• Nvidia Tesla GPU

    Access supercomputer capabilities with low cost, low server count, and low power consumption.


Workstation

with Nvidia GPU


Rackmount Server

with Nvidia GPU


Supercomputer & Cluster

with Nvidia GPU

Surin’s solutions based on Nvidia GPUs

Surin has a history of development of advanced supercomputers, workstations, servers and cluster based on NVIDIA Tesla and NVIDIA Quadro® GPUs.

Surin has been selected as the supplier of Nvidia GPUs by many research and industrial centers.

Many of the most popular applications already feature GPU support. Your own applications may take advantage of GPU acceleration through several different avenues:

  • “Drop-in” GPU-accelerated libraries – provide high-speed implementations of the functions your application currently executes on CPUs.
  • OpenACC / OpenMP Compiler directives – allow you to quickly add GPU acceleration to the most performance critical sections of your application while maintaining portability.
  • CUDA integrated with C, C++ or Fortran – provides maximum performance and flexibility for your applications. Third-party language extensions are available for a host of languages, including Java, Mathematica, MATLAB, Perl and Python

Surin’s GPU-based solutions can help you use GPUs to accelerate your performance.

new Nvidia RTX GPU

  • Tesla® T4
  • Double Precision
  • Single Precision
  • Architecture
  • CUDA cores
  • Tesnor cores
  • Memory
  • Memory bandwidth
  • Interface
  • Can be installed on GPU servers
  • Tesla® T4
  • –
  • 8.1 TFlops
  • Nvidia Turing™
  • 2560
  • 320
  • 16GB GDDR6
  • 320 GB/s
  • PCI-E x16 Gen3
  • Can be installed on GPU servers

Turing or RTX is the latest Nvidia graphics processor architecture (GPU) built after the Volta architecture. The RTX architecture was first introduced in August 2018 with professional Quadro RTX GPUs and a week later with GeForce RTX graphics cards. The RTX architecture for the first time provides real-time ray tracing on graphic cards. The realization of this has long been one of the goals of the computer graphics industry. Key features of the RTX architecture include the AI ​​processor (Tensor cores) and special ray tracing processors (RT cores). Turing uses Microsoft DXR, OptiX and Vulkan to operate beam tracing.

Turing or RTX series features

  • CUDA cores
  • Ray Tracing core (RT Cores) for the first time
  • Tensor cores (AI cores)
  • Memory controller supporting GDDR6
  • Take advantage of NVLink2.0 between GPUs

Nvidia GPU models

  • Tesla® V100
  • Double precision
  • Single precision
  • Architecture
  • CUDA core
  • Tensor core
  • Memory
    بر روی کارت
  • پهنای Memory bandwidth
  • Interface
    Computer Link
  • Can be installed on GPU servers
  • Tesla® V100
  • upto 7.8 TFlops
  • upto 15.7 TFlops
  • Nvidia Volta™
  • 5120
  • 620
  • 16GB – 32GB HBM2
    (single chip)
  • 900 GB/s
  • NVIDIA NVLink or
    PCI-E x16 Gen3
  • Can be installed on GPU servers
  • Tesla® P100
  • upto 5.3 TFlops
  • upto 10.6 TFlops
  • Nvidia Pascal™
  • 3584
  • –
  • 12GB – 16GB HBM2
    (8GB/chip)
  • 732 GB/s
  • NVIDIA NVLink or
    PCI-E x16 Gen3
  • Can be installed on GPU servers
  • Tesla® K80
  • upto 2.91 TFlops
  • upto 5.6 TFlops
  • Nvidia Kepler™
  • 4992 (2496/chip)
  • –
  • 24GB GDDR5
    (12GB/chip)
  • 480 GB/s

  • PCI-E x16 Gen3
  • Can be installed on GPU servers
  • Tesla® P40
  • –
  • upto 12 TFlops
  • Nvidia Pascal™
  • 3840
  • –
  • 24GB GDDR5
    (ECC)
  • 346 GB/s

  • PCI-E x16 Gen3
  • Can be installed on GPU servers

Unique features available in the latest NVIDIA GPUs include:

  • High-speed, on-die GPU memory
  • NVLink interconnect speeds up data transfers up to 10X over PCI-Express
  • Unified Memory allows applications to directly access the memory of all GPUs and all of system memory
  • Direct CPU-to-GPU NVLink connectivity on OpenPOWER systems supports NVLink transfers between the CPUs and GPUs
  • ECC memory error protection – meets a critical requirement for computing accuracy and reliability in data centers and supercomputing centers.
  • System monitoring features – integrate the GPU subsystem with the host system’s monitoring and management capabilities such as IPMI. IT staff can manage the GPU processors in the computing system with widely-used cluster/grid management tools.

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