaction tasks to the special hardware (FPGA).

    Image title

    For X-Engine, handling of normal user requests is similar to that of LSM-tree-based storage engines:

  • A user submits a request to operate on a specified KV pair (Get/Insert/Update/Delete). In the case of a write operation, a new record appends to a memtable.
  • When a memtable reaches its maximum size, it turns into an immutable memtable.
  • The immutable memtable then turns into an SSTable and flushes to the persistent storage.
  • When L0 reaches the maximum number of SSTables, compaction gets triggered. They can divide offloading of a compaction task into the following steps:

  • CPU splits Load SSTables (that need to be compacted from the persistent storage) into multiple compaction tasks at the granularity of data blocks following the metadata, and pre-allocates memory space for computation result of each compaction task. Consequently, it pushes each successfully created compaction task into the Task Queue for FPGA to execute.
  • CPU reads the status of Compaction Units on FPGA and allocates compaction tasks from the Task Queue to available Compaction Units.
  • It transmits Input data to FPGA's DDR via DMA.
  • A Compaction Unit executes the compaction task and transmits the computation result via DMA back to the host; it attaches a return code to indicate the status of this compaction task (fail or success). Next, it pushes the compaction results of finished tasks to the Finished Queue.
  • The CPU checks the compaction result status in the Finished Queue. If a compaction task fails, the CPU executes it again.
  • It flushes the compaction results to storage.
  • Detailed Design FPGA-Based Compaction

    Compaction Units (CU) are the basic unit for FPGA to execute compaction tasks. An FPGA card can place multiple CUs, and each CU is composed of the following modules:

    Image title

    1. Decoder: In X-Engine, they store a KV in the data block after compression and encoding. The primary function of the Decoder module is to decode KV pairs. Each CU contains 4 Decoders, and a CU support a compression task of a maximum of 4 KV pairs. They need to split the compression tasks that require compression of more than 4 KV by the CPU. Based on their assessment, most compression tasks involve less than 4 KV pairs. They have placed 4 Decoders based on their considerations of performance and hardware resources. Comparing the configuration with 2 Decoders, we've increased 100% hardware consumption but obtained 300% performance improvement.

  • KV Ring Buffer: KV pairs decoded by the Decoder module get temporarily stored in KV Ring Buffer. Each KV Ring Buffer maintains a read indicator (maintained by the Controller module) and a write indicator (maintained by the Decoder module). KV Ring Buffer maintains three signals to indicate the current status: FLAG_EMPTY, FLAG_HALF_FULL, and FLAG_FULL. If FLAG_HALF_FULL is at a low level, the Decoder module will continue decoding KV pairs. Conversely, the Decoder module will stop decoding KV pairs until downstream consumers in the pipeline have consumed the decoded KV pairs.
  • KV Transfer: This module is responsible for transmitting keys to Key Buffer. Because merging KV pairs only involve comparison of key values, the values do not need to be transmitted. They can track the currently compared KV pairs by using the read indicator.
  • Key Buffer: This module stores keys of each KV pair that need to be compared. When all keys that need to be compared have been transmitted to the Key Buffer, the Controller notifies the Compaction PE to compare them.
  • Compaction PE: The Compaction Processing Engine (compaction PE) is responsible for comparing key values in Key Buffer. Comparison results are sent to the Controller, and then the Controller sends a notice to KV Transfer to transmit the corresponding KV pair to the Encoding KV Ring Buffer for the Encoder module to encode them.
  • Encoder: The Encoder module is responsible for encoding KV pairs from the Encoding KV Ring Buffer into a data block. If the data block reaches its maximum size, then the current data block gets flushed to DDR.
  • Controller: The Controller acts as a coordinator in CU. Although the Controller is not a part of the compaction pipeline, it plays a key role in each step of the compaction pipeline design.
  • A compaction process contains three key steps: decoding, merging, and encoding. The most significant challenge for designing a proper compaction pipeline is that the execution time for each step varies significantly. For example, because of parallel processing, the throughput of the decoder module is much higher than the encoder module. Therefore, they must suspend some fast modules to wait for downstream modules still in the pipeline. To match the throughput differences in each of the pipeline's modules, they have designed a Controller module to coordinate different steps in the pipeline. An additional benefit of this design is that it decouples each module in the pipeline and enables more flexible development and maintenance during engineering implementation.

