Under the MacBook hood with NVIDIA
By Simon Bisson & Mary Branscombe in Editorial
Posted in Processors, Silicon, Hardware, Laptop, Apple on
Apple’s switch from basing its laptops on Intel chipsets to NVIDIA’s new 9400M series has raised more than a few eyebrows. There’s a good reason for that switch, as I discovered when I had a conversation with NVIDIA’s Rene Haas last week.
In the past mobile graphics chips have been a poor cousin to their desktop relations. Some may have the same product numbers, but a fraction of the power. With the advent of technologies like OpenGL and the rise of General Purpose GPU computing (GPGPU), laptop GPUs looked like they were being left far behind. Popular software is starting to take advantage of GPU computing, with companies like Adobe taking advantage of GPU programming to accelerate and smooth operations in its latest version of the CS imaging and design suite. You couldn’t get the smooth rotations and zooms in Photoshop CS4 without OpenGL - and if your chipset doesn’t support it, you’ll just get an error message.
Apple’s new machines aren’t just using the 9400M for OpenGL. There’s a lot more to the chips than GPUs (though the 16 GPU cores take up most of the silicon). The chips also include much of the core system hardware you usually find as separate chips. The result brings the Northbridge and Southbridge into the same package, using much less real estate and allowing motherboards to be less than 1/2 the size, and at the same time giving increased graphics performance for the same power footprint. Laptops get better gaming performance, and applications get better user interface effects.
The MacBook’s improved video performance has been noticed, and it’s down to the 9400M’s built-in HD video support. There’s hardware support for the H.264 HD video codec Apple uses for its iTunes movies, as well as support for many of the decryption techniques needed to work with DVDs and BluRay. While Apple may not support BluRay yet, Windows will with Vista’s SP2 release, and NVIDIA’s chips handle the AES encryption used on BluRay discs, as well as handling high-end features like BD-Live.
The MacBook Pro shows off another of NVIDIA’s features, Hybrid SLI, which lets hardware developers add a second GPU for more processing power when it’s needed - turning it off when it’s additional boost is unnecessary. The Pro has an additional 9600MGT which can be used for gaming or intensive image processing - using more power than when a single GPU is used for word processing or web browsing
So why is NVIDIA producing this new chip? The main reason is the size of the laptop market. New laptops will outsell desktops by a large margin by 2012, and users want the same performance in their bags as well as on their desks. Only a small proportion of notebooks have discrete GPUs, with most using integrated graphics. GPUs need to compete with integrated chipsets on price, form factor and performance, so this is where a new single chip solution comes in to play.
There an interesting caveat to this story, too. NVIDIA’s CUDA GPGPU framework has become an interesting tool for developers who want to work with massively parallel application programming on GPUs. In the past it’s been resistant to talking about other GPGPU frameworks - but the Apple relationship is changing that. Apple has announced that it wil be supporting the OpenCL GPGPU APIs in the Snow Leopard release of OS X, and as a result, NVIDIA will be supporting OpenCL access to its CUDA frameworks. Supercomputer performance in a laptop will be a very interesting side effect of the 9400M chips.
This isn’t an exclusive deal with Apple, either. There will be more laptop manufacturers switching to this approach in future - so we can look forward to a much better laptop experience with Windows and Linux in the future.
–Simon
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CPU vs GPU, mythbusted or mythdirected?
By Simon Bisson & Mary Branscombe in Editorial
Posted in visualisation, Processors, Silicon on
The folk from Mythbusters were on hand at NVision08 to show the audience the difference between CPU and GPU computing. In true Mythbusters fashion they did it with vast amounts of paint, and what must have been one of the world’s largest paintball guns.
First they began with a simple (for them) demonstration of serial operations - using a paintball gun wielding robot to draw a smiley face on a whiteboard. A hundred or so blue dots made the robot one of the slowest (and loudest) dot matrix printers we’ve seen.
Parallel operations would take something a little larger, and their 1100 paintball inkjet printer filled much of the stage. Powered up it would create a picture of the Mona Lisa in glorious 8-bit colour in a fraction of second. Huge air tanks held the compressed air the device needed to simultaneously launch all the paintballs in all the tubes.
The demonstration was certainly impressive, but it was more than a little misleading.
The type of data-centric work that CUDA GPUs handle is more about using parallel processes to handle lots of small pieces of data, not about building complex images from small pieces of data. With a parallel architecture like that you develop algorithms that break down big problems and big data sets into smaller, easier to work with, pieces. Farmed out across tens and hundreds of processors in a GPU, each data block can be processed, before being reassembled and the results delivered.
They’re not new techniques, either, for one thing the approach is at the heart of computational fluid dynamics and finite element analysis. The parallel techniques used in GPU computing are certainly impressive, and are already delivering supercomputing to the desktops of the scientists and engineers who need the power (an Nvision session on using GPU-based supercomputers to model the plasma dynamics around neutron stars and the black hole at the centre of the galaxy was particularly impressive). Low-cost high-performance computing is the GPU’s strength, especially when compared to the hefty power requirements of an equicalent array of traditional CPUs.
