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NVIDIA Tesla K20 GPU Accelerator (Kepler GK110) Up Close

NVIDIA’s Tesla K20 GPU is currently the de facto standard for high-performance heterogeneous computing. Based upon the Kepler GK110 architecture, these are the GPUs you want if you’ll be taking advantage of the latest advancements available in CUDA 5.0 and CUDA 5.5. This generation was designed specifically for the exciting new features in CUDA such as dynamic parallelism.

With 5GB or 6GB of GDDR5 memory, they provide up to 3.95 TFLOPS single-precision and 1.33 TFLOPS double-precision floating point performance. Two variants of the GPU are available: K20 (available for workstations and servers) and K20X (available only for servers). Here are the full specifications:

Technical Details

For the technically-minded audience, here is the full information dump from nvidia-smi on Tesla K20 and K20X GPUs:

CUDA Device Query for Tesla K20

NVIDIA’s deviceQuery utility (from the CUDA SDK examples) demonstrates how applications can query the capabilities of a CUDA-capable GPU. This utility also gives valuable details about the Tesla GPU products.

Usage Differences for Tesla K20 vs K20X

It’s worth noting that there are different versions of the Tesla GPU products depending on the type of installation. For Tesla K20 we can provide anything from quiet workstations to full compute clusters. For the higher-performing Tesla K20X the options are limited. In particular, it’s not possible to provide a workstation which is quiet.

If a tower/workstation form-factor is required, we do have one available. Unfortunately, it’s rather noisy. There is no quiet configuration on the market for Tesla K20X. Please contact one of Microway’s HPC experts if you would like to discuss the alternatives.

For those curious as to why there are two separate product versions, it’s simply a question of optimized cooling. Take a look at our GPU servers and you’ll see that airflow is carefully channeled through the GPU slots. This provides the best possible cooling for dense installations, but simply doesn’t cool properly in workstation configurations. For workstations, actively-cooled versions are available for many GPUs.

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