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.
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PowerOfFacts - Thursday, June 23, 2016 - link
And now Oracle marketing speaks. Their HammerDB results are bogus. Oracle continues to site socket results when the majority of the world has moved on to per core results. They cite the results from a 32 core HammerDB then compare it to a 1 chip (1/2 of 1 socket) POWER8 because Phil has a hard-on for how "HE" believes IBM has packaged the processor and similarly chooses an Intel configuration to ensure "THEY" get the result they want. Phil & Oracle (appear) to always speak with forked tongue.patrickjp93 - Sunday, April 3, 2016 - link
"Best" only at specific scale-up workloads. There's a reason Sparc is not particularly popular for clusters and supercomputing (and it's NOT software compatibility). It sucks at a lot of workloads when compared to x86. As for the SAP benchmarks, that's to be expected since x86 doesn't yet support transactional memories. That changes with Skylake Purley though.Brutalizer - Wednesday, April 6, 2016 - link
In these 25ish benchmarks, the SPARC M7 is 2-3x faster on all kinds of workloads, not just some specific scale up workloads. The reason SPARC M7 is not popular for clusters (supercomputers are clusters) is not because of low raw compute performance, it is because of cost and wattage. The M7 is much more expensive than x86, and draws much more power. I guess somewhere 250 watt or so? M7 are in big enterprise servers, some have water cooling, etc. Whereas clusters have many cheap nodes, with no water cooling.Clusters can have x86 because the highest wattage x86 cpu, uses 140 watt or so. Not more. So it would be feasible to use 140 watt cpus in clusters. But not 250 watt cpus, they draw too much power.
For instance, the IBM Blue Gene supercomputer that hold spot nr 5 in top500 for a couple of years, used 850 MHz powerpc cpus, when everyone else used 2.4 GHz x86 or so. The 850 MHz cpu dont use lot of power, so that is the reason it was used in Blue Gene, not because it was faster (it wasnt). A large supercomputer can draw 10 MegaWatt, and that costs very much. Power is a huge issue in super computers. SPARC M7 draws too much power to be useful in a large cluster, and costs too much.
If we talk about raw compute power for SPARC M7, it reaches 1200 SPECint2006, whereas E5-2699v3 reaches 715 SPECint2006. Not really 2-3x faster, but still much faster.
In SPECfp2006, the M7 reaches 832, whereas the E5-2699v3 reach 474.
https://blogs.oracle.com/BestPerf/entry/201510_spe...
So, as you can see yourself, the SPARC M7 is faster on scale-up business workloads (it was designed for that type of workloads) and also faster on raw compute power. And faster in everything in between. Just look at the wide diversity among these 25 ish benchmarks.
Brutalizer - Wednesday, April 6, 2016 - link
BTW, do you really expect a 150 watt x86 cpu, to outperform a 250 watt SPARC M7 cpu? Have you seen benchmarks where they compare 250 watt graphics card vs a 150 watt graphics card? Which GPU do you think is faster? Do you expect a 150 watt GPU to outperform a 250 watt gpu?The SPARC M7 has 50% more cores, twice the cpu cache, twice the GHz, twice the Wattage, twice the RAM bandwidth, twice the nr of transistors (10 billions) - and you are surprised it is 2-3x faster than x86?
BTW, the SPARC M7 has stronger cores than x86. If you look at all these benchmarks, typically one M7 with 32 cores, is faster than two E5-2699v3 with 2x18 = 36 cores. This must mean that one SPARC M7 core, packs more punch than a E5-2699v3 core, because 32 SPARC cores are faster than 36 x86 cores in all benchmarks.
adamod - Friday, June 3, 2016 - link
i know this is an old post but i am confused (this isnt something i have learned much about yet) i am hoping you can help some...if the sparc has 2 to 3x performance and is 250w compared to 140w then wouldnt that make it MORE efficient? and if you need two 2699's to compare to a sparc m7 then wouldnt that be 280w, more than the 250w of the xeons? i realize there are other factors here but this doesnt make sense to me. also yea there are graphics cards that are a lower wattage and perform better...i am an AMD fan but nvidia has had some faster cards with better performance in the past...i have an R9 280X, a mid grade card rated at i believe 225w, kinda crazy when it can get beaten by 17w nvidia cardstqth - Sunday, April 3, 2016 - link
The SPARC and POWER servers are for people with unlimited pocket where compactness and reliability worth the premium it's spent on. If you have to ask how much it costs, you'd probably can't afford it.Xeons are commodity hardware where you could purchase the best bang for your buck.
They are not aiming at the same market. Most software wouldn't even work on both system.
Besides, benchmarks are worthless - unless the performance of the specific software is tested. And that's rare.
PowerOfFacts - Thursday, June 23, 2016 - link
Depends on which Xeon processors you are referring to. The latest Broadwell EP & EX chips can cost over $7K each. Well on par if not exceeding POWER8 chips and definitely more than OpenPOWER chips. Times are changing. Intel has milked their clients for a long time feeding them the marketing line of open, commodity & low cost. They are no longer open buying up ecosystem integrating into the silicone, what exactly does commodity mean anyway and as low cost goes ... as I just said, pretty salty.yuhong - Thursday, March 31, 2016 - link
64GB LR-DIMMs will probably not come out at reasonable prices until 8Gbit DDR4 is more mainstream.iwod - Thursday, March 31, 2016 - link
I thought Samsung announced a 128GB DIMM with some type of 3D / TSV RAM.Casper42 - Thursday, March 31, 2016 - link
Not shipping just yet though.Should be sometime this year though.