    Image title

    When integrating FPGA compaction into X-Engine they hope to have independent CU throughput performance; the baseline of the experiment is the CPU.

    Single-core compaction thread (Intel(R) Xeon(R) E5-2682 v4 CPU with 2.5 GHz)

    Image title

    We can draw the following three conclusions from the experiment:

  • n all KV lengths, FPGA compaction has a higher throughput than that of a single-thread CPU; this proves the feasibility of compaction offload;
  • With the increase of KV lengths, FPGA compaction throughput reduces. This is because the lengths of bytes that need to be compared have increased, resulting in the increase of cost for comparison.
  • The acceleration rate (FPGA throughput / CPU throughput) increases with the value length. This is because when the KV length is short, it requires frequent communication and status checking among different modules; this means a relatively high cost in comparison with normal pipeline operations.
  • Asynchronous Scheduling Logic Design

    Because a link request in FPGA is completed in milliseconds, using the traditional synchronous scheduling method will cause high thread switching costs. Based on FPGA's characteristics, they have redesigned an asynchronous scheduling compaction method, where:

  • The CPU is responsible for building compaction tasks and pushing them into the Task Queue.
  • A thread pool is maintained to distribute compaction tasks to specified CUs.
  • When a compaction task is finished, it will be pushed to the Finished Queue.
  • The CPU will then check the task execution status, and schedule CPU compaction threads to re-execute the failed compaction tasks.
  • Asynchronous scheduling significantly reduces the thread-switching cost of CPU.

    Image title

    Fault Tolerance Mechanism Design

    For FPGA compaction, the following three reasons can lead to the failure of compaction task:

  • Data gets damaged during the transmission process: Calculate the CRC values of data before and after transmission, and compare the values. If these two CRC values are inconsistent, it means that the data is damaged.
  • FPGA internal errors (bit upset): To solve this problem, they have attached an additional CU to each CU. They can compare the computation results of both CUs and any inconsistency in the results will indicate that a bit upset error has occurred.
  • Input data of a compaction task is invalid: To facilitate FPGA compaction design, they have set a restriction on the length of KVs. The compaction tasks for KVs that exceed the maximum allowable length are identified as invalid tasks.
  • To ensure the data is correct, the CPU will conduct computation again on all failed tasks. As they mentioned earlier in the fault tolerance mechanism, they have addressed a small part of compaction tasks that exceed the limits and have avoided the risk of FPGA internal errors.

    Image title

    Experiment Results Lab environment
  • CPU: 64-core Intel (E5-2682 v4, 2.50 GHz) processor
  • Memory: 128 GB
  • FPGA card: Xilinix VU9P
  • memtable: 40 GB
  • block cache 40 GB
  • We compared the performance of two storage engines:

  • X-Engine-CPU: compaction operation executed by CPU
  • X-Engine-FPGA: compaction offloaded to FPGA for execution
  • DbBench

    Image title

    Result analysis:

  • n a write-only scenario, X-Engine-FPGA sees a 40% throughput increase. From the performance curve they can tell that when compaction begins, the performance of X-Engine-CPU drops by 1/3.
  • FPGA compaction has a higher throughput and is faster, so the read path is shortened faster. Therefore, in the read/write hybrid scenario, X-Engine-FPGA throughput increases by 50%.
  • The throughput in the read/write hybrid scenario is smaller than that of the write-only scenario. Read operations require access to data stored in persistent layers which brings in I/O cost and affects the overall throughput performance.
  • These two performance curves represent two different compaction statuses. In the left figure, the system performance jitters periodically meaning that the compaction operation is competing with normal transaction handling threads for CPU resources; while in the right figure, X-Engine-CPU's performance maintains at a low-level meaning that the compaction speed is smaller than the write speed, causing accumulation of SSTables. Compaction tasks are subject to constant scheduling at the backend.
  • CPU schedules the Compaction tasks. That's why X-Engine-FPGA's performance also jitters and the curve is not smooth.
  • YCSB

    Image title

    Result analysis:

  • On YCSB benchmark, due to the influence of compaction, X-Engine-CPU's performance decreases by approximately 80%. However, for X-Engine-FPGA, its performance only sees a fluctuation of 20% due to the influence of the compaction scheduling logic.
  • The check-unique logic introduces read operations. With the increase in pressure testing time, the read path becomes longer, and the performance of both storage engines decreases with time.
  • In the write-only scenario, X-Engine-FPGA's throughput increases by 40%. However, with the increase in the read/write ratio, the acceleration effect of FPGA Compaction decreases gradually. When the read/write ratio becomes higher, the write pressure becomes smaller, and the SSTable accumulation becomes slower thus reducing the number of threads that handle compaction tasks. Therefore, X-Engine-FPGA sees a more obvious performance increase in write-intensive workloads.
  • With the increase in the read/write ratio, the throughput increases. When write throughput is smaller than that of the KV interface, the cache miss ratio is relatively low, thus avoiding frequent I/O operations. With the increase in the proportion of write operations, the number of threads that handle compaction tasks also increases, thus reducing the system's throughput capability.
  • Image title

    Result analysis:

  • With FPGA acceleration, X-Engine-FPGA's performance improves by 10%–15% when the number of connections is increased from 128 to 1024. When the number of connections increases, the throughput of both systems gradually decreases because of the lock competition of hotspot rows increases.
  • TPC-C's read/write ratio is 1.8 : 1. In the experiment, under the TPC-C benchmark, more than 80% of CPU resources were consumed on SQL resolution and lock competition of hotspot rows. The actual write pressure was not very heavy. Based on their observation in the experiment, the number of threads that execute compaction tasks in the X-Engine-CPU is no more than three (a total of 64 cores). Therefore, FPGA's acceleration effect is not as obvious as the previous instances.
  • SysBench

    We have included testing for InnoDB in this experiment (buffer size = 80 GB)

    Image title

    Result analysis:

  • X-Engine-FPGA improves more than 40% of throughput performance. Because SQL resolution consumes a large number of CPU resources, the throughput of DBMS is smaller than that of the KV interface.
  • X-Engine-CPU reaches a balance at a low level. Because the compaction speed is slower than the writing speed, SST files are accumulated, and compaction is constantly scheduled.
  • X-Engine-CPU's performance is twice that of InnoDB, which shows the advantage of LSM tree-based storage engines in a write-intensive scenario;
  • In comparison with the TPC-C benchmark, Sysbench is more similar to Alibaba's real transaction scenario. For a transaction system, most queries are data insertion queries and simple point queries and seldom involve range queries. A decrease in hotspot row conflicts causes the number of resources consumed in the SQL layer to decrease. During the experiment, they have observed that for X-Engine-CPU, when more than 15 threads are used to execute compaction tasks, the performance improvement brought by FPGA acceleration is very obvious.
  • Conclusion

    In this article, the X-Engine storage engine accelerated by FPGA brings 50% performance improvement for the KV interface, and 40% performance improvement for the SQL interface. With the decrease in the read/write ratio, FPGA's acceleration effect becomes more obvious, thus meaning that FPGA compaction acceleration is suitable for write-intensive workloads. This is consistent with the intention of the LSM-tree design. Also, they have avoided FPGA's internal defects by designing a fault tolerance mechanism, and we've finally created a high-availability CPU-FPGA hybrid storage engine that meets Alibaba's real service requirements.

    It is the first real project that uses a heterogeneous computing device introduced by X-DB to accelerate core database functions. Based on their experiences, FPGA can completely meet the computing demands raised by X-Engine's compaction tasks. At the same time, they have been researching to schedule more suitable computing tasks to FPGA for execution, such as compression, BloomFilter generation, and SQL JOIN operators. At present, the R&D for the compression function is completed, and it will be built into a set of IP together with Compaction to perform data compaction and compression operations simultaneously.

    X-DB FPGA-Compaction hardware acceleration is an R&D project completed by three parties; these parties are respectively the Alibaba Database Department database kernel team, the Alibaba Server R&D Department custom computing team, and Zhejiang University. Xilinx's technical team has also made great contributions to the success of this project. They hereby extend their gratitude to them. They will post X-DB online for public beta this year. You will then be able to experience the significant performance improvement with FPGA acceleration to X-DB.

    Buy server hardware with these key functions in mind | real questions and Pass4sure dumps

    When the time comes to buy server hardware, there are a lot of factors to consider, such as the number of processors, the available memory and the total storage capacity. Buyers should closely evaluate eight important features when comparing the servers available from the leading vendors.

    These eight features cover the basic components to look for to buy server hardware, but they don't represent all the features that buyers should consider. Decision-makers at every organization must determine exactly what they need to support their existing and future workloads, keeping in mind the differences between rack, blade and mainframe computers.

