The Intel Xeon E5 v4 Review: Testing Broadwell-EP With Demanding Server Workloads
by Johan De Gelas on March 31, 2016 12:30 PM EST- Posted in
- CPUs
- Intel
- Xeon
- Enterprise
- Enterprise CPUs
- Broadwell
Big Data 101
Many of you have experienced this: you got a massive (text) file (a log of several weeks, some crawled web data) and you need to extract data from it. Moving it inside a text editor will not help you. The text editor will probably collapse and crash as it cannot handle the hundreds of gigabytes of text. Even if it doesn’t, repeated searches (via grep for example) are not exactly a very fast nor are they scientific way to analyze what is hidden inside that enormous hump of data.
Importing and normalizing it into a SQL database via the typical Extract, Transform and Load (ETL) process is tedious and extremely slow. SQL databases are built to handle limited amounts of structured data after all.
That is why Google created MapReduce: by splitting up those massive amounts of data in smaller slices (mapping) and reducing (aggregating, calculating, counting) them to the results that matters to you, you can avoid the rather sequential and slow query execution plans that need to evaluate the whole database to provide meaningful results. Combine this with a redundant and distributed filesystem HDFS that keeps data close to the processing node. The result is that you do not need to import data before you can use it, you do not need the ultimate SSD to quickly load so much data at once, and you can distribute your data mining over a large cluster.
I am of course talking about the grandfather of all Big Data crunching: Hadoop. However Hadoop had two significant disadvantages: although it could crunch through terabytes of data where most other data mining systems collapsed, it was pretty slow the moment you had go through iterative steps, as it wrote the intermediate results to disk. It also was pretty bad if you just want to launch a quick simple query.
Apache Spark 1.5: The Ultimate Big Data Cruncher
This brings us to Spark. In addressing Hadoop’s disadvantages, the members of UC Berkeley’s AMPlab invented a method of keeping the slices of data that need the same kind of operations in memory (Resilient Distributed Datasets). So Spark makes much better use of DRAM than MapReduce, and also avoids many write operations.
But there is more: the Spark framework also includes machine learning / AI libraries that you can use inside your scala/python code. The resulting workload is a marriage of machine learning and data mining that is highly parallel and does not need the best I/O technology to crunch through hundreds of gigabytes of data. In summary, Spark absolutely craves more CPU power and better RAM technology.
Meanwhile, according to Intel, this kind of big data technology is top growth driver of enterprise compute demand in the next few years, as enterprise demand is expected to grow by 67%. And Intel is not the only one that has seen the opportunity; IBM has a Spark Technology Center in San Francisco and launched "Insight Cloud Services", a cloud service based on top of Spark.
Intel now has a specialized Big Data Solutions group, led by Ananth Sankaranarayanan. This group spearheaded the "Big Bench" benchmark development, which was adopted by a TPC group as TPCx-BB. The benchmark is expressed in BBQs per minute...(BBQ = Big Bench Queries). (ed: yummy)
Among the contributors are Cloudera, Cisco, VMware and ... IBM and Huawei. Huawei is the parent company of the HiSilicon ARM processor, and IBM of course has the POWER 8. Needless to say, the new benchmark is going to be an important battleground which might decide whether or not Intel will remain the dominant enterprise CPU vendor. We have yet to evaluate TPC-BBx, but the next page gives you some hard benchmark numbers.
Facebook
Twitter
RSS
112 Comments
View All Comments
xrror - Tuesday, April 5, 2016 - link
Even at 3.3Ghz though, they shouldn't be that slow. I'm taking a guess - if this was a student lab, and they bothered to specifically order xeon (or opteron back in the day) workstations - I'm guessing this was a CAD/CAM lab or something running a boatload of expensive licenced software (like, autodesk, solidworks, etc) and some of that stuff is horrible at thrashing on the hard drive, constantly.And I doubt your school could spring the cash for SSD drives in them (because Workstation SKU == you pay dearly OEM workstation 'certified' drive cost).
