The NVIDIA Collective Communications Library (NCCL) implements multi-GPU and multinode (MGMN) communication primitives optimized for NVIDIA GPUs and networking. NCCL is a central piece of software for multi-GPU deep learning training. It handles any kind of inter-GPU communication, be it over PCI, NVLink, or networking. It uses advanced topology detection, optimized communication graphs��
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Parallel thread execution (PTX) is a virtual machine instruction set architecture that has been part of CUDA from its beginning. You can think of PTX as the assembly language of the NVIDIA CUDA GPU computing platform. In this post, we��ll explain what that means, what PTX is for, and what you need to know about it to make the most of CUDA for your applications. We��ll start by walking through��
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In modern software development, time is an incredibly valuable resource, especially during the compilation process. For developers working with CUDA C++ on large-scale GPU-accelerated applications, optimizing compile times can significantly enhance productivity and streamline the entire development cycle. When using the compiler for offline compilation, efficient compilation times enable��
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Quantitative developers need to run back-testing simulations to see how financial algorithms perform from a profit and loss (P&L) standpoint. Statistical techniques are important to visualize the possible outcomes of the algorithms in terms of the possible P&L paths. GPUs can greatly reduce the amount of time needed to do this. In the broader picture, mathematical modeling of financial��
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NVIDIA cuDSS is a first-generation sparse direct solver library designed to accelerate engineering and scientific computing. cuDSS is increasingly adopted in data centers and other environments and supports single-GPU, multi-GPU and multi-node (MGMN) configurations. cuDSS has become a key tool for accelerating computer-aided engineering (CAE) workflows and scientific computations across��
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NVIDIA and Red Hat have partnered to bring continued improvements to the precompiled NVIDIA Driver introduced in 2020. Last month, NVIDIA announced that the open GPU driver modules will become the default recommended way to enable NVIDIA graphics hardware. Today, NVIDIA announced that Red Hat is now compiling and signing the NVIDIA open GPU kernel modules to further streamline the usage for��
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A new study and AI model from researchers at Stanford University is streamlining cancer diagnostics, treatment planning, and prognosis prediction. Named MUSK (Multimodal transformer with Unified maSKed modeling), the research aims to advance precision oncology, tailoring treatment plans to each patient based on their unique medical data. ��Multimodal foundation models are a new frontier in��
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Provides support for the NVIDIA Blackwell SM100 architecture. CUTLASS is a collection of CUDA C++ templates and abstractions for implementing high-performance GEMM computations.
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The NVIDIA Collective Communications Library (NCCL) implements multi-GPU and multinode communication primitives optimized for NVIDIA GPUs and networking. NCCL is a central piece of software for multi-GPU deep learning training. It handles any kind of inter-GPU communication, be it over PCI, NVLink, or networking. It uses advanced topology detection, optimized communication graphs��
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Historically, the GPU device code is compiled alongside the application with offline tools such as . In this case, the GPU device code is managed internally to the CUDA runtime. You can then launch kernels using and the CUDA runtime ensures that the invoked kernel is launched. However, in some cases, GPU device code needs to be dynamically compiled and loaded. This post shows a way to��
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The latest release of the CUDA Toolkit, version 12.8, continues to push accelerated computing performance in data sciences, AI, scientific computing, and computer graphics and simulation, using the latest NVIDIA CPUs and GPUs. This post highlights some of the new features and enhancements included with this release: CUDA Toolkit 12.8 is the first version of the Toolkit to support��
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NVIDIA recently announced a new generation of PC GPUs��the GeForce RTX 50 Series��alongside new AI-powered SDKs and tools for developers. Powered by the NVIDIA Blackwell architecture, fifth-generation Tensor Cores and fourth-generation RT Cores, the GeForce RTX 50 Series delivers breakthroughs in AI-driven rendering, including neural shaders, digital human technologies, geometry and lighting.
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Rare diseases are difficult to diagnose due to limitations in traditional genomic sequencing. Wolfgang Pernice, assistant professor at Columbia University, is using AI-powered cellular profiling to bridge these gaps and advance personalized medicine. At NVIDIA GTC 2024, Pernice shared insights from his lab��s work with diseases like Charcot-Marie-Tooth (CMT) and mitochondrial disorders.
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Whether you��re just starting your GPU programming journey or you��re a CUDA ninja looking to share advanced techniques, join us in San Jose on 1/30/25.
