It’s just not worth buying computers at the moment.

In our present market, two turbulent forces entwine to null the value of anything less than monumental improvements. These are Brexit and memory price gouging. To qualify the clickbaity title, specifically I mean for technical computing in the UK.

Lies, damn lies, and marketing

In an earlier post I described that although an interesting technical observation, the idea of doubling in actual performance has been falsely perpetuated by marketing types. 10-20% improvement is more realistic however incremental improvements have at least been improvements. And a new machine has been a worthwhile investment over renewing maintenance on an old machine. Plus we like shiny new machines…

But there are many ways of measuring performance, and for many workloads even a 10% generational improvement is a falsehood.


To test my title hypothesis, consider SPECfp. This is a computer benchmark designed to test the floating point performance, however it differs from Linpack in that it more accurately represents Scientific / Technical computing applications. These tend to be very data-orientated and often push entire system bandwidths to the limits moving data on and off the CPUs

I collected data published on and using R extracted a comparable set of statistics for generations of Intel Xeon E5 CPUs, grouped them by their E5 sku number to compare generation-on-generation performance. Anyone who has benchmarked AMD for performance applications will naturally know why I’m only looking at Intel…

The below charts depicts those E5 numbers which occur in all four of the most recent generations being considered. There was an un-even time gap between generations, for further comparison I have also plotted this data against. A trend of decreasing improvements can be seen.

OK yes I’ve exaggerating, but not my much

In fairness to Intel, using these skus as the comparison point is a bit of a simplification. They will say we shouldn’t compare based on their E5 label, but whichever way we look at it the same patterns emerge. The below chart takes the highest performing CPU of each generation (including CPUs not represented on the earlier chart) and plots them against time.

I’ve taken the opportunity to join in the debunking of the “doubling in performance every two years” myth here by also plotting where that doubling in performance would have led to. I give this chart the alternative title: “Where marketing think computer performance has been going compared to where it has actually been going”.

Where marketing think computer performance has been going compared to where it has actually been going

The below chart plots the performance of all E5-2600 CPUs including those which do not occur in all generations for a fuller comparison agnostic to the names these products are given. Again the diminishing returns are apparent.


The Intimidating Shadow of Ivy Bridge

Returning to my hypothesis, specifically I’d like to zone in on the Ivy Bridge (v2) CPUs launched in the tail end of 2013. Initially priced at a premium, however as prices settled into 2014 many more were bought. Machines sold with 3-years maintenance are pretty standard in IT, and so a significant number of machines up for maintenance renewals or replacement are Ivy Bridge.

Comparing the highest SKU of each generation, we see only 12% increase in real performance Ivy Bridge to Broadwell. Comparing SKUs over generations we typically see around 22% improvement.

This is most worrying as with current memory prices and currency exchanges servers typically cost 20-25% more than they were 8-9 months ago.

Memory cost did decrease per GB from Ivy Bridge until recently, plus we now have DDR4 and SSDs are more sensible. But if you have a higher end Ivy Bridge server falling off warranty, it’s just not replacing it right now. Buy maintenance instead and hope Skylake is better.

The Compute Landscape at the Beginning of 2017

For years the IT industry has accepted Intel as the only viable option. At the beginning of Intel’s reign the consensus was: “yeah Intel CPUs are way better than anyone else, let’s buy lots”. But now the feeling is: “oh, another incremental upgrade from Intel. What’s AMD up to? Ah still nothing. Fine buy more Intel…

Being fair it is mean of me to wail on Intel for AMD’s failure/refusal to compete, Intel have still been innovating just not at the rate we became accustomed to in the competitive years. 2017 looks to be an interesting year, a year we all get more choice.

Beyond Kidz wiv Graphics Cardz

NVIDIA have been pushing really hard for years now to establish themselves beyond gaming. Their GPU hardware offers excellent performance but despite creating a whole CUDA ecosystem to support their products, few made the leap. Incrementally faster horses were fine and we could all get on with our work.

Deep/Machine Learning is beginning to revolutionise IT. It’s stretching out beyond academia into more and more commercial uses. Soon if you do not have an analytics strategy you will not be competitive. This is an excellent area to use GPU accelerators; many machine learning applications involve a larger number of parallel computations proportional to the amount of data. And “big data” applications exploit scale-out designs beautifully.

Intel position their Phi co-processors (and lately Knight’s Landing processors) as a competitor to NVIDIA GPUs, but without significant direction no one really knows what to do with a large number of inferior Xeon cores in one box. Our E5 Xeons are often not at 100% utilisation, there’s little benefit moving to a platform with less memory per core, and less network bandwidth per core.

