Supercomputing PCs

Overview. Over the last few years, the scene on the high-performance computer market has witnessed even more dramatic changes than the general computer market which have also been extremely turbulent. As today's graphics card chips have gigantic capacities and significantly exceed normal CPUs in terms of transistors and complexity, it makes sense to outsource intensive computing applications onto the GPU. The transtec supercomputer systems are based on innovative, parallel architecture from NVIDIA CUDA™ and have an enormous computing power. Cluster performance in one PC: Up to 4 TP computing power Up to 960 parallel cores Up to 250 times faster than standard PCs and workstations The supercomputer in PC format: "Each scientist has his own supercomputer" Connection to standard sockets Convenient implementation: Extremely compatible, easy to learn-in CUDA C programming environment Programming in C for Windows or Linux Is this level of parallelisation and throughput imaginable on dual or quad-core CPUs? No, it's not! The graphics card as parallel computer >> 1000W CUDA Supercomputing PC (C1060) >> 1000R CUDA Supercomputing System (S1070) >> Areas of application >>

Graphics card as parallel computer. In the past, graphics card application was restricted to 2 or 3-dimensional images. A CPU, however, was used to calculate the displayed images. The increasing complexity of the calculations prompted calculations to be made on the graphics card itself. In the last few years, we have witnessed an enormous interest in utilising the immense capacity of parallel GPUs for other operations beyond the traditional 3D graphics. GPUs have developed far beyond simple pipeline implementation which is limited to fixed functions. They have become flexible, programmable and multiple parallel cores.

Their computing performance was increasingly employed to solve general numerical problems. Initially, extremely specialised hardware and creative implementations were required for these operations. However, modern interpretations already support abstract parallel computing models such as stream processing. This complemented the graphics pipeline and was used as the foundation for implementation on a GPU. With the Compute Unified Device Architecture (CUDA) from NVIDIA, new hardware and software possibilities are now available which make stream processing appear too restrictive.

Supercomputing PCs

The simplest option is to integrate multiple graphics cards into one computer. Today's mainboards currently have up to 4 PCI Express 16x slots. Two of these slots are usually equipped with full data transfer rate required for installing the graphics cards. A number of these computers can be connected to the network at a relatively low cost to build high-performance computing clusters.

1000W CUDA Supercomputing PC:

up to two TESLA 1060 cards up to 2 TP computing power up to 480 cores up to 8 GB RAM and 8 GB graphics memory up to 4 TB hard drive memory

Supercomputing system

PCI Express switches also present another scalability option for the cascadable connection of multiple graphics cards on one single PCI Express bus. The Tesla S1070 System houses in one height unit 4 Tesla graphics cards each with 1 Teraflop single-precision computing performance and a total of 16 GB graphics memory. The cards can be accessed from outside via a shared PCI Express port using a PCI Express switch.

transtec 1000R CUDA Supercomputer:
comprising of:

Front-end system

• max. four Xeon processors
• max. two interface cards for the TESLA 1070 system
• max. 128 GB RAM
• 4 hot-swap SATA hard drives
• Infiniband 20Gbps controller integrated
• 1U rackmount

Tesla 1070 system

• up to four TP computing power in one highly dense 1U system
• up to 960 cores
• 16 GB high-speed memory
• delivers up to 408 GB/s storage bandwidth for extremely fast data transfer
• high-speed data transfer with PCI-Express 2.0
• rackmount assembly with one single screw

Areas of application.

GPU computing products enable scientists to execute larger algorithms. The products are intended for

• Rendering
• Medical research and
• Complex data processing

as GPUs are considerably more effective for parallel data processing than standard processors.

Geoscientists, molecular biology or medical institutes also rely on enormous computing power for simulations.

Oil companies can also conduct complex geographical and seismic analyses with GPU computing technology, for example. Weather simulations can also be accelerated by a factor of 50.