    Companies should view these eight features as the starting point to identify their requirements and evaluate the available products and should expand their research as necessary to ensure they're addressing every concern.


    One of the most important components to consider when buying server hardware is the processor that carries out the data computations. Also referred to the central processing unit (CPU), the processor does all the heavy lifting when it comes to running programs and sifting through data. Most servers run multiple processors, usually with one per socket. However, a processor can also be made up of multiple cores to support multiprocessing capabilities.

    Multiple cores usually translate to better performance, but the number of cores is not the only factor to consider. Buyers should also consider the processor speed -- CPU clock speed -- and available cache, as well as the total number of sockets, as these can differ significantly from one processor to the next.

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    Adequate server memory is essential to a high-performing system, and the more memory that is available, the better the workloads are likely to perform. However, other factors can also contribute to performance, such as the memory's speed and quality. Most server memory is made up of dual in-line memory module integrated circuit boards with some type of random-access memory.

    Companies should view these eight features as the starting point to identify their requirements and evaluate the available products and should expand their research as necessary to ensure they're addressing every concern.

    Server memory might also include fault-tolerant capabilities or other features that enhance reliability. One of the most common capabilities is error-correcting code (ECC), a method to detect and correct common single-bit errors. When evaluating server hardware memory, you should look at the entire offering, keeping in mind the types of workloads and applications you run.

    For example, Fujitsu's mainframe computers in the BS2000 SE series support up to 1.5 TB of memory. However, IBM's ZR1 mainframe, which is part of the z14 family, supports up to 8 TB of memory. The ZR1 also provides up to 8 TB of available redundant array of independent memory to improve transaction response times, a pre-emptive dynamic RAM feature to isolate and recover from failures quickly, and ECC technologies to detect and correct bit errors.


    Servers vary greatly in the amount and types of internal storage that they support, in part because workflows and applications also vary. For example, a server hosting a relational database management system will have different requirements than one hosting a web application. In addition, the use of external storage, such as storage area networks (SANs), can also impact internal storage requirements.

    When you buy server hardware, be sure to evaluate each prospective server to ensure it can meet your storage needs. Today, most servers support both solid-state drives (SSDs) and hard disk drives (HDDs). But buyers should certainly verify this support, as well as the server's supported drive technologies, such as Serial-Attached SCSI (SAS), Serial Advanced Technology Attachment (SATA) or non-volatile memory express (NVMe). Other considerations should include drive speeds, capacities, endurance and support for redundant array of independent disks (RAID).

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    A server's ability to connect to networks, peripherals, storage and other components is essential to its effectiveness within the data center. The server needs the necessary connectors and drivers to ensure that it can properly communicate with other entities and process various workloads. Buyers need to determine exactly what type of connectivity is necessary and, from there, examine the server's specs to verify whether it will meet those requirements.

    Servers differ widely in this regard, so buyers should look for specifics such as the number and speed of the Ethernet connectors, the number and type of USB ports, the availability of management interfaces, the types of protocols available, support for SANs and other storage systems, as well as whatever other components are necessary to facilitate connectivity.

    Acer's rack server Altos R380 F3 is a good example of what connectivity features to look for when you buy server hardware. It includes two Ethernet ports, either 1 GB or 10 GB, an RJ-45 management port, three USB 3.0 ports, one USB 2.0 port, and a video port. In addition, the server offers up to seven Peripheral Component Interconnect Express (PCIe) 3.0 slots and one PCIe 1.0 slot.

    Hot swapping

    Servers offer hot swapping capabilities to varying degrees. Hot swapping refers to the ability to replace or add a component without needing to shut down the system.

    The term hot plugging sometimes refers to hot swapping, although, in theory, hot plugging capabilities are limited to being able to add components but not replace them without shutting down the system. Because of the confusion around these terms, it is best to verify how each vendor uses them.

    One of the most common hot swappable components is the disk drive. For example, the Cisco UCS B480 M5 blade server supports hot swappable drives, as does the Huawei FusionServer CH242 V5 blade server and the Intel R2224WFQZS rack server.

    With blade systems, the hot swapping capabilities are often within the chassis itself. One example is the chassis used for the Lenovo ThinkSystem SN850 blade server, which provides hot swapping capabilities for the fans and power supplies, in addition to the server's disk drives. However, these types of capabilities are not limited to blade servers. The Acer Altos R380 F3 system also supports hot swappable fans and power supplies even though it is a rack server.