The Mythbusters’ demonstration was good (and an enjoyable piece of theatre), but it really told a different story. So how could the intrepid special effects team have told the real story of GPU computing?
How about one robot carrying a large, heavy cube across the stage? Suddenly it’s joined and over-taken by a swarm of smaller machines, all carrying smaller cubes - cubes that weigh as much as the single cube on the struggling robot. Or if paint is the preferred metaphor, a can of paint slowly emptying through a single pipe. Meanwhile another can empties through hundreds of holes in much less time.
So, how would you demonstrate it?
–S
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Let’s get physical
By Simon Bisson & Mary Branscombe in Editorial
Posted in Processors, Silicon, Software on
Nvidia has decided that the visual computing world needs a conference, and has taken over San Jose to deliver just that. It’s an odd event, with a high-level academic parallel processing track running alongside highly analytical business sessions - and what’s billed as the world’s largest LAN party filling one of the conference halls.
Games may have made Nvidia, but it’s the rest of the graphics industry that keeps it going. Simulation and CAD drive much of today’s industrial design, while complex financial calculations can be run on GPU-powered parallel processors. It’s not just black hole plasma dynamics - it’s also the models that help calculate how a fusion reactor will operate. According to Nvidia GPU computing is bringing supercomputing to the desks of the people who need it the most - for just the cost of a video card.
One of the keynotes showcased a NASCAR simulator used by drivers to hone their skills. On stage we heard a populist story of what it was like to be a driver, and what it was like to use simulation tools. Off stage we heard a more interesting story about how the simulator developers were looking at using the latest generation of GPUs in their application. The ability to use a GPU for parallel processing - and the availability of powerful hardware physiscs engines - has made them completely rethink their next generation, as the new hardware features mean that they can now work on making the simulation more realistic.
That’s what the drivers want. Asked what he really wanted from a simulator, Kyle Busch didn’t talk about new high-resolution graphics or realtime ray tracing. What he wanted was more accurate physical behaviours. In the real world passing on the left is different from passing on the right, while slipstreaming another car can change the performance dramatically. A simulation may look real, but without the physics it’s not realistic at all.
One plan for the next generation is to move away from the current car model, with only 6-degrees of freedom. Instead, it really needs 72 degrees, for all the hinge and flex points - all of which are changing dynamically. That’s where parallel processing comes in, as it allows a car to be modelled in real time, taking advantage of physics engines to turn those model calculations into real world behaviours. Improving the simulation will mean more (and happier) customers - as well as a continually improving model that can be shared with vehicle manufacturers.
It’s an approach that requires specialist processing that goes beyond the traditional CPU. Don’t confuse it with the death of the CPU, though. There will always be a place for the traditional CPU - it’s just that silicon technology has become ubiquitous enough for specialist hardware to offload processor intensive functions.
Need to encrypt something? Just use the hardware cryptosystem built into a TPM. Need to do thread intensive Java? Hook up an Azul network processing appliance. Need to do complex vector calculations on large amounts of data? Use a GPU. Nvidia’s CEO Jen-Hsen Huang talks about it as heterogenous computing, where the CPU handles tasks, and more specialised hardware handle the complex tasks that tax general purpose silicon.
Intel and AMD may still say that general purpose processors are just what the world needs - but they’re still investing in HyperTransport and QuickPath, the fast buses that specialised silicon needs. I wonder why they’re doing that, if specialised silicon is the dead end they say it is. Is there something about Moore’s Law they’re not telling us?
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IDF: stress testing SSD
By Simon Bisson & Mary Branscombe in Editorial
Posted in Silicon, Storage, Hardware, Laptop, Intel on
Battery life? Performance? No, the important test Intel’s new SSD passes is known internally as P*ssmark
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You say Express Gate, I say Palladium
By Simon Bisson & Mary Branscombe in Editorial
Posted in Futures, Silicon, virtualisation, Hardware, Laptop, Mobile, Security, Intel, Microsoft on
Imagine a second, simpler operating system on your PC with fixed features, so it’s more secure - after all, if you can’t add more programs you can’t add a virus either. It would have to start up quickly, so that Windows wasn’t waiting for it, so it would be ideal for listening to music and watching video. I’m not thinking about virtualization per se, although that’s one way to achieve something similar; this is two operating systems side by side, both with access to the PC hardware, but one of them does much more limited and circumscribed things.
Can you tell what it is yet?
No, actually, I’m not talking about Palladium - sorry, Microsoft Next Generation Secure Computing Base. That grew out of an attempt to reassure Sony that it would be OK to allow DVD movies to play on a PC without piracy becoming endemic and turned into a much more useful and visionary idea about using public key cryptography not to identify people but to secure machines. It would have been a good way to implement the DRM it was associated with in the public eye, though wouldn’t have forced it on anyone who didn’t want to run it. Palladium loaded a secure piece of software called the TOR that acted as a secure area that could only run trusted code (written to public APIs), where the apps would be invisible to the main OS - all secured by the machine-specific key in your TPM and some new technology from Intel.
