Get Ready for The Future with More Powerful, More Efficient Computing
Graphics processing unit (GPU)-accelerated computing occurs when you use a GPU in combination with a CPU, letting the GPU handle as much of the parallel process application code as possible. The GPU takes the parallel computing approach orders of magnitude beyond the CPU, offering thousands of compute cores. This can accelerate some software by 100x over a CPU alone. Plus, the GPU achieves this acceleration while being more power- and cost-efficient than a CPU.
Because the Penguin Computing team (2019 NVIDIA Partner of the Year) is experienced with building both CPU and GPU-based systems as well as the storage subsystems required for this level of data analytics, the outcome of moving to a GPU-accelerated strategy is superior performance by all measures, faster compute time, and reduced hardware requirements.
19″ EIA Servers
21″ OCP Servers
Selected applications supported by NVIDIA-based Penguin Computing GPU servers:
- ANSYS Fluent
- Simulia Abaqus
Selected deep learning frameworks supported by NVIDIA-based Penguin Computing GPU servers:
- Microsoft Cognitive Toolkit
Benefits of GPU-Accelerated Computing
- Computing Power/Speed A single GPU can offer the performance of hundreds of CPUs for certain workloads. In fact, NVIDIA, a leading GPU developer, predicts that GPUs will help provide a 1000X acceleration in compute performance by 2025.
- Efficiency/Cost Adding a single GPU-accelerated server costs much less in upfront, capital expenses and, because less equipment is required, reduces footprint and operational costs. Using libraries also allows organizations to use GPU acceleration without in-depth knowledge of GPU programming, reducing the investment of time required to achieve results.
- Flexibility The inherently flexible nature of GPU programmability allows new algorithms to be developed and deployed quickly across a variety of industries. According to Intersect360 Research, 70% of the most popular HPC applications, including 10 of the top 10, have built-in support for GPUs.
- Long-Term Benefits Adding GPU-accelerated computing now prepares you for the artificial intelligence (AI) revolution, which also relies in GPU-accelerated computing. This inevitable increase on the reliance on GPUs means that early adopters will enjoy not only greater computing power over time but have a greater margin of difference over time than competitors who do not migrate to GPU-accelerated computing.