In the end of the day, every software success relies on hardware modifications.
I am a bit fed up with people telling me that software codecs are just good enough – they consume the same battery life (they don’t), they provide the same quality (they don’t), they can run on small devices (they can’t). You can spam to your heart’s content in the comments below, but please – read this one through before you do.
The model of running our code faster than ever with each hardware iteration is breaking. We have 2 or even 3 GHz machines, but this has been true for several years. To stay ahead of the curve, we’ve moved into packing more computational units (cores) into our chipsets (this was the Intel way of life) or we went to accelerating specific tasks with dedicated hardware (the ARM/MIPS way of life in embedded devices).
Every time a software technology becomes mainstream, it gets accelerated by way of hardware. Here are a few examples from recent years.
Virtualization is a great concept: you take the hardware and virtualize it – running the software on top of it wherever you want – instead of running a single software instance on a single physical hardware you can now run multiple software instances on a single piece of hardware – sharing the capacity it has.
Guess what – this whole virtualization thing requires hardware that explicitly supports it to be of any real use without huge penalties on performance and capabilities. This is why Intel and other chipset vendors have invested in virtualization in their chipsets. While there are still debates as to the advantages of hardware assisted virtualization, you can be rest assured that this trend will only increase.
Guess what? That realistic video game you are playing? Or the nice transitions your iPhone does for the user interface? They get accelerated on a GPU.
The iPhone and most other smartphones today use PowerVR GPU which makes its appearance as part of their system-on-chip. The code that runs the visualization stuff simply offloads all the heavy lifting to the GPU – can’t do it on the host chip with software if you want responsiveness from your phone or a full day of battery life.
We’ve been doing video compression in hardware for ages – the reason is simple – there was no other way.
And then Intel with its Moore’s Law decided to catch up with our needs. But then our needs increased: we decided we want more resolution and frame rate for our videos, and we want to compress them better with modern standards.
So yes. You can use an Intel chip and run video compression on it. But even Intel understands the futility of it, so their latest chipsets does hardware based encoding of video. If you own an Android or an iPhone – your camera recordings and YouTube playback are done with hardware acceleration as well.
When it comes to security, hardware can cause problems and solve them.
Troy Hunt did an interesting test where he tried to generate hash values using a GPU. Here’s the result:
You see, using hashcat to leverage the power of the GPU in the 7970 you can generate up to 4.7393 billion hashes per second. That’s right, billion as in b for “bloody fast”.
Let’s see you do that without a GPU acceleration – a CPU won’t get to billion hashes per second – that’s for sure.
On the other end of it, when you want to encrypt bulk data – either for storage or network – you will shift to hardware to do it efficiently. As Adrian Kingsley-Hughes notes in his own suggests:
This drive features AES 256-bit hardware encryption to allow you to encrypt and protect your sensitive data while at the same time getting the performance, reliability and power benefits of a solid state drive.
In other words – if heavy lifting is done by hardware, then there are no penalties on the software you still need to run on the same hardware.
The most fascinating example of all is probably that of taking Java and accelerating it – running pieces of it on the GPU itself.
Hardware acceleration is going to grow. It has come to a point where there are thoughts of accelerating higher level programming languages such as Java, which only goes to show that as much as Moore’s law is concerned, our needs are insatiable even further.
So you see, even if you do think you are running pure software for your super uber algorithm – it is most likely accelerated somewhere to make it feasible for the use cases you need.
In other words – hardware trumps software each and EVERY time.
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