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Intel predicts an all IA future, consigns CUDA to the footnotes
By Simon Bisson & Mary Branscombe in Editorial
Posted in Silicon, Futures, Intel, Server on
With Intel’s 40th birthday on the horizon (and with it the 40th anniversary of the microprocessor), Intel’s Pat Gelsinger took a few minutes yesterday to ruminate on the past, present and future - and to take a few questions.
Beginning with a look back to the i386, and the shift from 16 to 32-bit computing, Gelsinger pointed to a time of technical and industry transition, much like today. It was the point where Compaq moved ahead of IBM, and Windows and Microsoft began to shape the software industry. We’re in the middle of another shift at the moment, what Gelsinger called the “third era of Moore’s Law”.
The first era was the age of invention, with the second concentrating on scale and manufacturing. Gelsinger calls the third era “The right hand turn”, where the industry starts to concentrate on energy efficiency. He went on to describe the industry’s success as resulting from “the power of compatibility”, where compatible software means that each generation of silicon can inherit the work of the entire industry (with just a little recompile along the way). There have been plenty of changes in Microprocessor design, purely by increasing numbers of transistors - the power controller on Intel’s Nehalem processors is bigger than Gelsinger’s first processor. There’s a sheer complexity to these machines, which Gelsinger described as “the most advanced things ever built”.
That’s the past and today, so what about tomorrow? Intel reckons on having 10 years of visibility into the future of silicon. Gelsinger described silicon as “the scaffolding for half the periodic table”. The future will be much the same, even if it’s based on silicon nanowires and spintronics. The first big change will be in just a couple of years, with the shift to 450mm wafers. The investment this requires will be huge, and Intel expects this to trigger a wave of industry consolidations - just to help pay for the new fabs.
Gelsinger also sees Intel’s IA architecture as a key differentiator between it and the rest of the industry. As multicore systems become more and more common, and as IA scales up to teraflop terascale systems and down to milliwatts, software will be compatible between all the different versions of the architecture. There of course will be different languages and libraries (especially for parallel processing systems), but code will be portable.
The result will be what Gelsinger calls an “AE724″ world. Bill Gates’ vision was a computer on every desk and in every home, Intel’s is much more ambitious. It’s a world where everyone has access to the Internet, with computing embedded into the environment and the infrastructure - everywhere you can imagine. It’s certainly a big picture - and one that will mean a shift in the way we develop applications and in how we design networks and data centres.
We blogged about GPU-based computing last week, and Gelsinger was asked about Intel’s response to NVIDIA’s CUDA and AMD’s CTM. Describing CUDA as “an interesting footnote in the history of computing”, Gelsinger talked about GPU computing as a cool idea that required a new programming model. He felt that this would be hard to deal with compared to general purpose computing techniques, and suggested that Intel’s massively multicore Larabee would be the right answer in the long term.
It’s true the microprocessor and the software stack make a huge difference. I probably wouldn’t have dialed in to the conference call if Skype didn’t connect to US 1-800 numbers for free from anywhere in the world. Whether the future’s all Intel is another question. IA is an important architecture but there’s still space for low power alternatives like ARM, or for specialised co-processors from the likes of Toshiba, Azul, AMD and NVIDIA. General purpose silicon is just one way of working - and if you’re prepared to target a specific niche there’s still plenty of scope to make a very healthy profit with specialised silicon.
–Simon
Rated: 100% (1 votes)
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More battery life, fewer explosions
By Simon Bisson & Mary Branscombe in Editorial
Posted in Futures, Silicon, Toys & gadgets, Hardware, Laptop, Mobile on
No battery ever lasts long enough. The extended battery on the HP 2710 tablets
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CUDA - let the GPU take the strain
By Simon Bisson & Mary Branscombe in Editorial
Posted in Processors, Silicon, Applications, Business, Server on
The barracuda is the wolf of the sea, a slim silver dart that hunts in deadly packs. It’s perhaps not surprising that NVIDIA has taken part of its name for its GPU-based supercomputing tools.
On a recent trip to the US, Mary and I met up with some of the folk behind CUDA at NVIDIA’s Sunnyvale headquarters. It was a fascinating conversation - if only because I used to write scientific computing software, and something like CUDA would have sped up my work massively. When a problem takes days to solve, something using something like CUDA to accelerate processing makes a lot of sense.
Prior to CUDA, NVIDIA had tried to use GPUs for compute, but had run into architectural problems. Things changed with their series 8 GPU, which was very different to anything they’d built before, being designed for compute as well as graphics. That’s lead to some tradeoffs - there’s silicon on the GPUs that’s unused when it’s used as an accelerator (and vice versa). However NVIDIA makes so many chips, there’s not really any financial issue, it all comes out of the economies of scale.
CUDA is more than just a set of chips - it’s a language framework for working with GPUs, that can andle both sequential and parallel code together. Developers don
Rated: 90% (2 votes)
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