Verify the benefits of GPU-acceleration for your workloads
GPU-Accelerated Applications Available for Testing
- TensorFlow with Keras
- PyTorch, MXNet, and Caffe2 deep learning frameworks
- RAPIDS for data science and analytics on GPUs
- NVIDIA DIGITS Deep Learning training system
- nvBowtie (GPU-accelerated Bowtie2)
- MATLAB runtime (users may execute pre-compiled MATLAB applications)
- NAMD (with multicore, MPI and CUDA builds)
- Quantum Espresso
- HOOMD-blue (single and double precision)
- NVIDIA CUDA SDK
- NVIDIA cuDNN, NCCL, and more
- FFTW3 (single, double, and quad precision builds)
- Python with H5py, NumPy, pandas, PyCUDA, pydot, scikit-image, scikit-learn, SciPy, SymPy, and more
MPI & Compilers
MPI & Compiler Software
- GNU GCC Compiler Collection (multiple versions, as needed) Provides C, C++ and Fortran compilers.
- Intel Parallel Studio XE Cluster Edition (multiple versions, as needed) Provides C, C++ and Fortran Compilers; Integrated Performance Primitives (IPP), Math Kernel Library (MKL), Clik Plus, Threading Building Blocks (TBB), MPI Library, MPI Benchmarks, Trace Analyzer & Collector, VTune Amplifier XE, Inspector XE, Advisor XE
- NVIDIA NVHPC SDK for Fortran/C/C++ (multiple versions, as needed) Provides NVIDIA C, C++ and Fortran compilers. Also with GPU-acceleration via CUDA Fortran and OpenACC.
- AMD Optimizing C/C++ Compiler (AOCC) Provides C, C++, and Fortran compilers with optimizations for the latest AMD EPYC CPUs.
Systems Available for Testing
Microway offers a Linux-based benchmark cluster for customers to test GPU-enabled applications. The cluster includes:
- NVIDIA DGX A100 deep learning system (with NVSwitch/NVLink-connected A100 GPUs)
- Microway NumberSmasher and Navion GPU Nodes
- CentOS Linux (with support for Singularity images)
- Pre-configured GPU-enabled software packages
- Alternate test configurations available upon request.
Custom NumberSmasher (Xeon) and Navion (EPYC) GPU Nodes include
- Up to eight NVIDIA datacenter PCI-E or NVLink GPUs per node
- Two 16-core Intel Xeon Gold 6226R “Cascade Lake” CPUs in NumberSmasher nodes
with others available upon request
- Two 16-, 24-, 32-, 48- or 64-core AMD EPYC CPUs in Navion nodes
- Up to 768GB DDR4 memory in each node
- EDR and HDR InfiniBand HCAs and switching
Unlike traditional CPUs, which focus on general-purpose software applications, NVIDIA GPUs are designed specifically to provide the highest compute performance possible. For many applications, a GPU-accelerated system will be 5X to 25X times faster than a CPU-only system.
The NVIDIA A100 GPUs are the latest and fastest accelerators. Based on the “Volta” architecture, they feature:
Improved compute performance per GPU
Up to 9.7 TFLOPS double- and 19.5 TFLOPS single-precision floating-point performance
Faster GPU memory
High-bandwidth HBM2 memory provides a 3X improvement over older GPUs
NVLink provides 5X~10X faster transfers than PCI-Express
Try today on advanced, fully integrated hardware
Whether you use community-built code or have in-house GPU-accelerated applications, we are offering remote benchmarking time on our latest hardware. This includes NVIDIA A100 and Tesla V100S GPUs with over 5X the performance of previous GPUs.
See how fast your code can run
To log in and test your code, register above. After registration, you will receive an email with instructions. For any questions, please email firstname.lastname@example.org.
NVIDIA GPU Accelerated Applications
NVIDIA GPU compute processors accelerate many common scientific codes – AMBER, NAMD and LAMMPS are just a few of the applications enjoying significant speed-ups. You can run your own code or one of the preloaded applications.