This is all guesses though. And not trying to defend - it does suck when you have what should be a sweet machine choking for whatever reason, and you're there trying to get your assignments done and you just want to smash the screen cause it just chhhuuuuuuggggsss... ;p
SkipPerk - Friday, April 8, 2016 - link
I have seen this many times, even in the for-profit sector. I once saw a compute cluster that was choking on server with slow storage. They had a 10 gb network and fast Xeon machines running on flash, but the primary storage was too slow. When they get a proper SAN it was an order of magnitude improvement.Back in the day storage was often the bottleneck, but it still comes up today.
someonesomewherelse - Thursday, September 1, 2016 - link
We ran everything in virtual machines with the actual disk images not stored locally.... and the lans in the classrooms were 100mbit, idk about the connection from the classroom to the server with the image. How's that for slow?I would have loved it if our stuff was as 'slow' as yours. The wifi in the classrooms was very fast too..... especially since I doubt anyone bothered with turning of their torrents (well I mean it's completely understandable, you are going to watch the new episode of your favorite show once you are back home and not everyone had (well has, but most people can get it now) fth with at least 100Mbit line (ideally symmetrical, but some isps are too gready with ul speeds so 300/50 is cheaper than 100/100...... and good luck getting 1000/1000 on a residential package (the hw isn't the problem since you can get 1000/1000 with a commercial (aka over priced) package..... using the same hw... basically I would just need to sign a new contract, send it back, and enjoy the faster line in 1 business day or less)...well at least there are no bw caps (if I didn't read foreign boards bw caps on non mobile connections would be something I'd think no isp could do and not lose all customers) and there's we have no dmca (or something similar) and afaik no plans for one either (if they tried to pass such a law I can imagine that you'd have enough support for a referendum which you would win with a huge mayority), even better, the methods used to catch people downloading torrents are illegal anyway so any evidence obtained with them or derived from them is inadmissible anyway and just by presenting it you have admitted to several crimes which the police and prosecution are obliged to investigate/prosecute.... copyright infringment however is a civil matter).
donwilde1 - Tuesday, April 5, 2016 - link
One of the more interesting Intel features, in my opinion, is that Broadwell carries an on-board encryption engine with its own interpreter similar to a small-memory, embedded JVM. This enables full Trusted Boot capability, which I view as a necessity in today's hackable world. Would you consider a follow-on article on this? The project was a clean-room development called BeiHai, done in China.JamesAnthony - Wednesday, April 6, 2016 - link
From what I can tell in looking over the benchmarks, there is not much of an increase in performance at all in core vs core performance speeds going from the V1 CPUs to the V4 CPUsAs if you look at the benchmarks, and calculate that you are comparing 16 cores to 44 cores, the 44 core setup is not 2.75x faster.
So while your overall speed goes up, your work accomplished per core is not increasing at the same rate.
Why does this matter? Well thanks to software licensing costs, as you add cores it gets very expensive quickly. So if your software costs (which can easily exceed the hardware costs very quickly) go up with each core you add, but the work done does not, you quickly wind up in a negative cost / performance ratio.
For quite a few people the E5-2667 v2 CPU with 8 cores at 3.5 GHz (Turbo 4) comes out around the best value for the software licensing cost.
So while Intel puts out processors that overall can do more work than the previous ones, the move to per core software licensing is making it a negative value proposition. This is why people keep wanting higher clock speed lower core count processors, but we seem stuck around 3.5 GHz for many years.
SkipPerk - Friday, April 8, 2016 - link
Although you are right for workstations, so much demand is for generic virtualized machines. Many buyers are fine with 2 ghz with as many cores as they can get. They load as little RAM as the spec requires and throw out the cheapest single core, dual thread 2 GB RAM VM they can. This is how call centers work, not to mention many low-level office jobs. They do not care about performance because this is more than enough.I have had specialty applications where prosumer 6-core or 8-core CPUs were the better deal (usually liquid cooled and overclocked), but not many buyers are licensing insanely expensive analytical software by the core.
SeanJ76 - Sunday, April 10, 2016 - link
@Xeon chips!! TOTAL GARBAGE!legolasyiu - Wednesday, April 20, 2016 - link
The ASUS Workstation/Server board with V4 boards are very stable and they have 10% OC. I am very interested how the processor with those boards.Bulat Ziganshin - Saturday, May 7, 2016 - link
>This increases AES (symmetric) encryption performance by 20-25%PCLMULQDQ implements part of Galois Field multiplication and bdw actually improved only GCM part of AES-GCM algo. neither AES nor other popular symmetric encryption algos became faster
oceanwave1000 - Monday, May 9, 2016 - link
This article mentioned that the Broadwell EP e5-v4 family has 3 die configurations. I got the 306mm2 and 454mm2. Did anyone catch the third one?Thanks.