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RAPIDS 24.12 introduces cuDF packages to PyPI, speeds up aggregations and reading files from AWS S3, enables larger-than-GPU memory queries in the Polars GPU engine, and faster graph neural network (GNN) training on real-world graphs. Starting with the 24.12 release of RAPIDS, CUDA 12 builds of , , , and all of their dependencies are now available on PyPI. As a result��
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XGBoost is a machine learning algorithm widely used for tabular data modeling. To expand the XGBoost model from single-site learning to multisite collaborative training, NVIDIA has developed Federated XGBoost, an XGBoost plugin for federation learning. It covers vertical collaboration settings to jointly train XGBoost models across decentralized data sources, as well as horizontal histogram-based��
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With the latest release of Warp 1.5.0, developers now have access to new tile-based programming primitives in Python. Leveraging cuBLASDx and cuFFTDx, these new tools provide developers with efficient matrix multiplication and Fourier transforms in Python kernels for accelerated simulation and scientific computing. In this blog post, we��ll introduce these new features and show how they can be used��
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As of 3/18/25, NVIDIA Triton Inference Server is now NVIDIA Dynamo. The demand for AI-enabled services continues to grow rapidly, placing increasing pressure on IT and infrastructure teams. These teams are tasked with provisioning the necessary hardware and software to meet that demand while simultaneously balancing cost efficiency with optimal user experience. This challenge was faced by the��
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As we move towards a more dense computing infrastructure, with more compute, more GPUs, accelerated networking, and so forth��multi-gpu training and analysis grows in popularity. We need tools and also best practices as developers and practitioners move from CPU to GPU clusters. RAPIDS is a suite of open-source GPU-accelerated data science and AI libraries. These libraries can easily scale-out for��
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In the wake of ever-growing power demands, power systems optimization (PSO) of power grids is crucial for ensuring efficient resource management, sustainability, and energy security. The Eastern Interconnection, a major North American power grid, consists of approximately 70K nodes (Figure 1). Aside from sheer size, optimizing such a grid is complicated by uncertainties such as catastrophic��
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Accelerated quantum supercomputing combines the benefits of AI supercomputing with quantum processing units (QPUs) to develop solutions to some of the world��s hardest problems. Realizing such a device involves the seamless integration of one or more QPUs into a traditional CPU and GPU supercomputing architecture. An essential component of any accelerated quantum supercomputer is a programming��
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nvmath-python (Beta) is an open-source Python library, providing Python programmers with access to high-performance mathematical operations from NVIDIA CUDA-X math libraries. nvmath-python provides both low-level bindings to the underlying libraries and higher-level Pythonic abstractions. It is interoperable with existing Python packages, such as PyTorch and CuPy. In this post, I show how to��
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Python is the most common programming language for data science, machine learning, and numerical computing. It continues to grow in popularity among scientists and researchers. In the Python ecosystem, NumPy is the foundational Python library for performing array-based numerical computations. NumPy��s standard implementation operates on a single CPU core, with only a limited set of operations��
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The ability to compare the sequences of multiple related proteins is a foundational task for many life science researchers. This is often done in the form of a multiple sequence alignment (MSA), and the evolutionary information retrieved from these alignments can yield insights into protein structure, function, and evolutionary history. Now, with MMseqs2-GPU, an updated GPU-accelerated��
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A new machine-learning algorithm that listens to digital heartbeat data could help veterinarians diagnose murmurs and early-stage heart disease in dogs. Developed by a team of researchers from the University of Cambridge, the study analyzes electronic stethoscope recordings to grade murmur intensity and diagnose the stage of myxomatous mitral valve disease (MMVD)��the most common form of heart��
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By enabling CUDA kernels to be written in Python similar to how they can be implemented within C++, Numba bridges the gap between the Python ecosystem and the performance of CUDA. However, CUDA C++ developers have access to many libraries that presently have no exposure in Python. These include the CUDA Core Compute Libraries (CCCL), cuRAND, and header-based implementations of numeric types��
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Researchers at UCLA have developed a new AI model that can expertly analyze 3D medical images of diseases in a fraction of the time it would otherwise take a human clinical specialist. The deep-learning framework, named SLIViT (SLice Integration by Vision Transformer), analyzes images from different imagery modalities, including retinal scans, ultrasound videos, CTs, MRIs, and others��
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Reality capture creates highly accurate, detailed, and immersive digital representations of environments. Innovations in site scanning and accelerated data processing, and emerging technologies like neural radiance fields (NeRFs) and Gaussian splatting are significantly enhancing the capabilities of reality capture. These technologies are revolutionizing interactions with and analyses of the��
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The NVIDIA RTX AI for Windows PCs platform offers a thriving ecosystem of thousands of open-source models for application developers to leverage and integrate into Windows applications. Notably, llama.cpp is one popular tool, with over 65K GitHub stars at the time of writing. Originally released in 2023, this open-source repository is a lightweight, efficient framework for large language model��
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AI techniques like large language models (LLMs) are rapidly transforming many scientific disciplines. Quantum computing is no exception. A collaboration between NVIDIA, the University of Toronto, and Saint Jude Children��s Research Hospital is bringing generative pre-trained transformers (GPTs) to the design of new quantum algorithms, including the Generative Quantum Eigensolver (GQE) technique.
