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| Get started with GPUs |
Cut down simulation time from days to hours with the Molecular Dynamics (MD) SimCluster. Designed with Tesla GPUs, the MD SimCluster is optimized to simulate large size models and gain higher accuracy while reducing simulation time. Preconfigured to accelerate AMBER or NAMD, all you need to do is load your models to start your simulation.
"With Tesla GPUs, AMBER users in universities can obtain application performance that outstrips what is even possible with extensive supercomputing access."
Ross Walker
Assistant Research Professor at San Diego Computer Center |
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| Why GPUs for AMBER and NAMD? |
| Simulate Molecules Faster |
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Fail Fast and Often |
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Simulate Larger Molecules |
| Faster simulations of molecules and its interactions result in faster product development and delivery to market. GPUs drive new scientific discoveries and help researchers build improved and less risky products. |
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For MD researchers it is a known phrase "Better to fail faster and to fail often". For example researchers can eliminate poor drug candidates faster with faster simulations and focus on high quality ones. GPUs allow a cost-effective and time efficient way to carry out research. |
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Being able to simulate several nanoseconds of large and complex molecules enables researchers and scientists to replace time-consuming costly "wet lab" experimentation with simulations. GPUs enable simulations once considered intractable providing a deeper understanding of molecular behavior. |
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| See for yourself - Test drive the MD SimCluster |
One of the questions researchers have is whether MD SimCluster will work for their models. With just three easy steps you can test-drive MD SimCluster and discover how much speed up your models will achieve.
Step 1: Visit the test-drive and schedule time
Step 2: Log in to the MD SimCluster.
Step 3: Load your models for AMBER or NAMD and run simulations.
Speed up results provided at the end of the simulation.
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| What is GPU Computing? |
GPU computing is the use of a GPU (graphics processing unit) to do general purpose scientific and engineering computing.The model for GPU computing is to use a CPU and GPU together. The sequential part of the application runs on the CPU and the computationally-intensive part runs on the GPU. GPU computing is enabled by the massively parallel architecture of NVIDIA's GPUs called the CUDA architecture. The CUDA architecture consists of 100s of processor cores that operate together to crunch through the data set in the application.
AMBER and NAMD along with several other MD applications support GPU computing. From the user's perspective, these applications just run faster using the GPU to boost performance.
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