After years of unchallenged Intel dominance they are emitting the field of dreams aura of “If we build it, they will come”. This works for Xeon E5 chips as no one’s building anything else. But with NVIDIA building and aggressively supporting users move to their platform, accelerator users are flocking to NVIDIA leaving Phi and Knight’s Landing dead on the side of the road.

Are AMD about to ante up?

You’d think that as Intel have been cramming more and more cores into a box then AMD should have been quite competitive, until recently AMD were exceeding Intel in this metric. But their architecture is such that two “cores” share an ALU. This makes it not too dis-similar to Intel’s Hyperthreading where two virtual cores also time-share a physical core. Both get good utilisation out of their ALUs, but in most fair comparison Intel outperforms AMD.

AMD have been viewed as a cheaper “also-ran”. With the major exception of cloud providers, most of the industry has been moving to do more from less hardware. And even many cloud providers are using Intel (often E3s stacked high and sold cheap).

Intel have been coasting. The time is right for AMD to get back in the game. PCIe Gen 4.0 along with a refreshed nano-architecture could offer great potential for high-bandwidth applications.  Bandwidth between CPUS and accelerators, memory and the network.

Choice is Good

I’m speculating somewhat on AMD’s next platform and weather it will be any good, but NVIDIA certainly are well placed for 2017. The announcement of their Pascal architecture last year was a game changer for accelerators of which we are still feeling excitement. And IBM’s opening of their historically proprietary POWER platform into the OpenPower foundation opens the gates for more competitive POWER systems to break through.

I see more going on in compute now than there has been for years.

The Myths and Marketing of Moore’s Law

Moore’s Law won’t end. Even when it ends it won’t end.

The Law follows that more components can be crammed into an integrated circuit with developments in technology over time. However transistors are getting so small that current leakage becomes a greater issue. In short this means there needs to be an amount of empty space between transistors for them to work predictably and without predictability you can’t build computers. This “empty space” (dark silicon) means even if we were to make transistors infinitely small, there would still be a finite limit on how many we could fit on a chip.

For electrical transistors at least, the current wording of Moore’s Law is ending. I won’t prophesies a paradigm shift to optical or quantum computers to take the next leg; although on the way they will not arrive in time. It won’t end for a much simpler reason…

What’s this doubling business?

The idea of doubling in “performance” always was a myth. Even in the frequency scaling hey-day we saw diminishing returns but a doubling in something sure was a good reason to buy a new computer. With recent CPU architectures we’ve only been seeing ~10% increase in performance for a die shrink and ~20% for a full nano-architecture redesign, which is why for many system owners the hardware refresh cycle can be five or more years.

Why it won’t end:

It’s not a law governing what will happen but an observation on what has happened. The prospect of selling computers funds innovation IT so marketeers will just adapt the law to observe something else. We old hats know this won’t be the first time. The real world implication of Moore’s Law is you buy a new computer every few years, which is why though the wording may change The Law will continue. And the myth of doubling with it.

time to take Java seriously again?

Like many Computer Science graduates Java was the first language I’d say I really learnt. Sure I’d dabbled in C and VB but Java is where I first wrote meaningful code beyond examples from the text book. Again like many Computer Science graduates, I turned my back on Java pretty soon after that.

The need is not to get the most out of your hardware but to get the most out of your data, as quickly and continuously as possible to retain your advantage.

My experience in video game programming as well as my current day job around research computing (although not in a programming capacity) both feature squeezing every drop out of hardware which sadly leaves little space for Java. In both code written in fast low-level languages is optimised to exploit the hardware it will run on.


The ongoing data analytics and machine learning revolution, surely the most exciting area in IT at the moment, is bringing with it a data-centric approach of which we should all take note. The need is not to get the most out of your hardware but to get the most out of your data, as quickly and continuously as possible to retain your advantage.

Spark for example is written in Scala, which compiles into Java byte code to run on the Java Virtual Machine which itself finally runs on the hardware. Furthermore many Spark apps are themselves written in a different language such a R or Python which have to first interface with Spark. This is a lot of layers of abstraction each adding overheads which would be shunned by performant orientated programmers.


Yet when I look at these stacks I instead see wonderful things being done and begin to see past my preconceptions.

I’m also seeing containers grow in prominence which are a natural fit for Java development. With S2I builds (source to image) developers can seamlessly inject their code from their git repository into a Docker image and deploy that straight onto a managed system.

Whilst C++ will remain the norm for mature performant orientated applications, hypothesis testing and prototyping to yield quick results is giving an extra life to Java.