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NVIDIA NeMo has consistently developed automatic speech recognition (ASR) models that set the benchmark in the industry, particularly those topping the Hugging Face Open ASR Leaderboard. These NVIDIA NeMo ASR models that transcribe speech into text offer a range of architectures designed to optimize both speed and accuracy: Previously, these models faced speed performance��
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Includes C++ runtime support in Windows Support, Enhanced Dynamic Shape support in Converters, PyTorch 2.4, CUDA 12.4, TensorRT 10.1, Python 3.12.
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The vast majority of the world��s data remains untapped, and enterprises are looking to generate value from this data by creating the next wave of generative AI applications that will make a transformative business impact. Retrieval-augmented generation (RAG) pipelines are a key part of this, enabling users to have conversations with large corpuses of data and turning manuals, policy documents��
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Stephen Jones, a leading expert and distinguished NVIDIA CUDA architect, offers his guidance and insights with a deep dive into the complexities of mapping applications onto massively parallel machines. Going beyond the basics to explore the intricacies of GPU programming, he focuses on practical techniques such as parallel program design and specific details of GPU optimization for improving the��
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CUDA Graphs are a way to define and batch GPU operations as a graph rather than a sequence of stream launches. A CUDA Graph groups a set of CUDA kernels and other CUDA operations together and executes them with a specified dependency tree. It speeds up the workflow by combining the driver activities associated with CUDA kernel launches and CUDA API calls. It also enforces the dependencies with��
]]>In the realm of high-performance computing (HPC), NVIDIA has continually advanced HPC by offering its highly optimized NVIDIA High-Performance Conjugate Gradient (HPCG) benchmark program as part of the NVIDIA HPC benchmark program collection. We now provide the NVIDIA HPCG benchmark program in the /NVIDIA/nvidia-hpcg GitHub repo, using its high-performance math libraries, cuSPARSE��
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NVSHMEM is a parallel programming interface that provides efficient and scalable communication for NVIDIA GPU clusters. Part of NVIDIA Magnum IO and based on OpenSHMEM, NVSHMEM creates a global address space for data that spans the memory of multiple GPUs and can be accessed with fine-grained GPU-initiated operations, CPU-initiated operations, and operations on CUDA streams.
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Driven by shifts in consumer behavior and the pandemic, e-commerce continues its explosive growth and transformation. As a result, logistics and transportation firms find themselves at the forefront of a parcel delivery revolution. This new reality is especially evident in last-mile delivery, which is now the most expensive element of supply chain logistics. It represents more than 41%
]]>To fully harness the capabilities of NVIDIA GPUs, optimizing NVIDIA CUDA performance is essential, particularly for developers new to GPU programming. This talk is specifically designed for those stepping into the world of CUDA, providing a solid foundation in GPU architecture principles and optimization techniques. Athena Elafrou, a developer technology engineer at NVIDIA��
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The open-source llama.cpp code base was originally released in 2023 as a lightweight but efficient framework for performing inference on Meta Llama models. Built on the GGML library released the previous year, llama.cpp quickly became attractive to many users and developers (particularly for use on personal workstations) due to its focus on C/C++ without the need for complex dependencies.
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The release supports GB100 capabilities and new library enhancements to cuBLAS, cuFFT, cuSOLVER, cuSPARSE, as well as the release of Nsight Compute 2024.3.
