Transportation Industry

Moving Forward with Transportation

Fully autonomous and driver assistance technologies powered by deep learning have become a key focus for every car manufacturer, as well as transportation services and technology companies. The car needs to know exactly where it is, recognize the objects around it, and continuously calculate the optimal path for a safe driving experience. This situational and contextual awareness of the car and its surroundings demands a powerful GPU computing systems that can merge data from cameras and other sensors, plus navigation sources, while also figuring out the safest path – all in real-time.

Data scientists in transportation have been using GPUs, such as the NVIDIA® Tesla® M40 GPU Acceletator, for machine learning to make groundbreaking improvements across a variety of applications including image classification and processing, video analytics, and natural language processing. In particular, Deep Learning — the use of sophisticated, multi-level "deep" neural networks to create systems that can perform feature detection from massive amounts of unlabeled training data — is an area that has been seeing significant investment and research from autonomous driving and transportation applications.

The NVIDIA Tesla M40 GPU Accelerator, along with the Cirrascale GX8 Series of deep learning rackmount servers, can dramatically reduces the time to train deep neural networks — as much as 13X faster than a CPU. Additionally, the Tesla M40 GPU Accelerator provides 24GB of ultra-fast GDDR5 memory, which enables a single Cirrascale GX8 rackmount server to house up to an incredible 192GB of GPU memory.

Featured Resources for Transportation

Transportation Deep Learning Servers

Over the past several years, Cirrascale has been working with a variety of application developers as well as data scientists in both industry and academia. Together, our goal has been to continue making groundbreaking improvements in developing some of the most advanced hardware capable of increasing the overall speed and flexibility of deep learning training and inference by using multi-GPU compute solutions.

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Scaling GPU Compute Performance White Paper

GPU chip manufacturers have been providing more computational power within each GPU card. Recently, this includes packing multiple GPUs within each card, such as the NVIDIA® Tesla® K80 Dual-GPU Accelerator cards, which contain two GPUs per card. Additionally, with new iterations, they continue to increase overall memory size and bandwidth accessible to those GPUs to accelerate multiple HPC applications. Read this paper to discover the best way to scale GPU compute performance.

Download Cirrascale GPU Compute Performance White Paper
NVIDIA® GPU Application Catalog

NVIDIA identifies over two hundred applications for a wide range of industries already optimized for GPUs, including High Performance Computing. Discover if your application is supported and put those applications to work with a Cirrascale GB series blade server.

Download NVIDIA® GPU Application Catalog

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