CUDA is Like Owning a Supercomputer

The word supercomputer gets thrown around quite a bit. The original Cray-1, for example, operated at about 150 MIPS and had about eight megabytes of memory. A modern Intel i7 CPU can hit almost 250,000 MIPS and is unlikely to have less than eight gigabytes of memory, and probably has quite a bit more. Sure, MIPS isn’t a great performance number, but clearly, a top-end PC is way more powerful than the old Cray. The problem is, it’s never enough.

Today’s computers have to processes huge numbers of pixels, video data, audio data, neural networks, and long key encryption. Because of this, video cards have become what in the old days would have been called vector processors. That is, they are optimized to do operations on multiple data items in parallel. There are a few standards for using the video card processing for computation and today I’m going to show you how simple it is to use CUDA — the NVIDIA proprietary library for this task. You can also use OpenCL which works with many different kinds of hardware, but I’ll show you that it is a bit more verbose.
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Neural Network Learns SDR Ham Radio

Identifying ham radio signals used to be easy. Beeps were Morse code, voice was AM unless it sounded like Donald Duck in which case it was sideband. But there are dozens of modes in common use now including TV, digital data, digital voice, FM, and more coming on line every day. [Randaller] used CUDA to build a neural network that could interface with an RTL-SDR dongle and can classify the signals it hears. Since it is a neural network, it isn’t so much programmed to do it as it is trained. The proof of concept has training to distinguish FM, SECAM, and tetra. However, you can train it to recognize other modulation schemes if you want to invest the time into it.

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Leveraging the GPU to accelerate the Linux kernel

Powerful graphics cards are pretty affordable these days. Even though we rarely do high-end gaming on our daily machine we still have a GeForce 9800 GT. That goes to waste on a machine used mainly to publish posts and write code for microcontrollers. But perhaps we can put the GPU to good use when it comes compile time. The KGPU package enlists your graphics card to help the kernel do some heavy lifting.

This won’t work for just any GPU. The technique uses CUDA, which is a parallel computing package for NVIDIA hardware. But don’t let lack of hardware keep you from checking it out. [Weibin Sun] is one of the researchers behind the technique. He posted a whitepaper (PDF) on the topic over at his website.

Add this to the growing list of non-graphic applications for today graphics hardware.

UPDATE: Looks like we won’t be trying this out after all. Your GPU must support CUDA 2.0 or higher. We found ours on this list and it’s only capable of CUDA 1.0.

[Thanks John]

GPU Processing and Password Cracking

Recently, research students at Georgia Tech released a report outlining the dangers that GPUs pose to the current state of password security. There are a number of ways to crack a password, all with their different pros and cons, but when it comes down to it, the limiting factor in all of these methods is processing complexity. The more operations that need to be run, the longer it takes, and the less useful each tool is for cracking passwords. In the past, most recommendations for password security revolved around making sure your password wasn’t something predictable, such as “password” or your birthday. With today’s (and tomorrows) GPUs, this may no longer be enough.

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