]]>With the R515 driver, NVIDIA released a set of Linux GPU kernel modules in May 2022 as open source with dual GPL and MIT licensing. The initial release targeted datacenter compute GPUs, with GeForce and Workstation GPUs in an alpha state. At the time, we announced that more robust and fully-featured GeForce and Workstation Linux support would follow in subsequent releases and the NVIDIA Open��
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NVIDIA is excited to collaborate with Colfax, Together.ai, Meta, and Princeton University on their recent achievement to exploit the Hopper GPU architecture and Tensor Cores and accelerate key Fused Attention kernels using CUTLASS 3. FlashAttention-3 incorporates key techniques to achieve 1.5�C2.0x faster performance than FlashAttention-2 with FP16, up to 740 TFLOPS. With FP8��
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nvmath-python is an open-source Python library that provides high performance access to the core mathematical operations in the NVIDIA Math Libraries. Available now in beta.
]]>cuDSS (Preview) is an accelerated direct sparse solver. It now supports multi-GPU multi-node platforms, and introduces a hybrid memory mode.
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NVIDIA DOCA GPUNetIO is a library within the NVIDIA DOCA SDK, specifically designed for real-time inline GPU packet processing. It combines technologies like GPUDirect RDMA and GPUDirect Async to enable the creation of GPU-centric applications where a CUDA kernel can directly communicate with the network interface card (NIC) for sending and receiving packets, bypassing the CPU and excluding it��
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The latest release of NVIDIA cuBLAS library, version 12.5, continues to deliver functionality and performance to deep learning (DL) and high-performance computing (HPC) workloads. This post provides an overview of the following updates on cuBLAS matrix multiplications (matmuls) since version 12.0, and a walkthrough: Grouped GEMM APIs can be viewed as a generalization of the batched��
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Nsight Compute 2024.2 adds Python syntax highlighting and call stacks, a redesigned report header, and source page statistics to make CUDA optimization easier.
]]>CUDA Toolkit 12.5 supports new NVIDIA L20 and H20 GPUs and simultaneous compute and graphics to DirectX, and updates Nsight Compute and CUDA-X Libraries.
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Post updated on February 3, 2025 with details about CUDA 12.8. CUDA Graphs can provide a significant performance increase, as the driver is able to optimize execution using the complete description of tasks and dependencies. Graphs provide incredible benefits for static workflows where the overhead of graph creation can be amortized over many successive launches. However��
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Missed GTC or want to replay your favorite training labs? Find it on demand with the NVIDIA GTC Training Labs playlist.
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NVIDIA GPUs are becoming increasingly powerful with each new generation. This increase generally comes in two forms. Each streaming multi-processor (SM), the workhorse of the GPU, can execute instructions faster and faster, and the memory system can deliver data to the SMs at an ever-increasing pace. At the same time, the number of SMs also typically increases with each generation��
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AI is augmenting high-performance computing (HPC) with novel approaches to data processing, simulation, and modeling. Because of the computational requirements of these new AI workloads, HPC is scaling up at a rapid pace. To enable applications to scale to multi-GPU and multi-node platforms, HPC tools and libraries must support that growth. NVIDIA provides a comprehensive ecosystem of��
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NVIDIA cuSPARSELt harnesses Sparse Tensor Cores to accelerate general matrix multiplications. Version 0.6. adds support for the NVIDIA Hopper architecture.
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The latest release of CUDA Toolkit, version 12.4, continues to push accelerated computing performance using the latest NVIDIA GPUs. This post explains the new features and enhancements included in this release: CUDA and the CUDA Toolkit software provide the foundation for all NVIDIA GPU-accelerated computing applications in data science and analytics, machine learning��
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Predicting 3D protein structures from amino acid sequences has been an important long-standing question in bioinformatics. In recent years, deep learning�Cbased computational methods have been emerging and have shown promising results. Among these lines of work, AlphaFold2 is the first method that has achieved results comparable to slower physics-based computational methods.
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cuBLASDx allows you to perform BLAS calculations inside your CUDA kernel, improving the performance of your application. Available to download in Preview now.
]]>Many CUDA applications running on multi-GPU platforms usually use a single GPU for their compute needs. In such scenarios, a performance penalty is paid by applications because CUDA has to enumerate/initialize all the GPUs on the system. If a CUDA application does not require other GPUs to be visible and accessible, you can launch such applications by isolating the unwanted GPUs from the CUDA��
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cuBLASMp is a high-performance, multi-process, GPU-accelerated library for distributed basic dense linear algebra. It is available to download in Preview now.
