GPU Computing

GRAPHIC PROCESSING

GPU IN COMPUTING

With thousands of cores, the power of the graphic processing units is used to accelerate a wide range of mission-critical applications, from artificial intelligence (AI) to automatic learning. Processing with GPU acceleration transfers processing portions of the application of intensive use to the GPU, while the rest of the code is still being executed on the CPU. This way, the applications are executed more quickly and the users will see the difference. 

Advanced HPC can help you exploiting the high potential of GPU Computing. We work with an expert in GPU applications renowned worldwide and highly respected, as preferred partner in NVIDIA partner network, therefore we can design custom GPU solutions to satisfy all your needs, even the most unique and complex.

Collaborating with Do IT Systems offers many advantages:
 

  • Validation and burn-in services of the applications installed and professionally certified
  • Custom-designed GPU systems to satisfy every requirement of the HPC application
  • Access to the technology that is stimulating innovations in AI, automatic learning and deep learning
  • Ability to work with a team that also features a NVIDIA Partner Network (NPN) as preferred partner for accelerated calculations

QUICKER APPLICATIONS

GPU AND CALCULATION POWER

GPU computing consists in using a GPU (graphic processing unit) to speed up scientific and technical processing of general use.

The GPU exploits its natural characteristic of powerful vectorial processor required to process 3D images, accelerate the parts of generic code that allow massive parallel processing (GPGPU or General Purpose GPU).  According to the cases, the calculations may use the GPU as main calculation component or as accelerator of some code sections, leaving the rest of the calculation to the CPU (e.g. hybrid processing).

A CPU consists of a limited number of cores compared to a GPU, which offers a higher calculation power for “embarrassingly parallel” cases.  The parallel architecture confers to the GPU its high calculation performances. There are numerous applications with GPU acceleration that provide a simple way to access high-performance calculations (HPC).

The developers of applications exploit the performances of parallel GPU architecture using a parallel programming model conceived by NVIDIA called "CUDA". All NVIDA GPUs (GeForce®, Quadro® and Tesla®) support NVIDIA® CUDA® parallel programming model.

Tesla GPUs are designed as computational accelerators or complementary processors optimized for scientific and technical IT applications. The latest Tesla GPUs, series 20, are based on the latest implementation of CUDA platform called “Fermi architecture”. Fermi boasts key calculation features like over 500 gigaflop of IEEE standard double-precision floating-point hardware support, L1 and L2 caches, protection from ECC memory errors, data caches managed by the local user in form of shared storage dispersed in the entire GPU, unified accesses to storage and much more. 
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