    Redundancy is important to ensure a server's continued operation in the event of a component failure. Most servers provide some level of redundancy, often for the hard drives, power supplies and fans. The Asus RS720-E9-RS12-E rack server, for example, offers redundant power supplies and the HPE ProLiant DL380 Gen10 rack server offers redundant fans.

    As with its hot swapping capabilities, the redundancy available to blade servers is often located within the chassis. For instance, the chassis that support the Dell PowerEdge M830 blade server and Supermicro SBI-6129P-T3N blade server both provide redundant power supplies.

    However, the Dell chassis also offers redundant cooling components, and the server itself provides redundant embedded hypervisors.


    Admins must manage a server effectively to ensure its continued operation while delivering optimal performance. Most servers provide at least some management capabilities.

    For example, many servers support the Intelligent Platform Management Interface (IPMI), a specification developed by Dell, Hewlett Packard, Intel and NEC to monitor and manage server systems. Not surprisingly, the servers offered by these companies, such as the Dell PowerEdge M830, HPE ProLiant DL380 Gen10, Intel Server System R2224WFQZS and NEC Express5800/B120g-h, are IPMI-compliant.

    But servers are certainly not limited to IPMI capabilities. For example, the Acer Altos R380 F3 rack server comes with the Acer Smart Server Manager; the Asus RS720-E9-RS12-E rack server comes with the ASUS Control Center; and the Cisco Unified Computing System (UCS) B480 M5 blade server comes with Cisco Intersight, Cisco UCS Manager, Cisco UCS Central Software, Cisco UCS Director and Cisco UCS Performance Manager.

    Blade systems usually provide some type of module to manage the individual blades. For instance, Huawei's FusionServer CH242 V5 blade system includes the Intelligent Baseboard Management System module to monitor the compute node's operating status and support remote management.

    Not surprisingly, systems such as Fujitsu's BS2000 mainframes provide a variety of management capabilities. For example, each BS2000 system includes a management unit that works in conjunction with the SE Manager to offer a centralized interface from which to administer the entire server environment. And IBM's ZR1 mainframe includes the IBM Hardware Management Console (HMC) 2.14, the IBM Dynamic Partition Manager and an optimized z/OS platform for IBM Open Data Analytics.


    Another important factor to consider is the server's security features. As with other features, servers can vary significantly in what they offer, with each vendor taking a different approach to securing their systems.

    For example, the Lenovo ThinkSystem SN850 blade server provides an integrated Trusted Platform Module 2.0 chip to store the RSA encryption keys used for hardware authentication. The server also supports Secure Boot, Intel Execute Disable Bit (EDB) functionality and Intel Trusted Execution Technology.

    Another example is the Oracle Server X7-2 rack server, which comes with the Oracle Integrated Lights Out Manager 4.x, a cloud-ready service processor for monitoring and managing system and chassis functions. On the other hand, the Huawei FusionServer CH242 V5 blade server supports the Advanced Encryption Standard -- New Instructions, as well as Intel's EDB feature and Trusted Execution Technology.

    IBM's ZR1 mainframe is also strong when it comes to security. The server includes on-chip cryptographic coprocessors and the Central Processor Assist for Cryptographic Function (CPACF), which includes the new Crypto Express6S feature to enable pervasive encryption and support a secure cloud strategy. The CPACF is standard on every core. The platform also includes IBM Secure Service Containers to securely deploy container-based applications.

    Plextor Introduces the PlexWriter 52/24/52A CD-R/RW Drive; Its Second 52X Optical Disk Drive; Ideal for Systems Integrators and Resellers Who Want High-Performance, Low-Cost ... | real questions and Pass4sure dumps

    FREMONT, Calif.--(BUSINESS WIRE)--May 27, 2003--Plextor(R) Corp., a leading developer and manufacturer of DVD and CD equipment, today announced the availability of the PlexWriter(TM) 52/24/52A CD-R/RW drive. The high-speed 3-in-1 drive features 52X CD-Writing, 24X CD-Rewriting, and 52X-max CD-Reading, and is available as a 5.25-inch half-height internal drive with an ATA/ATAPI-5 interface. The new PlexWriter was specifically engineered to appeal to high-volume resellers and systems integrators seeking value, performance, and reliability in a CD burner.