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NVIDIA CUDA-Q is a platform for building quantum-classical computing applications. It is an open-source programming model for heterogeneous computing such as quantum processor units (QPUs), GPUs, and CPUs. CUDA-Q accelerates workflows such as quantum simulation, quantum machine learning, quantum chemistry, and more. It optimizes these workflows as part of its compiler toolchain and uses the��
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There are some useful intrinsic functions in the NVIDIA GPU instruction set that are not included in standard graphics APIs. Updated from the original 2016 post to add information about new intrinsics and cross-vendor APIs in DirectX and Vulkan. For example, a shader can use warp shuffle instructions to exchange data between threads in a warp without going through shared memory��
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NVIDIA Isaac Transport for ROS (NITROS) is the implementation of two hardware-acceleration features introduced with ROS 2 Humble-type adaptation and type negotiation. Type adaptation enables ROS nodes to work in a data format optimized for specific hardware accelerators. The adapted type is used by processing graphs to eliminate memory copies between the CPU and the memory accelerator.
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High-performance computing (HPC) powers applications in simulation and modeling, healthcare and life sciences, industry and engineering, and more. In the modern data center, HPC synergizes with AI, harnessing data in transformative new ways. The performance and throughput demands of next-generation HPC applications call for an accelerated computing platform that can handle diverse workloads��
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Real-time autonomous robot navigation powered by a fast motion-generation algorithm can enable applications in several industries such as food and services, warehouse automation, and machine tending. Motion generation for manipulators is extremely challenging, as it requires satisfying complex constraints and minimizing several cost terms. In addition, manipulators can have many��
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See how KDNuggets achieved 500x speedup using CuPy and NVIDIA CUDA on 3D arrays.
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The latest release of CUDA Toolkit continues to push the envelope of accelerated computing performance using the latest NVIDIA GPUs. New features of this release, version 12.3, include: CUDA and the CUDA Toolkit continue to provide the foundation for all accelerated computing applications in data science, machine learning and deep learning, generative AI with LLMs for both training and��
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Differentiable Slang easily integrates with existing codebases��from Python, PyTorch, and CUDA to HLSL��to aid multiple computer graphics tasks and enable novel data-driven and neural research. In this post, we introduce several code examples using differentiable Slang to demonstrate the potential use across different rendering applications and the ease of integration. This is part of a series��
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NVIDIA just released a SIGGRAPH Asia 2023 research paper, SLANG.D: Fast, Modular and Differentiable Shader Programming. The paper shows how a single language can serve as a unified platform for real-time, inverse, and differentiable rendering. The work is a collaboration between MIT, UCSD, UW, and NVIDIA researchers. This is part of a series on Differentiable Slang. For more information about��
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This NVIDIA HPC SDK 23.9 update expands platform support and provides minor updates.
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Generative AI is taking the world by storm, from large language models (LLMs) to generative pretrained transformer (GPT) models to diffusion models. NVIDIA is uniquely positioned to accelerate generative AI workloads, but also those for data processing, analytics, high-performance computing (HPC), quantitative financial applications, and more. NVIDIA offers a one-stop solution for diverse workload��
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GPU acceleration is enabling faster and more intelligent applications than ever before, and the CUDA Toolkit is key to harnessing acceleration on NVIDIA GPUs. But debugging, profiling, and optimizing CUDA can be a challenge, especially if you are unable to inspect hardware-level throughput and performance. To help you harness CUDA acceleration, NVIDIA offers Nsight Developer Tools.
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NVIDIA has already made available a GPU driver binary symbols server for Windows. Now, NVIDIA is making available a repository of CUDA Toolkit symbols for Linux. NVIDIA is introducing CUDA Toolkit symbols for Linux for an application development enhancement. During application development, you can now download obfuscated symbols for NVIDIA libraries that are being debugged or profiled in��
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Episode 5 of the NVIDIA CUDA Tutorials Video series is out. Jackson Marusarz, product manager for Compute Developer Tools at NVIDIA, introduces a suite of tools to help you build, debug, and optimize CUDA applications, making development easy and more efficient. This includes: IDEs and debuggers: integration with popular IDEs like NVIDIA Nsight Visual Studio Edition��
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On July 26, connect with NVIDIA CUDA product team experts on the latest CUDA Toolkit 12.
