Is it possible to fit the computing power of a large supercomputer cluster in the tight space of a PC case? Looking at new computational methods for tomography—a technique used by medical scanners to create 3D images—University of Antwerp researchers have built a budget supercomputer using 13 Nvidia GTX 295 graphics cards. tomography code is developed using the NVIDIA CUDA framework, a C-like programming language that allows for efficient programming of the NVIDIA GPUs.
- Intel Core i7 920
- 6 NVIDIA GTX295 dual-GPU+ GTX275 single-GPU (total 13 GPUs)
- 6×2GB Corsair DDR3 1333
- Lian-Li PC-P80 Armorsuit Case
- ASUS P6T7 WS Motherboard
- Samsung Spinpoint F3 1TB Harddrive
- Thermaltake Toughpower 1500W + 3x Thermaltake PowerExpress 450W
- Price : Less than $ 8073
CalcUA Super Computer
- 256-node supercomputer @AMD Opteron 250 2.4GHz (total 512 cores)
- Price : $ 4,6 Million
Here’s Benchmark result, see the differences :O
While it is an impressive example how GPUs can be applied in non-traditional ways, there are a few notes to be added. Of course, GPUs cannot replace traditional supercomputers, which still can be applied to applications with a broader range. Also, supercomputers usually carry huge memories, often in the Terabyte range, which cannot be matched by today’s GPU clusters. When we talk to scientists working with supercomputers and GPUs, they typically believe that future supercomputers will not completely transition to GPU clusters, but may develop into systems that consist of a traditional supercomputer structure as well as GPU capability.