NVIDIA has this week announced updates to its accelerated computing libraries providing developers with 65 software development kits for accelerating quantum computing, last-mile delivery, supercomputing for the PyData ecosystem and more enhance catalog of 150+ offerings. Updates of rolled out to NVIDIA ReOpt, cuQuantum, cuNumeric, cuGraph, Modulus, Morpheus, NeMo Megatron, Riva, RAPIDS, DOCA and more.
New SDKs released by NVIDIA include :
- NVIDIA ReOpt, for real-time logistics, introduces advanced, massively parallel algorithms that optimize vehicle routes, warehouse selection and fleet mix. Its dynamic rerouting capabilities can reduce travel time, save fuel costs and minimize idle periods, potentially saving billions for the logistics and supply chain industries.
- cuNumeric, for array computing, implements the NumPy application programming interface for automatic scaling to multi-GPU and multi-node systems with zero code changes — a value for the 20 million-strong community of data scientists, researchers and scientists using Python. Available now on GitHub and Conda, it scales to thousands of GPUs, creating accelerated computing for the PyData and NumPy ecosystem.
- cuQuantum, for quantum computing, enables large quantum circuits to be simulated dramatically faster, allowing quantum researchers to study a broader space of algorithms and applications. Developers can simulate areas such as near-term variational quantum algorithms for molecules and error correction algorithms to identify fault tolerance, as well as accelerate popular quantum simulators from Atos, Google and IBM.
- CUDA-X accelerated DGL container, for graph neural networks, offers developers and data scientists working on GNNs with large graphs a quick way to set up a working environment. The container makes it easy to work in an integrated, GPU-accelerated GNN environment combining DGL and Pytorch. With GPU-accelerated GNNs, even the largest graphs in the world, approaching a trillion edges in a single graph, can be mined for insights. For instance, Pinterest uses graph neural networks with billions of nodes and edges to understand their ecosystem of over 300 billion Pins, based on GPUs and optimized libraries for training and inference of models.
Updated NVIDIA SDKs
- RAPIDS 21.10, for data science, offers new functions to work with time series data and several speedups to existing algorithms. The RAPIDS Accelerator for Apache Spark 3.0 allows enterprises to accelerate their analytics operations on NVIDIA GPUs with no code changes. With RAPIDS downloads having grown by 400 percent this year, this is one of NVIDIA’s most popular SDKs.
- Deepstream 6.0, for intelligent video analytics, introduces a new graph composer interface that makes computer vision accessible to users with minimal coding capability and a visual drag-and-drop interface for simple, intuitive AI product development flow.
- Triton 2.15, TensorRT 8.2 and cuDNN 8.4, for deep neural networks, provides new optimizations for large language models and inference acceleration for gradient-boosted decision trees and random forests.
- DOCA 1.2, for data center networking, offers a zero-trust security framework that extends threat protection through hardware and software authentication, line-rate data encryption, distributed firewall and smart telemetry.
- Merlin 0.8, for recommender systems, has new capabilities for predicting a user’s next action with little or no user data and support for models larger than GPU memory.
For more information on all the new updates to the NVIDIA development tools jump over to the official NVIDIA blog by following the link below.
Source : NVIDIA
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