World's Fastest GPUs

NVIDIA Tesla GPU Computing

NVIDIA Tesla-based systems deliver supercomputing performance at a lower power, lower cost, and using many fewer servers than standard CPU-only compute systems. Successfully deployed in demanding applications at research institutes, universities, and enterprises, NVIDIA Tesla powers the most powerful supercomputers worldwide.

NVIDIA Elite Solution ProviderMicroway designs customized GPU clusters, servers, and WhisperStations based on the NVIDIA Tesla, Quadro, GeForce and GRID GPUs. We have been selected as the vendor of choice for a number of NVIDIA CUDA centers, including Carnegie Mellon University, Harvard, Johns Hopkins and Massachusetts General Hospital.

While traditionally known for gaming and graphics, today’s NVIDIA GPUs deliver record compute speeds for seismic processing, biochemistry simulations, weather and climate modeling, image, video and signal processing, computational finance, computational physics, CAE, CFD, and data analytics.

Unique features available in the latest NVIDIA GPUs include:

  • NVIDIA GK110 DieDynamic parallelism – supports GPU threads launching new threads. This simplifies parallel programming by avoiding unnecessary communication between the GPU and the CPU.
  • HyperQ – enables up to 32 work queues per GPU. Multiple CPU cores and MPI processes are therefore able to address the GPU concurrently.
  • GPU Boost increases the clock speed of all CUDA cores, providing a 20% to 30% performance boost for many common applications.
  • 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.
  • Asynchronous transfer with dual DMA engines turbocharges system performance through simultaneous data transfers over the PCI-Express bus while the computing cores are crunching other data.

Tesla K80 – Density and Performance per Watt

Integrated in Microway NumberSmasher GPU Servers and GPU Clusters

SpecificationsNVIDIA Tesla K80

  • Two GK210 chips on a single PCB
  • 4992 CUDA cores, 2496 per chip
  • 5.6 TFLOPS single, 1.87 TFLOPS double precision
  • Dynamic GPU Boost for performance optimization
  • 8.74 TFLOPS single precision, 2.91 TFLOPS double precision with GPU Boost
  • 24GB GDDR5 memory (12GB per chip)
  • Memory bandwidth up to 480GB/s
  • PCI-E x16 Gen3 interface to system
  • Passive heatsink only, suitable for specially-designed GPU servers

Tesla M40 – the World’s Fastest Deep Learning Training Accelerator

Integrated in Microway NumberSmasher GPU Servers and GPU Clusters

SpecificationsPhoto of the front of the NVIDIA Tesla M40 GPU

  • 7 TFLOPS single-precision floating point performance
  • NVIDIA “Maxwell” GM200 graphics processing unit (GPU)
  • 3072 CUDA cores
  • Dynamic GPU Boost for performance optimization
  • Available with 12GB or 24GB GDDR5 memory
  • Memory bandwidth up to 288GB/s
  • PCI-E x16 Gen3 interface to system
  • Passive heatsink only, suitable for specially-designed GPU servers

Tesla K40 – Balanced GPU Memory and Compute Performance

Integrated in Microway NumberSmasher GPU Servers and GPU Clusters

SpecificationsPhotograph of NVIDIA Tesla Kepler GK110b K40 GPU without cover

  • 2880 CUDA GPU cores (GK110b)
  • 4.29 TFLOPS single; 1.43 TFLOPS double-precision
  • 12GB GDDR5 memory
  • Memory bandwidth up to 288 GB/s
  • PCI-E x16 Gen3 interface to system
  • Supports Dynamic Parallelism and HyperQ features
  • Active and Passive heatsinks available for installation in workstations and specially-designed GPU servers
  • GPU Boost increased clock speeds

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.

Tesla GPU computing solutions fit seamlessly into your existing workstation or HPC infrastructure enabling you to solve problems orders-of-magnitude faster.

Call a Microway Sales Engineer for Assistance : 508.746.7341 or
Click Here to Request More Information.

Comments are closed.