Parallel processing: accelerating the rate of scientific discovery

Learn how GPU-enabled parallel processing is helping research scientists answer some of the world’s hardest questions faster than ever before.

Parallel processing Accelerating the rate of scientific discovery

Today, a large volume of scientific research is all about extracting insights and meaning from massive data sets. Whether it’s through genome sequencing or observation of the Earth’s climate, leading scientists are generating several terabytes of data every day.

Gone are the days of struggling to observe experiments and capture data from observations. Today, there’s a virtually limitless number of ways to capture research data, shifting the challenge from “how do we collect it”, to “how can we analyze and unlock value from it?”

Traditionally, research analysis was performed by multi-core CPUs, but as data volumes have grown, they’ve quickly become far too much for CPUs to handle.

The responsibility now falls to GPUs, or more specifically, parallel processing. The emergence of general-purpose GPU computing – thanks to innovations like NVIDIA® CUDA® – has enabled researchers and data scientists alike to unlock the power of parallel processing and complete multiple complex calculations at once.

For scientific research teams, this is a major breakthrough.

Faster answers to the world’s biggest questions

With the power of parallel processing, research scientists can now support artificial intelligence (AI), machine learning (ML), and deep learning (DL) workloads, and gain the performance required to run high performance computing (HPC) research applications.

The throughput of NVIDIA GPUs powered by CUDA is extremely high, helping cut processing, prediction, and analysis time for machine learning data sets from days to just hours. In some cases, GPUs powered by CUDA can deliver up to a 50X performance advantage over CPUs – completely redefining what’s possible for research teams.

Using a single GPUs is also more cost-effective and power efficient compared to performing the same tasks on multiple CPUs. And thanks to the emergence of cloud-based deployment, smaller labs and research start-ups can now provision technology like AI and critical data workloads with previously unthinkable levels of computational power – without investing in costly on-premise computing clusters. .

Freedom to scale as research scope grows

For research teams, every breakthrough brings a greater future challenge.. Being able to access flexible NVIDIA GPU resources in Google Cloud means they can easily scale their performance, as the data volumes they handle and HPC app demands increase.

With cloud-based technology, scalability is virtually limitless – giving teams the freedom to use hundreds of NVIDIA GPUs and tackle data visualization and analysis tasks that previously would have been impossible.

That’s exactly what Princeton University’s Seung Lab has done. The lab partnered with NVIDIA and Google Cloud to gain the power of thousands of NVIDIA T4 GPUs and achieve a landmark breakthrough for neuroscience: reconstructing the connectome  of a cubic millimeter of neocortex.

“It’s thrilling to wield thousands of NVIDIA T4 GPUs,” said Sebastian Seung, Computer Science and Neuroscience Professor, Princeton University. “These computational resources are allowing us to trace 5 km of neuronal wiring and identify a billion synapses inside the tiny volume.”

Discover the power of CUDA-X

With the NVIDIA CUDA-X™ platform, harnessing the power of parallel processing for scientific research has never been simpler.

The CUDA-X HPC platform helps deliver dramatically higher performance than CPUs across computational physics, chemistry, molecular dynamics, and seismic exploration. And CUDA-X AI™ enables simpler AI application development, accelerating data processing, feature engineering, ML, and more.

Both are now easily accessible in Google Cloud, helping scientific researchers tap into unprecedented GPU power and continue solving challenging problems at scale.

Unlock the Power of Two

By partnering with a GPU leader like NVIDIA, and a cloud leader like Google, you get The Power of Two together, we can help you make the most of the opportunities enabled by ML, work on large data sets without worrying about processing limitations, and accelerate the rate of discovery.

To learn more about NVIDIA and Google Cloud, and experience the Power of Two for yourself, visit today.

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