    Although the PlexWriter(TM) 52/24/52A breaks a new price barrier for Plextor, it still includes a full suite of Roxio digital media software, as well as a unique combination of Plextor features and technologies that deliver unparalleled recording reliability. Buffer Under Run Proof technology prevents buffer underrun errors, so users can multi-task during a recording session. PoweRec(TM) (Plextor Optimized Writing Error Reduction Control) technology is a sophisticated write strategy for stable recording at maximum speeds. Superior 52X-max digital audio extraction (DAE) eliminates pops, clicks, and hisses for superior sound quality.

    "Drive cost is a factor considered by many of their VARs and Systems Integrators," said Howard Wing, vice president of sales and marketing for Plextor. "We set out to design a 52X drive that would be affordable, yet still uphold Plextor's reputation for dependability and consistency in recording CDs. Now they have two 52X drive solutions--the PlexWriter(TM) Premium for professionals and other end-users who want the ultimate in user-controlled features, and the low-cost PlexWriter(TM) 52/24/52A for high-volume systems integrators."

    PlexWriter 52/24/52A CD-R/RW Drive

    The PlexWriter 52/24/52A drive makes installation faster and easier than ever before. The drive is designed for flexible PC installation, with ATA/ATAPI-5 interface, support for horizontal or vertical drive bay orientation, and Plug & Play compatibility with Microsoft Windows(R) 98/ME/2000/XP(TM) operating systems.

    The unit features a 2 MB data buffer and burst data transfer rates of 33.3 MB/sec (UltraDMA Mode 2) or 16.6 MB/sec (PIO Mode 4/DMA Mode 2). PlexWriter drives support a wide variety of CD writing modes, including Track-at-Once, Disc-at-Once, Session-at-Once, Multisession, and variable/fixed packet writing.

    Software Bundle - Roxio(R) Easy CD Creator(TM), Roxio PhotoSuite(R) and PlexTools(R)

    Plextor's bundled software package offers ease-of-use and extensive functionality. The PlexWriter 52/24/52A ships with the Easy CD Creator/DirectCD software package from Roxio, Inc. Roxio's number one selling CD-recording software allows users to drag and drop files to CD-R and CD-RW media. Easy CD Creator includes DirectCD(TM) (packet writing), CD pre-mastering, one-button CD copier, and the ability to create enhanced and mixed-mode CDs. Easy CD Creator also provides the ability to create music CDs, photo slide shows synchronized with music, data CDs, and backup copies of personal discs.

    Roxio PhotoSuite allows consumers to capture, organize, edit and share digital photos. It includes simple electronic albums and a combination of automated and advanced photo-editing tools. Consumers can also print and share online or quickly E-mail them without leaving the program.

    PlexTools allows you to control the important functions of the PlexWriter 52/24/52A with an easy-to-use interface. It permits drive identification and control, display of Compact Disc information, Digital Audio Extraction, CD copying and more.

    The PlexWriter 52/24/52A drive also includes a 30-day trial version of Dantz Retrospect(R). Built on patented technologies, the Dantz Retrospect family of backup products has earned numerous awards for protecting vital information on file servers, business-critical application servers, desktops, and notebooks.

    Pricing and Availability

    The Plextor PlexWriter 52/24/52A drive is available for shipment to distributors and resellers in North and South America in late June 2003. The PlexWriter 52/24/52A has a Manufacturer's Suggested Retail Price (MSRP) of $86.00 USD. All retail packages include unlimited toll-free technical support and one-year full warranty.

    About Plextor

    Plextor Corp. is a leading developer and manufacturer of high-performance CD-related equipment for professional use in consumer and business environments. Since opening its headquarters in Silicon Valley in 1990, Plextor has introduced numerous generations of award-winning optical storage products, including CD-ROM, CD-Recordable, CD-ReWritable, and DVD+R/RW drives. Plextor is privately owned by Shinano Kenshi Co., Ltd., a developer and manufacturer of advanced technology hardware and precision electronic equipment headquartered in Japan. Shinano Kenshi is best known for its expertise in manufacturing motors.

    Note to Editors: Plextor is a registered trademark and PlexWriter is a trademark of Plextor Corp. Roxio, PhotoSuite, and Easy CD Creator are registered trademarks of Roxio, Inc. All other trademarks, trade names, registered trademarks, or registered trade names are the property of their respective holders.

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