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Heterogeneous computing architectures��those that incorporate a variety of processor types working in tandem��have proven extremely valuable in the continued scalability of computational workloads in AI, machine learning (ML), quantum physics, and general data science. Critical to this development has been the ability to abstract away the heterogeneous architecture and promote a framework that��
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We were stuck. Really stuck. With a hard delivery deadline looming, our team needed to figure out how to process a complex extract-transform-load (ETL) job on trillions of point-of-sale transaction records in a few hours. The results of this job would feed a series of downstream machine learning (ML) models that would make critical retail assortment allocation decisions for a global retailer.
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Organizations are increasingly adopting hybrid and multi-cloud strategies to access the latest compute resources, consistently support worldwide customers, and optimize cost. However, a major challenge that engineering teams face is operationalizing AI applications across different platforms as the stack changes. This requires MLOps teams to familiarize themselves with different environments and��
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The latest release of CUDA Toolkit 12.2 introduces a range of essential new features, modifications to the programming model, and enhanced support for hardware capabilities accelerating CUDA applications. Now out through general availability from NVIDIA, CUDA Toolkit 12.2 includes many new capabilities, both major and minor. The following post offers an overview of many of the key��
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Watch on-demand as experts deep dive into CUDA 12.2, including support for confidential computing.
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At the heart of the rapidly expanding set of AI-powered applications are powerful AI models. Before these models can be deployed, they must be trained through a process that requires an immense amount of AI computing power. AI training is also an ongoing process, with models constantly retrained with new data to ensure high-quality results. Faster model training means that AI-powered applications��
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AI is transforming industries, automating processes, and opening new opportunities for innovation in the rapidly evolving technological landscape. As more businesses recognize the value of incorporating AI into their operations, they face the challenge of implementing these technologies efficiently, effectively, and reliably. Enter NVIDIA AI Enterprise, a comprehensive software suite��
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QHack is an educational conference and the world��s largest quantum machine learning (QML) hackathon. This year at QHack 2023, 2,850 individuals from 105 different countries competed for 8 days to build the most innovative solutions for quantum computing applications using NVIDIA quantum technology. The event was organized by Xanadu, with NVIDIA sponsoring the QHack 2023 NVIDIA Challenge.
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On June 6, learn how researchers use OpenACC for GPU acceleration of multiphase and compressible flow solvers that obtain speedups at scale.
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This post covers CPU best practices when working with NVIDIA GPUs. To get a high and consistent frame rate in your applications, see all Advanced API Performance tips. To get the best performance from your NVIDIA GPU, pair it with efficient work delegation on the CPU. Frame-rate caps, stutter, and other subpar application performance events can often be traced back to a bottleneck on the CPU.
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]]>This post covers best practices for using sampler feedback on NVIDIA GPUs. To get a high and consistent frame rate in your applications, see all Advanced API Performance tips. Sampler feedback is a DirectX 12 Ultimate feature for capturing and recording texture sampling information and locations. Sampler feedback was designed to provide better support for streaming and texture-space shading.
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Accurate weather modeling is essential for companies to properly forecast renewable energy production and plan for natural disasters. Ineffective and non-forecasted weather cost an estimated $714 billion in 2022 alone. To avoid this, companies need faster, cheaper, and more accurate weather models. In a recent GTC session, Microsoft, and TempoQuest detailed their work with NVIDIA to address��
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Debugging is difficult. Debugging across multiple languages is especially challenging, and debugging across devices often requires a team with varying skill sets and expertise to reveal the underlying problem. Yet projects often require using multiple languages, to ensure high performance where necessary, a user-friendly experience, and compatibility where possible. Unfortunately��
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GPUs continue to get faster with each new generation, and it is often the case that each activity on the GPU (such as a kernel or memory copy) completes very quickly. In the past, each activity had to be separately scheduled (launched) by the CPU, and associated overheads could accumulate to become a performance bottleneck. The CUDA Graphs facility addresses this problem by enabling multiple GPU��
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The Dataiku platform for everyday AI simplifies deep learning. Use cases are far-reaching, from image classification to object detection and natural language processing (NLP). Dataiku helps you with labeling, model training, explainability, model deployment, and centralized management of code and code environments. This post dives into high-level Dataiku and NVIDIA integrations for image��
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