NVIDIA Launches Array of New CUDA Libraries to Expand Accelerated Computing and Deliver Order-of-Magnitude Speedup to Science and Industrial Applications

News summary: New libraries in accelerated computing deliver order-of-magnitude speedups and reduce energy consumption and costs in data processing, generative AI, recommender systems, AI data curation, data processing, 6G research, AI-physics and more. They include:

LLM applications: NeMo Curator, to create custom datasets, adds image curation and Nemotron-4 340B for high-quality synthetic data generation

Data processing: cuVS for vector search to build indexes in minutes instead of days and a new Polars GPU Engine in open beta

Physical AI: For physics simulation, Warp accelerates computations with a new TIle API. For wireless network simulation, Aerial adds more map formats for ray tracing and simulation. And for link-level wireless simulation, Sionna adds a new toolchain for real-time inference

Companies around the world are increasingly turning to NVIDIA accelerated computing to speed up applications they first ran on CPUs only. This has enabled them to achieve extreme speedups and benefit from incredible energy savings.

In Houston, CPFD makes computational fluid dynamics simulation software for industrial applications, like its Barracuda Virtual Reactor software that helps design next-generation recycling facilities. Plastic recycling facilities run CPFD software in cloud instances powered by NVIDIA accelerated computing. With a CUDA GPU-accelerated virtual machine, they can efficiently scale and run simulations 400x faster and 140x more energy efficiently than using a CPU-based workstation.

Bottles being loaded into a plastics recycling facility. AI-generated image. A popular video conferencing application captions several hundred thousand virtual meetings an hour. When using CPUs to create live captions, the app could query a transformer-powered speech recognition AI model three times a second. After migrating to GPUs in the cloud, the application's throughput increased to 200 queries per second - a 66x speedup and 25x energy-efficiency improvement.

In homes across the globe, an e-commerce website connects hundreds of millions of shoppers a day to the products they need using an advanced recommendation system powered by a deep learning model, running on its NVIDIA accelerated cloud computing system. After switching from CPUs to GPUs in the cloud, it achieved significantly lower latency with a 33x speedup and nearly 12x energy-efficiency improvement.

With the exponential growth of data, accelerated computing in the cloud is set to enable even more innovative use cases.

NVIDIA Accelerated Computing on CUDA GPUs Is Sustainable Computing NVIDIA estimates that if all AI, HPC and data analytics workloads that are still running on CPU servers were CUDA GPU-accelerated, data centers would save 40 terawatt-hours of energy annually. That's the equivalent energy consumption of 5 million U.S. homes per year.

Accelerated computing uses the parallel processing capabilities of CUDA GPUs to complete jobs orders of magnitude faster than CPUs, improving productivity while dramatically reducing cost and energy consumption.

Although adding GPUs to a CPU-only server increases peak power, GPU acceleration finishes tasks quickly and then enters a low-power state. The total energy consumed with GPU-accelerated computing is significantly lower than with general-purpose CPUs, while yielding superior performance.

GPUs achieve 20x greater energy efficiency compared to traditional computing on CPU-only servers because they deliver greater performance per watt, completing more tasks in less time. In the past decade, NVIDIA AI computing has achieved approximately 100,000x more energy efficiency when processing large language models. To put that into perspective, if the efficiency of cars improved as much as NVIDIA has advanced the efficiency of AI on its accelerated computing platform, they'd get 500,000 miles per gallon. That's enough to drive to the moon, and back, on less than a gallon of gasoline.

In addition to these dramatic boosts in efficiency on AI workloads, GPU computing can achieve incredible speedups over CPUs. Customers of the NVIDIA accelerated computing platform running workloads on cloud service providers saw speedups of 10-180x across a gamut of real-world tasks, from data processing to computer vision, as the chart below shows.

Speedups of 10-180x achieved in real-world implementations by cloud customers across workloads with the NVIDIA accelerated computing platform. As workloads continue to demand exponentially more computing power, CPUs have struggled to provide the necessary performance, creating a growing performance gap and driving compute inflation. The chart below illustrates a multiyear trend of how data growth has far outpaced the growth in compute performance per watt of CPUs.

The widening gap between data growth and the lagging compute performance per watt of CPUs. The energy savings of GPU acceleration frees up what would otherwise have been wasted cost and energy.

With its massive energy-efficiency savings, accelerated computing is sustainable computing.

The Right Tools for Every Job GPUs cannot accelerate software written for general-purpose CPUs. Specialized algorithm software libraries are needed to accelerate specific workloads. Just like a mechanic would have an entire toolbox from a screwdriver to a wrench for different tasks, NVIDIA provides a diverse set of libraries to perform low-level functions like parsing and executing calculations on data.

Each NVIDIA CUDA library is optimized to harness hardware features specific to NVIDIA GPUs. Combined, they encompass the power of the NVIDIA platform.

New updates continue to be added on the CUDA platform roadmap, expanding across diverse use cases:

LLM Applications NeMo Curator gives developers the flexibility to quickly create custom datasets in large language model (LLM) use cases. Recently

LINK:	https://blogs.nvidia.com/blog/cuda-accelerated-computing-energy-effici...
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