The One Laptop per Child project is having its annual give get one promotion. Basically you pay 400 $ , you get one XO laptop free and another XO laptop is donated in your name in a devleoping country. For the technically minded ,here is a great review of the XO laptop at http://www.bunniestudios.com/blog/?p=218
On a slightly different scale is the NVidia GPU ( as opposed to CPU powered computers). They are available here at http://www.nvidia.com/object/wtb_notebooks.html
These and the forthcoming series of NVidia powered GPus are going to give extremely high speeds within a price range of 10,000 USD. How high is the speed ?
Well here is a case study from the NYT "
Techniscan Medical Systems of Salt Lake City has turned to Nvidia’s graphics processors to speed up a three-dimensional breast scanning device that could be used for cancer detection if the machine received regulatory approval. Techniscan must turn tens of gigabytes of raw data generated by transmitting pulses of energy through a breast submerged in water into medical image files that consume just 100 megabytes. This whole process used to take a couple of hours using Intel’s processors and now takes just 15 minutes with Nvidia’s hardware."
And here is finally your desktop supercomputer, the Tesla from Nvidia.
"Get your own supercomputer. Experience cluster level computing performance—up to 250 times faster than standard PCs and workstations—right at your desk. The NVIDIA® Tesla™ Personal Supercomputer is based on the revolutionary NVIDIA® CUDA™ parallel computing architecture and powered by up to 960 parallel processing cores."
Now data mining and analytics people love processing power. With this much processing power it can be quite a lot of fun !
So if you deal with data more than 1 Gb at a time or have more than 10 Pcs, or 2 servers, try the Tesla.
- Massively-parallel many-core architecture
- 240 scalar processor cores per GPU
- Integer, single-precision and double-precision floating point operations
- Hardware Thread Execution Manager enables thousands of concurrent threads per GPU
- Parallel shared memory enables processor cores to collaborate on shared information at local cache performance
- Ultra-fast GPU memory access with 102 GB/s peak bandwidth per GPU
- IEEE 754 single-precision and double-precision floating point
- Each Tesla C1060 GPU delivers 933 GFlops Single Precision and 78 GFlops Double Precision performance
Software Development Tools
- C language compiler, debugger, profiler, and emulation mode for debugging
- Standard numerical libraries for FFT (Fast Fourier Transform), BLAS (Basic Linear Algebra Subroutines), and CuDPP (CUDA Data Parallel Primitives)
- 3 or 4 Tesla C1060 Computing Processors with 4GB of dedicated memory per GPU
- 2.33 GHz+ Quad-core AMD Phenom or Opteron, — OR — Quad-core Intel Core 2 or Xeon
- Minimum system memory: 12 GB for 3 Tesla C1060s and 16 GB for 4 Tesla C1060s (at least 4GB per Tesla C1060)
- 12GB+ system memory (at least 4GB per Tesla C1060)
- 1200-1350 Watt Power supply
- Acoustics < 45dbA
- Microsoft® Windows® XP 64-bit and 32-bit (64-bit recommended)
- Linux® 64-bit and 32-bit (64-bit recommended)
- Red Hat Enterprise Linux 4 and 5
- SUSE 10.1, 10.2 and 10.3